Sample records for biomedical text databases
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DeTEXT: A Database for Evaluating Text Extraction from Biomedical Literature Figures
Yin, Xu-Cheng; Yang, Chun; Pei, Wei-Yi; Man, Haixia; Zhang, Jun; Learned-Miller, Erik; Yu, Hong
2015-01-01
Hundreds of millions of figures are available in biomedical literature, representing important biomedical experimental evidence. Since text is a rich source of information in figures, automatically extracting such text may assist in the task of mining figure information. A high-quality ground truth standard can greatly facilitate the development of an automated system. This article describes DeTEXT: A database for evaluating text extraction from biomedical literature figures. It is the first publicly available, human-annotated, high quality, and large-scale figure-text dataset with 288 full-text articles, 500 biomedical figures, and 9308 text regions. This article describes how figures were selected from open-access full-text biomedical articles and how annotation guidelines and annotation tools were developed. We also discuss the inter-annotator agreement and the reliability of the annotations. We summarize the statistics of the DeTEXT data and make available evaluation protocols for DeTEXT. Finally we lay out challenges we observed in the automated detection and recognition of figure text and discuss research directions in this area. DeTEXT is publicly available for downloading at http://prir.ustb.edu.cn/DeTEXT/. PMID:25951377
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Biocuration workflows and text mining: overview of the BioCreative 2012 Workshop Track II.
Lu, Zhiyong; Hirschman, Lynette
2012-01-01
Manual curation of data from the biomedical literature is a rate-limiting factor for many expert curated databases. Despite the continuing advances in biomedical text mining and the pressing needs of biocurators for better tools, few existing text-mining tools have been successfully integrated into production literature curation systems such as those used by the expert curated databases. To close this gap and better understand all aspects of literature curation, we invited submissions of written descriptions of curation workflows from expert curated databases for the BioCreative 2012 Workshop Track II. We received seven qualified contributions, primarily from model organism databases. Based on these descriptions, we identified commonalities and differences across the workflows, the common ontologies and controlled vocabularies used and the current and desired uses of text mining for biocuration. Compared to a survey done in 2009, our 2012 results show that many more databases are now using text mining in parts of their curation workflows. In addition, the workshop participants identified text-mining aids for finding gene names and symbols (gene indexing), prioritization of documents for curation (document triage) and ontology concept assignment as those most desired by the biocurators. DATABASE URL: http://www.biocreative.org/tasks/bc-workshop-2012/workflow/.
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ERIC Educational Resources Information Center
Kollegger, James G.; And Others
1988-01-01
In the first of three articles, the producer of Energyline, Energynet, and Tele/Scope recalls the development of the databases and database business strategies. The second describes the development of biomedical online databases, and the third discusses future developments, including full text databases, database producers as online host, and…
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An overview of biomedical literature search on the World Wide Web in the third millennium.
Kumar, Prince; Goel, Roshni; Jain, Chandni; Kumar, Ashish; Parashar, Abhishek; Gond, Ajay Ratan
2012-06-01
Complete access to the existing pool of biomedical literature and the ability to "hit" upon the exact information of the relevant specialty are becoming essential elements of academic and clinical expertise. With the rapid expansion of the literature database, it is almost impossible to keep up to date with every innovation. Using the Internet, however, most people can freely access this literature at any time, from almost anywhere. This paper highlights the use of the Internet in obtaining valuable biomedical research information, which is mostly available from journals, databases, textbooks and e-journals in the form of web pages, text materials, images, and so on. The authors present an overview of web-based resources for biomedical researchers, providing information about Internet search engines (e.g., Google), web-based bibliographic databases (e.g., PubMed, IndMed) and how to use them, and other online biomedical resources that can assist clinicians in reaching well-informed clinical decisions.
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Subramani, Suresh; Kalpana, Raja; Monickaraj, Pankaj Moses; Natarajan, Jeyakumar
2015-04-01
The knowledge on protein-protein interactions (PPI) and their related pathways are equally important to understand the biological functions of the living cell. Such information on human proteins is highly desirable to understand the mechanism of several diseases such as cancer, diabetes, and Alzheimer's disease. Because much of that information is buried in biomedical literature, an automated text mining system for visualizing human PPI and pathways is highly desirable. In this paper, we present HPIminer, a text mining system for visualizing human protein interactions and pathways from biomedical literature. HPIminer extracts human PPI information and PPI pairs from biomedical literature, and visualize their associated interactions, networks and pathways using two curated databases HPRD and KEGG. To our knowledge, HPIminer is the first system to build interaction networks from literature as well as curated databases. Further, the new interactions mined only from literature and not reported earlier in databases are highlighted as new. A comparative study with other similar tools shows that the resultant network is more informative and provides additional information on interacting proteins and their associated networks. Copyright © 2015 Elsevier Inc. All rights reserved.
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Conceptual biology, hypothesis discovery, and text mining: Swanson's legacy.
Bekhuis, Tanja
2006-04-03
Innovative biomedical librarians and information specialists who want to expand their roles as expert searchers need to know about profound changes in biology and parallel trends in text mining. In recent years, conceptual biology has emerged as a complement to empirical biology. This is partly in response to the availability of massive digital resources such as the network of databases for molecular biologists at the National Center for Biotechnology Information. Developments in text mining and hypothesis discovery systems based on the early work of Swanson, a mathematician and information scientist, are coincident with the emergence of conceptual biology. Very little has been written to introduce biomedical digital librarians to these new trends. In this paper, background for data and text mining, as well as for knowledge discovery in databases (KDD) and in text (KDT) is presented, then a brief review of Swanson's ideas, followed by a discussion of recent approaches to hypothesis discovery and testing. 'Testing' in the context of text mining involves partially automated methods for finding evidence in the literature to support hypothetical relationships. Concluding remarks follow regarding (a) the limits of current strategies for evaluation of hypothesis discovery systems and (b) the role of literature-based discovery in concert with empirical research. Report of an informatics-driven literature review for biomarkers of systemic lupus erythematosus is mentioned. Swanson's vision of the hidden value in the literature of science and, by extension, in biomedical digital databases, is still remarkably generative for information scientists, biologists, and physicians.
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2013-01-01
Background Most of the institutional and research information in the biomedical domain is available in the form of English text. Even in countries where English is an official language, such as the United States, language can be a barrier for accessing biomedical information for non-native speakers. Recent progress in machine translation suggests that this technique could help make English texts accessible to speakers of other languages. However, the lack of adequate specialized corpora needed to train statistical models currently limits the quality of automatic translations in the biomedical domain. Results We show how a large-sized parallel corpus can automatically be obtained for the biomedical domain, using the MEDLINE database. The corpus generated in this work comprises article titles obtained from MEDLINE and abstract text automatically retrieved from journal websites, which substantially extends the corpora used in previous work. After assessing the quality of the corpus for two language pairs (English/French and English/Spanish) we use the Moses package to train a statistical machine translation model that outperforms previous models for automatic translation of biomedical text. Conclusions We have built translation data sets in the biomedical domain that can easily be extended to other languages available in MEDLINE. These sets can successfully be applied to train statistical machine translation models. While further progress should be made by incorporating out-of-domain corpora and domain-specific lexicons, we believe that this work improves the automatic translation of biomedical texts. PMID:23631733
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Jiang, Xiangying; Ringwald, Martin; Blake, Judith; Shatkay, Hagit
2017-01-01
The Gene Expression Database (GXD) is a comprehensive online database within the Mouse Genome Informatics resource, aiming to provide available information about endogenous gene expression during mouse development. The information stems primarily from many thousands of biomedical publications that database curators must go through and read. Given the very large number of biomedical papers published each year, automatic document classification plays an important role in biomedical research. Specifically, an effective and efficient document classifier is needed for supporting the GXD annotation workflow. We present here an effective yet relatively simple classification scheme, which uses readily available tools while employing feature selection, aiming to assist curators in identifying publications relevant to GXD. We examine the performance of our method over a large manually curated dataset, consisting of more than 25 000 PubMed abstracts, of which about half are curated as relevant to GXD while the other half as irrelevant to GXD. In addition to text from title-and-abstract, we also consider image captions, an important information source that we integrate into our method. We apply a captions-based classifier to a subset of about 3300 documents, for which the full text of the curated articles is available. The results demonstrate that our proposed approach is robust and effectively addresses the GXD document classification. Moreover, using information obtained from image captions clearly improves performance, compared to title and abstract alone, affirming the utility of image captions as a substantial evidence source for automatically determining the relevance of biomedical publications to a specific subject area. www.informatics.jax.org. © The Author(s) 2017. Published by Oxford University Press.
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Extracting biomedical events from pairs of text entities
2015-01-01
Background Huge amounts of electronic biomedical documents, such as molecular biology reports or genomic papers are generated daily. Nowadays, these documents are mainly available in the form of unstructured free texts, which require heavy processing for their registration into organized databases. This organization is instrumental for information retrieval, enabling to answer the advanced queries of researchers and practitioners in biology, medicine, and related fields. Hence, the massive data flow calls for efficient automatic methods of text-mining that extract high-level information, such as biomedical events, from biomedical text. The usual computational tools of Natural Language Processing cannot be readily applied to extract these biomedical events, due to the peculiarities of the domain. Indeed, biomedical documents contain highly domain-specific jargon and syntax. These documents also describe distinctive dependencies, making text-mining in molecular biology a specific discipline. Results We address biomedical event extraction as the classification of pairs of text entities into the classes corresponding to event types. The candidate pairs of text entities are recursively provided to a multiclass classifier relying on Support Vector Machines. This recursive process extracts events involving other events as arguments. Compared to joint models based on Markov Random Fields, our model simplifies inference and hence requires shorter training and prediction times along with lower memory capacity. Compared to usual pipeline approaches, our model passes over a complex intermediate problem, while making a more extensive usage of sophisticated joint features between text entities. Our method focuses on the core event extraction of the Genia task of BioNLP challenges yielding the best result reported so far on the 2013 edition. PMID:26201478
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Ravikumar, Komandur Elayavilli; Wagholikar, Kavishwar B; Li, Dingcheng; Kocher, Jean-Pierre; Liu, Hongfang
2015-06-06
Advances in the next generation sequencing technology has accelerated the pace of individualized medicine (IM), which aims to incorporate genetic/genomic information into medicine. One immediate need in interpreting sequencing data is the assembly of information about genetic variants and their corresponding associations with other entities (e.g., diseases or medications). Even with dedicated effort to capture such information in biological databases, much of this information remains 'locked' in the unstructured text of biomedical publications. There is a substantial lag between the publication and the subsequent abstraction of such information into databases. Multiple text mining systems have been developed, but most of them focus on the sentence level association extraction with performance evaluation based on gold standard text annotations specifically prepared for text mining systems. We developed and evaluated a text mining system, MutD, which extracts protein mutation-disease associations from MEDLINE abstracts by incorporating discourse level analysis, using a benchmark data set extracted from curated database records. MutD achieves an F-measure of 64.3% for reconstructing protein mutation disease associations in curated database records. Discourse level analysis component of MutD contributed to a gain of more than 10% in F-measure when compared against the sentence level association extraction. Our error analysis indicates that 23 of the 64 precision errors are true associations that were not captured by database curators and 68 of the 113 recall errors are caused by the absence of associated disease entities in the abstract. After adjusting for the defects in the curated database, the revised F-measure of MutD in association detection reaches 81.5%. Our quantitative analysis reveals that MutD can effectively extract protein mutation disease associations when benchmarking based on curated database records. The analysis also demonstrates that incorporating discourse level analysis significantly improved the performance of extracting the protein-mutation-disease association. Future work includes the extension of MutD for full text articles.
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Architecture for biomedical multimedia information delivery on the World Wide Web
NASA Astrophysics Data System (ADS)
Long, L. Rodney; Goh, Gin-Hua; Neve, Leif; Thoma, George R.
1997-10-01
Research engineers at the National Library of Medicine are building a prototype system for the delivery of multimedia biomedical information on the World Wide Web. This paper discuses the architecture and design considerations for the system, which will be used initially to make images and text from the third National Health and Nutrition Examination Survey (NHANES) publicly available. We categorized our analysis as follows: (1) fundamental software tools: we analyzed trade-offs among use of conventional HTML/CGI, X Window Broadway, and Java; (2) image delivery: we examined the use of unconventional TCP transmission methods; (3) database manager and database design: we discuss the capabilities and planned use of the Informix object-relational database manager and the planned schema for the HNANES database; (4) storage requirements for our Sun server; (5) user interface considerations; (6) the compatibility of the system with other standard research and analysis tools; (7) image display: we discuss considerations for consistent image display for end users. Finally, we discuss the scalability of the system in terms of incorporating larger or more databases of similar data, and the extendibility of the system for supporting content-based retrieval of biomedical images. The system prototype is called the Web-based Medical Information Retrieval System. An early version was built as a Java applet and tested on Unix, PC, and Macintosh platforms. This prototype used the MiniSQL database manager to do text queries on a small database of records of participants in the second NHANES survey. The full records and associated x-ray images were retrievable and displayable on a standard Web browser. A second version has now been built, also a Java applet, using the MySQL database manager.
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Wei, Wei; Ji, Zhanglong; He, Yupeng; Zhang, Kai; Ha, Yuanchi; Li, Qi; Ohno-Machado, Lucila
2018-01-01
Abstract The number and diversity of biomedical datasets grew rapidly in the last decade. A large number of datasets are stored in various repositories, with different formats. Existing dataset retrieval systems lack the capability of cross-repository search. As a result, users spend time searching datasets in known repositories, and they typically do not find new repositories. The biomedical and healthcare data discovery index ecosystem (bioCADDIE) team organized a challenge to solicit new indexing and searching strategies for retrieving biomedical datasets across repositories. We describe the work of one team that built a retrieval pipeline and examined its performance. The pipeline used online resources to supplement dataset metadata, automatically generated queries from users’ free-text questions, produced high-quality retrieval results and achieved the highest inferred Normalized Discounted Cumulative Gain among competitors. The results showed that it is a promising solution for cross-database, cross-domain and cross-repository biomedical dataset retrieval. Database URL: https://github.com/w2wei/dataset_retrieval_pipeline PMID:29688374
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A Diagram Editor for Efficient Biomedical Knowledge Capture and Integration
Yu, Bohua; Jakupovic, Elvis; Wilson, Justin; Dai, Manhong; Xuan, Weijian; Mirel, Barbara; Athey, Brian; Watson, Stanley; Meng, Fan
2008-01-01
Understanding the molecular mechanisms underlying complex disorders requires the integration of data and knowledge from different sources including free text literature and various biomedical databases. To facilitate this process, we created the Biomedical Concept Diagram Editor (BCDE) to help researchers distill knowledge from data and literature and aid the process of hypothesis development. A key feature of BCDE is the ability to capture information with a simple drag-and-drop. This is a vast improvement over manual methods of knowledge and data recording and greatly increases the efficiency of the biomedical researcher. BCDE also provides a unique concept matching function to enforce consistent terminology, which enables conceptual relationships deposited by different researchers in the BCDE database to be mined and integrated for intelligible and useful results. We hope BCDE will promote the sharing and integration of knowledge from different researchers for effective hypothesis development. PMID:21347131
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García-Remesal, M; Maojo, V; Billhardt, H; Crespo, J
2010-01-01
Bringing together structured and text-based sources is an exciting challenge for biomedical informaticians, since most relevant biomedical sources belong to one of these categories. In this paper we evaluate the feasibility of integrating relational and text-based biomedical sources using: i) an original logical schema acquisition method for textual databases developed by the authors, and ii) OntoFusion, a system originally designed by the authors for the integration of relational sources. We conducted an integration experiment involving a test set of seven differently structured sources covering the domain of genetic diseases. We used our logical schema acquisition method to generate schemas for all textual sources. The sources were integrated using the methods and tools provided by OntoFusion. The integration was validated using a test set of 500 queries. A panel of experts answered a questionnaire to evaluate i) the quality of the extracted schemas, ii) the query processing performance of the integrated set of sources, and iii) the relevance of the retrieved results. The results of the survey show that our method extracts coherent and representative logical schemas. Experts' feedback on the performance of the integrated system and the relevance of the retrieved results was also positive. Regarding the validation of the integration, the system successfully provided correct results for all queries in the test set. The results of the experiment suggest that text-based sources including a logical schema can be regarded as equivalent to structured databases. Using our method, previous research and existing tools designed for the integration of structured databases can be reused - possibly subject to minor modifications - to integrate differently structured sources.
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Accessing Biomedical Literature in the Current Information Landscape
Khare, Ritu; Leaman, Robert; Lu, Zhiyong
2015-01-01
i. Summary Biomedical and life sciences literature is unique because of its exponentially increasing volume and interdisciplinary nature. Biomedical literature access is essential for several types of users including biomedical researchers, clinicians, database curators, and bibliometricians. In the past few decades, several online search tools and literature archives, generic as well as biomedicine-specific, have been developed. We present this chapter in the light of three consecutive steps of literature access: searching for citations, retrieving full-text, and viewing the article. The first section presents the current state of practice of biomedical literature access, including an analysis of the search tools most frequently used by the users, including PubMed, Google Scholar, Web of Science, Scopus, and Embase, and a study on biomedical literature archives such as PubMed Central. The next section describes current research and the state-of-the-art systems motivated by the challenges a user faces during query formulation and interpretation of search results. The research solutions are classified into five key areas related to text and data mining, text similarity search, semantic search, query support, relevance ranking, and clustering results. Finally, the last section describes some predicted future trends for improving biomedical literature access, such as searching and reading articles on portable devices, and adoption of the open access policy. PMID:24788259
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NASA Technical Reports Server (NTRS)
Grams, R. R.
1982-01-01
A system designed to access a large range of available medical textbook information in an online interactive fashion is described. A high level query type database manager, INQUIRE, is used. Operating instructions, system flow diagrams, database descriptions, text generation, and error messages are discussed. User information is provided.
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2013-01-01
Background A large-scale, highly accurate, machine-understandable drug-disease treatment relationship knowledge base is important for computational approaches to drug repurposing. The large body of published biomedical research articles and clinical case reports available on MEDLINE is a rich source of FDA-approved drug-disease indication as well as drug-repurposing knowledge that is crucial for applying FDA-approved drugs for new diseases. However, much of this information is buried in free text and not captured in any existing databases. The goal of this study is to extract a large number of accurate drug-disease treatment pairs from published literature. Results In this study, we developed a simple but highly accurate pattern-learning approach to extract treatment-specific drug-disease pairs from 20 million biomedical abstracts available on MEDLINE. We extracted a total of 34,305 unique drug-disease treatment pairs, the majority of which are not included in existing structured databases. Our algorithm achieved a precision of 0.904 and a recall of 0.131 in extracting all pairs, and a precision of 0.904 and a recall of 0.842 in extracting frequent pairs. In addition, we have shown that the extracted pairs strongly correlate with both drug target genes and therapeutic classes, therefore may have high potential in drug discovery. Conclusions We demonstrated that our simple pattern-learning relationship extraction algorithm is able to accurately extract many drug-disease pairs from the free text of biomedical literature that are not captured in structured databases. The large-scale, accurate, machine-understandable drug-disease treatment knowledge base that is resultant of our study, in combination with pairs from structured databases, will have high potential in computational drug repurposing tasks. PMID:23742147
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Featured Article: Genotation: Actionable knowledge for the scientific reader
Willis, Ethan; Sakauye, Mark; Jose, Rony; Chen, Hao; Davis, Robert L
2016-01-01
We present an article viewer application that allows a scientific reader to easily discover and share knowledge by linking genomics-related concepts to knowledge of disparate biomedical databases. High-throughput data streams generated by technical advancements have contributed to scientific knowledge discovery at an unprecedented rate. Biomedical Informaticists have created a diverse set of databases to store and retrieve the discovered knowledge. The diversity and abundance of such resources present biomedical researchers a challenge with knowledge discovery. These challenges highlight a need for a better informatics solution. We use a text mining algorithm, Genomine, to identify gene symbols from the text of a journal article. The identified symbols are supplemented with information from the GenoDB knowledgebase. Self-updating GenoDB contains information from NCBI Gene, Clinvar, Medgen, dbSNP, KEGG, PharmGKB, Uniprot, and Hugo Gene databases. The journal viewer is a web application accessible via a web browser. The features described herein are accessible on www.genotation.org. The Genomine algorithm identifies gene symbols with an accuracy shown by .65 F-Score. GenoDB currently contains information regarding 59,905 gene symbols, 5633 drug–gene relationships, 5981 gene–disease relationships, and 713 pathways. This application provides scientific readers with actionable knowledge related to concepts of a manuscript. The reader will be able to save and share supplements to be visualized in a graphical manner. This provides convenient access to details of complex biological phenomena, enabling biomedical researchers to generate novel hypothesis to further our knowledge in human health. This manuscript presents a novel application that integrates genomic, proteomic, and pharmacogenomic information to supplement content of a biomedical manuscript and enable readers to automatically discover actionable knowledge. PMID:26900164
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Featured Article: Genotation: Actionable knowledge for the scientific reader.
Nagahawatte, Panduka; Willis, Ethan; Sakauye, Mark; Jose, Rony; Chen, Hao; Davis, Robert L
2016-06-01
We present an article viewer application that allows a scientific reader to easily discover and share knowledge by linking genomics-related concepts to knowledge of disparate biomedical databases. High-throughput data streams generated by technical advancements have contributed to scientific knowledge discovery at an unprecedented rate. Biomedical Informaticists have created a diverse set of databases to store and retrieve the discovered knowledge. The diversity and abundance of such resources present biomedical researchers a challenge with knowledge discovery. These challenges highlight a need for a better informatics solution. We use a text mining algorithm, Genomine, to identify gene symbols from the text of a journal article. The identified symbols are supplemented with information from the GenoDB knowledgebase. Self-updating GenoDB contains information from NCBI Gene, Clinvar, Medgen, dbSNP, KEGG, PharmGKB, Uniprot, and Hugo Gene databases. The journal viewer is a web application accessible via a web browser. The features described herein are accessible on www.genotation.org The Genomine algorithm identifies gene symbols with an accuracy shown by .65 F-Score. GenoDB currently contains information regarding 59,905 gene symbols, 5633 drug-gene relationships, 5981 gene-disease relationships, and 713 pathways. This application provides scientific readers with actionable knowledge related to concepts of a manuscript. The reader will be able to save and share supplements to be visualized in a graphical manner. This provides convenient access to details of complex biological phenomena, enabling biomedical researchers to generate novel hypothesis to further our knowledge in human health. This manuscript presents a novel application that integrates genomic, proteomic, and pharmacogenomic information to supplement content of a biomedical manuscript and enable readers to automatically discover actionable knowledge. © 2016 by the Society for Experimental Biology and Medicine.
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Liu, Yifeng; Liang, Yongjie; Wishart, David
2015-07-01
PolySearch2 (http://polysearch.ca) is an online text-mining system for identifying relationships between biomedical entities such as human diseases, genes, SNPs, proteins, drugs, metabolites, toxins, metabolic pathways, organs, tissues, subcellular organelles, positive health effects, negative health effects, drug actions, Gene Ontology terms, MeSH terms, ICD-10 medical codes, biological taxonomies and chemical taxonomies. PolySearch2 supports a generalized 'Given X, find all associated Ys' query, where X and Y can be selected from the aforementioned biomedical entities. An example query might be: 'Find all diseases associated with Bisphenol A'. To find its answers, PolySearch2 searches for associations against comprehensive collections of free-text collections, including local versions of MEDLINE abstracts, PubMed Central full-text articles, Wikipedia full-text articles and US Patent application abstracts. PolySearch2 also searches 14 widely used, text-rich biological databases such as UniProt, DrugBank and Human Metabolome Database to improve its accuracy and coverage. PolySearch2 maintains an extensive thesaurus of biological terms and exploits the latest search engine technology to rapidly retrieve relevant articles and databases records. PolySearch2 also generates, ranks and annotates associative candidates and present results with relevancy statistics and highlighted key sentences to facilitate user interpretation. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.
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Liu, Yifeng; Liang, Yongjie; Wishart, David
2015-01-01
PolySearch2 (http://polysearch.ca) is an online text-mining system for identifying relationships between biomedical entities such as human diseases, genes, SNPs, proteins, drugs, metabolites, toxins, metabolic pathways, organs, tissues, subcellular organelles, positive health effects, negative health effects, drug actions, Gene Ontology terms, MeSH terms, ICD-10 medical codes, biological taxonomies and chemical taxonomies. PolySearch2 supports a generalized ‘Given X, find all associated Ys’ query, where X and Y can be selected from the aforementioned biomedical entities. An example query might be: ‘Find all diseases associated with Bisphenol A’. To find its answers, PolySearch2 searches for associations against comprehensive collections of free-text collections, including local versions of MEDLINE abstracts, PubMed Central full-text articles, Wikipedia full-text articles and US Patent application abstracts. PolySearch2 also searches 14 widely used, text-rich biological databases such as UniProt, DrugBank and Human Metabolome Database to improve its accuracy and coverage. PolySearch2 maintains an extensive thesaurus of biological terms and exploits the latest search engine technology to rapidly retrieve relevant articles and databases records. PolySearch2 also generates, ranks and annotates associative candidates and present results with relevancy statistics and highlighted key sentences to facilitate user interpretation. PMID:25925572
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A recent advance in the automatic indexing of the biomedical literature.
Névéol, Aurélie; Shooshan, Sonya E; Humphrey, Susanne M; Mork, James G; Aronson, Alan R
2009-10-01
The volume of biomedical literature has experienced explosive growth in recent years. This is reflected in the corresponding increase in the size of MEDLINE, the largest bibliographic database of biomedical citations. Indexers at the US National Library of Medicine (NLM) need efficient tools to help them accommodate the ensuing workload. After reviewing issues in the automatic assignment of Medical Subject Headings (MeSH terms) to biomedical text, we focus more specifically on the new subheading attachment feature for NLM's Medical Text Indexer (MTI). Natural Language Processing, statistical, and machine learning methods of producing automatic MeSH main heading/subheading pair recommendations were assessed independently and combined. The best combination achieves 48% precision and 30% recall. After validation by NLM indexers, a suitable combination of the methods presented in this paper was integrated into MTI as a subheading attachment feature producing MeSH indexing recommendations compliant with current state-of-the-art indexing practice.
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LAMDA at TREC CDS track 2015: Clinical Decision Support Track
2015-11-20
outperforms all the other vector space models supported by Elasticsearch. MetaMap is the online tool that maps biomedical text to the Metathesaurus, and...cases. The medical knowledge consists of 700,000 biomedical documents supported by the PubMed Central [3] which is online digital database freely...Science Research Program through the National Research Foundation (NRF) of Korea funded by the Ministry of Science, ICT , and Future Planning (MSIP
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Lowe, H. J.
1993-01-01
This paper describes Image Engine, an object-oriented, microcomputer-based, multimedia database designed to facilitate the storage and retrieval of digitized biomedical still images, video, and text using inexpensive desktop computers. The current prototype runs on Apple Macintosh computers and allows network database access via peer to peer file sharing protocols. Image Engine supports both free text and controlled vocabulary indexing of multimedia objects. The latter is implemented using the TView thesaurus model developed by the author. The current prototype of Image Engine uses the National Library of Medicine's Medical Subject Headings (MeSH) vocabulary (with UMLS Meta-1 extensions) as its indexing thesaurus. PMID:8130596
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UKPMC: a full text article resource for the life sciences.
McEntyre, Johanna R; Ananiadou, Sophia; Andrews, Stephen; Black, William J; Boulderstone, Richard; Buttery, Paula; Chaplin, David; Chevuru, Sandeepreddy; Cobley, Norman; Coleman, Lee-Ann; Davey, Paul; Gupta, Bharti; Haji-Gholam, Lesley; Hawkins, Craig; Horne, Alan; Hubbard, Simon J; Kim, Jee-Hyub; Lewin, Ian; Lyte, Vic; MacIntyre, Ross; Mansoor, Sami; Mason, Linda; McNaught, John; Newbold, Elizabeth; Nobata, Chikashi; Ong, Ernest; Pillai, Sharmila; Rebholz-Schuhmann, Dietrich; Rosie, Heather; Rowbotham, Rob; Rupp, C J; Stoehr, Peter; Vaughan, Philip
2011-01-01
UK PubMed Central (UKPMC) is a full-text article database that extends the functionality of the original PubMed Central (PMC) repository. The UKPMC project was launched as the first 'mirror' site to PMC, which in analogy to the International Nucleotide Sequence Database Collaboration, aims to provide international preservation of the open and free-access biomedical literature. UKPMC (http://ukpmc.ac.uk) has undergone considerable development since its inception in 2007 and now includes both a UKPMC and PubMed search, as well as access to other records such as Agricola, Patents and recent biomedical theses. UKPMC also differs from PubMed/PMC in that the full text and abstract information can be searched in an integrated manner from one input box. Furthermore, UKPMC contains 'Cited By' information as an alternative way to navigate the literature and has incorporated text-mining approaches to semantically enrich content and integrate it with related database resources. Finally, UKPMC also offers added-value services (UKPMC+) that enable grantees to deposit manuscripts, link papers to grants, publish online portfolios and view citation information on their papers. Here we describe UKPMC and clarify the relationship between PMC and UKPMC, providing historical context and future directions, 10 years on from when PMC was first launched.
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UKPMC: a full text article resource for the life sciences
McEntyre, Johanna R.; Ananiadou, Sophia; Andrews, Stephen; Black, William J.; Boulderstone, Richard; Buttery, Paula; Chaplin, David; Chevuru, Sandeepreddy; Cobley, Norman; Coleman, Lee-Ann; Davey, Paul; Gupta, Bharti; Haji-Gholam, Lesley; Hawkins, Craig; Horne, Alan; Hubbard, Simon J.; Kim, Jee-Hyub; Lewin, Ian; Lyte, Vic; MacIntyre, Ross; Mansoor, Sami; Mason, Linda; McNaught, John; Newbold, Elizabeth; Nobata, Chikashi; Ong, Ernest; Pillai, Sharmila; Rebholz-Schuhmann, Dietrich; Rosie, Heather; Rowbotham, Rob; Rupp, C. J.; Stoehr, Peter; Vaughan, Philip
2011-01-01
UK PubMed Central (UKPMC) is a full-text article database that extends the functionality of the original PubMed Central (PMC) repository. The UKPMC project was launched as the first ‘mirror’ site to PMC, which in analogy to the International Nucleotide Sequence Database Collaboration, aims to provide international preservation of the open and free-access biomedical literature. UKPMC (http://ukpmc.ac.uk) has undergone considerable development since its inception in 2007 and now includes both a UKPMC and PubMed search, as well as access to other records such as Agricola, Patents and recent biomedical theses. UKPMC also differs from PubMed/PMC in that the full text and abstract information can be searched in an integrated manner from one input box. Furthermore, UKPMC contains ‘Cited By’ information as an alternative way to navigate the literature and has incorporated text-mining approaches to semantically enrich content and integrate it with related database resources. Finally, UKPMC also offers added-value services (UKPMC+) that enable grantees to deposit manuscripts, link papers to grants, publish online portfolios and view citation information on their papers. Here we describe UKPMC and clarify the relationship between PMC and UKPMC, providing historical context and future directions, 10 years on from when PMC was first launched. PMID:21062818
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OntoGene web services for biomedical text mining.
Rinaldi, Fabio; Clematide, Simon; Marques, Hernani; Ellendorff, Tilia; Romacker, Martin; Rodriguez-Esteban, Raul
2014-01-01
Text mining services are rapidly becoming a crucial component of various knowledge management pipelines, for example in the process of database curation, or for exploration and enrichment of biomedical data within the pharmaceutical industry. Traditional architectures, based on monolithic applications, do not offer sufficient flexibility for a wide range of use case scenarios, and therefore open architectures, as provided by web services, are attracting increased interest. We present an approach towards providing advanced text mining capabilities through web services, using a recently proposed standard for textual data interchange (BioC). The web services leverage a state-of-the-art platform for text mining (OntoGene) which has been tested in several community-organized evaluation challenges,with top ranked results in several of them.
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OntoGene web services for biomedical text mining
2014-01-01
Text mining services are rapidly becoming a crucial component of various knowledge management pipelines, for example in the process of database curation, or for exploration and enrichment of biomedical data within the pharmaceutical industry. Traditional architectures, based on monolithic applications, do not offer sufficient flexibility for a wide range of use case scenarios, and therefore open architectures, as provided by web services, are attracting increased interest. We present an approach towards providing advanced text mining capabilities through web services, using a recently proposed standard for textual data interchange (BioC). The web services leverage a state-of-the-art platform for text mining (OntoGene) which has been tested in several community-organized evaluation challenges, with top ranked results in several of them. PMID:25472638
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Database citation in full text biomedical articles.
Kafkas, Şenay; Kim, Jee-Hyub; McEntyre, Johanna R
2013-01-01
Molecular biology and literature databases represent essential infrastructure for life science research. Effective integration of these data resources requires that there are structured cross-references at the level of individual articles and biological records. Here, we describe the current patterns of how database entries are cited in research articles, based on analysis of the full text Open Access articles available from Europe PMC. Focusing on citation of entries in the European Nucleotide Archive (ENA), UniProt and Protein Data Bank, Europe (PDBe), we demonstrate that text mining doubles the number of structured annotations of database record citations supplied in journal articles by publishers. Many thousands of new literature-database relationships are found by text mining, since these relationships are also not present in the set of articles cited by database records. We recommend that structured annotation of database records in articles is extended to other databases, such as ArrayExpress and Pfam, entries from which are also cited widely in the literature. The very high precision and high-throughput of this text-mining pipeline makes this activity possible both accurately and at low cost, which will allow the development of new integrated data services.
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Database Citation in Full Text Biomedical Articles
Kafkas, Şenay; Kim, Jee-Hyub; McEntyre, Johanna R.
2013-01-01
Molecular biology and literature databases represent essential infrastructure for life science research. Effective integration of these data resources requires that there are structured cross-references at the level of individual articles and biological records. Here, we describe the current patterns of how database entries are cited in research articles, based on analysis of the full text Open Access articles available from Europe PMC. Focusing on citation of entries in the European Nucleotide Archive (ENA), UniProt and Protein Data Bank, Europe (PDBe), we demonstrate that text mining doubles the number of structured annotations of database record citations supplied in journal articles by publishers. Many thousands of new literature-database relationships are found by text mining, since these relationships are also not present in the set of articles cited by database records. We recommend that structured annotation of database records in articles is extended to other databases, such as ArrayExpress and Pfam, entries from which are also cited widely in the literature. The very high precision and high-throughput of this text-mining pipeline makes this activity possible both accurately and at low cost, which will allow the development of new integrated data services. PMID:23734176
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An Interactive Iterative Method for Electronic Searching of Large Literature Databases
ERIC Educational Resources Information Center
Hernandez, Marco A.
2013-01-01
PubMed® is an on-line literature database hosted by the U.S. National Library of Medicine. Containing over 21 million citations for biomedical literature--both abstracts and full text--in the areas of the life sciences, behavioral studies, chemistry, and bioengineering, PubMed® represents an important tool for researchers. PubMed® searches return…
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Liljekvist, Mads Svane; Andresen, Kristoffer; Pommergaard, Hans-Christian; Rosenberg, Jacob
2015-01-01
Background. Open access (OA) journals allows access to research papers free of charge to the reader. Traditionally, biomedical researchers use databases like MEDLINE and EMBASE to discover new advances. However, biomedical OA journals might not fulfill such databases' criteria, hindering dissemination. The Directory of Open Access Journals (DOAJ) is a database exclusively listing OA journals. The aim of this study was to investigate DOAJ's coverage of biomedical OA journals compared with the conventional biomedical databases. Methods. Information on all journals listed in four conventional biomedical databases (MEDLINE, PubMed Central, EMBASE and SCOPUS) and DOAJ were gathered. Journals were included if they were (1) actively publishing, (2) full OA, (3) prospectively indexed in one or more database, and (4) of biomedical subject. Impact factor and journal language were also collected. DOAJ was compared with conventional databases regarding the proportion of journals covered, along with their impact factor and publishing language. The proportion of journals with articles indexed by DOAJ was determined. Results. In total, 3,236 biomedical OA journals were included in the study. Of the included journals, 86.7% were listed in DOAJ. Combined, the conventional biomedical databases listed 75.0% of the journals; 18.7% in MEDLINE; 36.5% in PubMed Central; 51.5% in SCOPUS and 50.6% in EMBASE. Of the journals in DOAJ, 88.7% published in English and 20.6% had received impact factor for 2012 compared with 93.5% and 26.0%, respectively, for journals in the conventional biomedical databases. A subset of 51.1% and 48.5% of the journals in DOAJ had articles indexed from 2012 and 2013, respectively. Of journals exclusively listed in DOAJ, one journal had received an impact factor for 2012, and 59.6% of the journals had no content from 2013 indexed in DOAJ. Conclusions. DOAJ is the most complete registry of biomedical OA journals compared with five conventional biomedical databases. However, DOAJ only indexes articles for half of the biomedical journals listed, making it an incomplete source for biomedical research papers in general.
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Andresen, Kristoffer; Pommergaard, Hans-Christian; Rosenberg, Jacob
2015-01-01
Background. Open access (OA) journals allows access to research papers free of charge to the reader. Traditionally, biomedical researchers use databases like MEDLINE and EMBASE to discover new advances. However, biomedical OA journals might not fulfill such databases’ criteria, hindering dissemination. The Directory of Open Access Journals (DOAJ) is a database exclusively listing OA journals. The aim of this study was to investigate DOAJ’s coverage of biomedical OA journals compared with the conventional biomedical databases. Methods. Information on all journals listed in four conventional biomedical databases (MEDLINE, PubMed Central, EMBASE and SCOPUS) and DOAJ were gathered. Journals were included if they were (1) actively publishing, (2) full OA, (3) prospectively indexed in one or more database, and (4) of biomedical subject. Impact factor and journal language were also collected. DOAJ was compared with conventional databases regarding the proportion of journals covered, along with their impact factor and publishing language. The proportion of journals with articles indexed by DOAJ was determined. Results. In total, 3,236 biomedical OA journals were included in the study. Of the included journals, 86.7% were listed in DOAJ. Combined, the conventional biomedical databases listed 75.0% of the journals; 18.7% in MEDLINE; 36.5% in PubMed Central; 51.5% in SCOPUS and 50.6% in EMBASE. Of the journals in DOAJ, 88.7% published in English and 20.6% had received impact factor for 2012 compared with 93.5% and 26.0%, respectively, for journals in the conventional biomedical databases. A subset of 51.1% and 48.5% of the journals in DOAJ had articles indexed from 2012 and 2013, respectively. Of journals exclusively listed in DOAJ, one journal had received an impact factor for 2012, and 59.6% of the journals had no content from 2013 indexed in DOAJ. Conclusions. DOAJ is the most complete registry of biomedical OA journals compared with five conventional biomedical databases. However, DOAJ only indexes articles for half of the biomedical journals listed, making it an incomplete source for biomedical research papers in general. PMID:26038727
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Text Mining the Biomedical Literature
2007-11-05
activities, and repeating past mistakes, or 3) agencies not participating in joint efforts that would fully exploit each agency’s strengths...research and joint projects (multi- department, multi-agency, multi-national, and government-industry) appropriate? • Is the balance among single...overall database taxonomy, i.e., are there any concepts missing from any of the databases, and even if not, do all the concepts bear the same
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A Recent Advance in the Automatic Indexing of the Biomedical Literature
Névéol, Aurélie; Shooshan, Sonya E.; Humphrey, Susanne M.; Mork, James G.; Aronson, Alan R.
2009-01-01
The volume of biomedical literature has experienced explosive growth in recent years. This is reflected in the corresponding increase in the size of MEDLINE®, the largest bibliographic database of biomedical citations. Indexers at the U.S. National Library of Medicine (NLM) need efficient tools to help them accommodate the ensuing workload. After reviewing issues in the automatic assignment of Medical Subject Headings (MeSH® terms) to biomedical text, we focus more specifically on the new subheading attachment feature for NLM’s Medical Text Indexer (MTI). Natural Language Processing, statistical, and machine learning methods of producing automatic MeSH main heading/subheading pair recommendations were assessed independently and combined. The best combination achieves 48% precision and 30% recall. After validation by NLM indexers, a suitable combination of the methods presented in this paper was integrated into MTI as a subheading attachment feature producing MeSH indexing recommendations compliant with current state-of-the-art indexing practice. PMID:19166973
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Duchrow, Timo; Shtatland, Timur; Guettler, Daniel; Pivovarov, Misha; Kramer, Stefan; Weissleder, Ralph
2009-01-01
Background The breadth of biological databases and their information content continues to increase exponentially. Unfortunately, our ability to query such sources is still often suboptimal. Here, we introduce and apply community voting, database-driven text classification, and visual aids as a means to incorporate distributed expert knowledge, to automatically classify database entries and to efficiently retrieve them. Results Using a previously developed peptide database as an example, we compared several machine learning algorithms in their ability to classify abstracts of published literature results into categories relevant to peptide research, such as related or not related to cancer, angiogenesis, molecular imaging, etc. Ensembles of bagged decision trees met the requirements of our application best. No other algorithm consistently performed better in comparative testing. Moreover, we show that the algorithm produces meaningful class probability estimates, which can be used to visualize the confidence of automatic classification during the retrieval process. To allow viewing long lists of search results enriched by automatic classifications, we added a dynamic heat map to the web interface. We take advantage of community knowledge by enabling users to cast votes in Web 2.0 style in order to correct automated classification errors, which triggers reclassification of all entries. We used a novel framework in which the database "drives" the entire vote aggregation and reclassification process to increase speed while conserving computational resources and keeping the method scalable. In our experiments, we simulate community voting by adding various levels of noise to nearly perfectly labelled instances, and show that, under such conditions, classification can be improved significantly. Conclusion Using PepBank as a model database, we show how to build a classification-aided retrieval system that gathers training data from the community, is completely controlled by the database, scales well with concurrent change events, and can be adapted to add text classification capability to other biomedical databases. The system can be accessed at . PMID:19799796
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SparkText: Biomedical Text Mining on Big Data Framework.
Ye, Zhan; Tafti, Ahmad P; He, Karen Y; Wang, Kai; He, Max M
Many new biomedical research articles are published every day, accumulating rich information, such as genetic variants, genes, diseases, and treatments. Rapid yet accurate text mining on large-scale scientific literature can discover novel knowledge to better understand human diseases and to improve the quality of disease diagnosis, prevention, and treatment. In this study, we designed and developed an efficient text mining framework called SparkText on a Big Data infrastructure, which is composed of Apache Spark data streaming and machine learning methods, combined with a Cassandra NoSQL database. To demonstrate its performance for classifying cancer types, we extracted information (e.g., breast, prostate, and lung cancers) from tens of thousands of articles downloaded from PubMed, and then employed Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression to build prediction models to mine the articles. The accuracy of predicting a cancer type by SVM using the 29,437 full-text articles was 93.81%. While competing text-mining tools took more than 11 hours, SparkText mined the dataset in approximately 6 minutes. This study demonstrates the potential for mining large-scale scientific articles on a Big Data infrastructure, with real-time update from new articles published daily. SparkText can be extended to other areas of biomedical research.
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SparkText: Biomedical Text Mining on Big Data Framework
He, Karen Y.; Wang, Kai
2016-01-01
Background Many new biomedical research articles are published every day, accumulating rich information, such as genetic variants, genes, diseases, and treatments. Rapid yet accurate text mining on large-scale scientific literature can discover novel knowledge to better understand human diseases and to improve the quality of disease diagnosis, prevention, and treatment. Results In this study, we designed and developed an efficient text mining framework called SparkText on a Big Data infrastructure, which is composed of Apache Spark data streaming and machine learning methods, combined with a Cassandra NoSQL database. To demonstrate its performance for classifying cancer types, we extracted information (e.g., breast, prostate, and lung cancers) from tens of thousands of articles downloaded from PubMed, and then employed Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression to build prediction models to mine the articles. The accuracy of predicting a cancer type by SVM using the 29,437 full-text articles was 93.81%. While competing text-mining tools took more than 11 hours, SparkText mined the dataset in approximately 6 minutes. Conclusions This study demonstrates the potential for mining large-scale scientific articles on a Big Data infrastructure, with real-time update from new articles published daily. SparkText can be extended to other areas of biomedical research. PMID:27685652
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Chen, Hongyu; Martin, Bronwen; Daimon, Caitlin M; Maudsley, Stuart
2013-01-01
Text mining is rapidly becoming an essential technique for the annotation and analysis of large biological data sets. Biomedical literature currently increases at a rate of several thousand papers per week, making automated information retrieval methods the only feasible method of managing this expanding corpus. With the increasing prevalence of open-access journals and constant growth of publicly-available repositories of biomedical literature, literature mining has become much more effective with respect to the extraction of biomedically-relevant data. In recent years, text mining of popular databases such as MEDLINE has evolved from basic term-searches to more sophisticated natural language processing techniques, indexing and retrieval methods, structural analysis and integration of literature with associated metadata. In this review, we will focus on Latent Semantic Indexing (LSI), a computational linguistics technique increasingly used for a variety of biological purposes. It is noted for its ability to consistently outperform benchmark Boolean text searches and co-occurrence models at information retrieval and its power to extract indirect relationships within a data set. LSI has been used successfully to formulate new hypotheses, generate novel connections from existing data, and validate empirical data.
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Biomedical hypothesis generation by text mining and gene prioritization.
Petric, Ingrid; Ligeti, Balazs; Gyorffy, Balazs; Pongor, Sandor
2014-01-01
Text mining methods can facilitate the generation of biomedical hypotheses by suggesting novel associations between diseases and genes. Previously, we developed a rare-term model called RaJoLink (Petric et al, J. Biomed. Inform. 42(2): 219-227, 2009) in which hypotheses are formulated on the basis of terms rarely associated with a target domain. Since many current medical hypotheses are formulated in terms of molecular entities and molecular mechanisms, here we extend the methodology to proteins and genes, using a standardized vocabulary as well as a gene/protein network model. The proposed enhanced RaJoLink rare-term model combines text mining and gene prioritization approaches. Its utility is illustrated by finding known as well as potential gene-disease associations in ovarian cancer using MEDLINE abstracts and the STRING database.
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Müller, H-M; Van Auken, K M; Li, Y; Sternberg, P W
2018-03-09
The biomedical literature continues to grow at a rapid pace, making the challenge of knowledge retrieval and extraction ever greater. Tools that provide a means to search and mine the full text of literature thus represent an important way by which the efficiency of these processes can be improved. We describe the next generation of the Textpresso information retrieval system, Textpresso Central (TPC). TPC builds on the strengths of the original system by expanding the full text corpus to include the PubMed Central Open Access Subset (PMC OA), as well as the WormBase C. elegans bibliography. In addition, TPC allows users to create a customized corpus by uploading and processing documents of their choosing. TPC is UIMA compliant, to facilitate compatibility with external processing modules, and takes advantage of Lucene indexing and search technology for efficient handling of millions of full text documents. Like Textpresso, TPC searches can be performed using keywords and/or categories (semantically related groups of terms), but to provide better context for interpreting and validating queries, search results may now be viewed as highlighted passages in the context of full text. To facilitate biocuration efforts, TPC also allows users to select text spans from the full text and annotate them, create customized curation forms for any data type, and send resulting annotations to external curation databases. As an example of such a curation form, we describe integration of TPC with the Noctua curation tool developed by the Gene Ontology (GO) Consortium. Textpresso Central is an online literature search and curation platform that enables biocurators and biomedical researchers to search and mine the full text of literature by integrating keyword and category searches with viewing search results in the context of the full text. It also allows users to create customized curation interfaces, use those interfaces to make annotations linked to supporting evidence statements, and then send those annotations to any database in the world. Textpresso Central URL: http://www.textpresso.org/tpc.
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Simmons, Michael; Singhal, Ayush; Lu, Zhiyong
2018-01-01
The key question of precision medicine is whether it is possible to find clinically actionable granularity in diagnosing disease and classifying patient risk. The advent of next generation sequencing and the widespread adoption of electronic health records (EHRs) have provided clinicians and researchers a wealth of data and made possible the precise characterization of individual patient genotypes and phenotypes. Unstructured text — found in biomedical publications and clinical notes — is an important component of genotype and phenotype knowledge. Publications in the biomedical literature provide essential information for interpreting genetic data. Likewise, clinical notes contain the richest source of phenotype information in EHRs. Text mining can render these texts computationally accessible and support information extraction and hypothesis generation. This chapter reviews the mechanics of text mining in precision medicine and discusses several specific use cases, including database curation for personalized cancer medicine, patient outcome prediction from EHR-derived cohorts, and pharmacogenomic research. Taken as a whole, these use cases demonstrate how text mining enables effective utilization of existing knowledge sources and thus promotes increased value for patients and healthcare systems. Text mining is an indispensable tool for translating genotype-phenotype data into effective clinical care that will undoubtedly play an important role in the eventual realization of precision medicine. PMID:27807747
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Simmons, Michael; Singhal, Ayush; Lu, Zhiyong
2016-01-01
The key question of precision medicine is whether it is possible to find clinically actionable granularity in diagnosing disease and classifying patient risk. The advent of next-generation sequencing and the widespread adoption of electronic health records (EHRs) have provided clinicians and researchers a wealth of data and made possible the precise characterization of individual patient genotypes and phenotypes. Unstructured text-found in biomedical publications and clinical notes-is an important component of genotype and phenotype knowledge. Publications in the biomedical literature provide essential information for interpreting genetic data. Likewise, clinical notes contain the richest source of phenotype information in EHRs. Text mining can render these texts computationally accessible and support information extraction and hypothesis generation. This chapter reviews the mechanics of text mining in precision medicine and discusses several specific use cases, including database curation for personalized cancer medicine, patient outcome prediction from EHR-derived cohorts, and pharmacogenomic research. Taken as a whole, these use cases demonstrate how text mining enables effective utilization of existing knowledge sources and thus promotes increased value for patients and healthcare systems. Text mining is an indispensable tool for translating genotype-phenotype data into effective clinical care that will undoubtedly play an important role in the eventual realization of precision medicine.
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PedAM: a database for Pediatric Disease Annotation and Medicine.
Jia, Jinmeng; An, Zhongxin; Ming, Yue; Guo, Yongli; Li, Wei; Li, Xin; Liang, Yunxiang; Guo, Dongming; Tai, Jun; Chen, Geng; Jin, Yaqiong; Liu, Zhimei; Ni, Xin; Shi, Tieliu
2018-01-04
There is a significant number of children around the world suffering from the consequence of the misdiagnosis and ineffective treatment for various diseases. To facilitate the precision medicine in pediatrics, a database namely the Pediatric Disease Annotations & Medicines (PedAM) has been built to standardize and classify pediatric diseases. The PedAM integrates both biomedical resources and clinical data from Electronic Medical Records to support the development of computational tools, by which enables robust data analysis and integration. It also uses disease-manifestation (D-M) integrated from existing biomedical ontologies as prior knowledge to automatically recognize text-mined, D-M-specific syntactic patterns from 774 514 full-text articles and 8 848 796 abstracts in MEDLINE. Additionally, disease connections based on phenotypes or genes can be visualized on the web page of PedAM. Currently, the PedAM contains standardized 8528 pediatric disease terms (4542 unique disease concepts and 3986 synonyms) with eight annotation fields for each disease, including definition synonyms, gene, symptom, cross-reference (Xref), human phenotypes and its corresponding phenotypes in the mouse. The database PedAM is freely accessible at http://www.unimd.org/pedam/. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.
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Relational Databases and Biomedical Big Data.
de Silva, N H Nisansa D
2017-01-01
In various biomedical applications that collect, handle, and manipulate data, the amounts of data tend to build up and venture into the range identified as bigdata. In such occurrences, a design decision has to be taken as to what type of database would be used to handle this data. More often than not, the default and classical solution to this in the biomedical domain according to past research is relational databases. While this used to be the norm for a long while, it is evident that there is a trend to move away from relational databases in favor of other types and paradigms of databases. However, it still has paramount importance to understand the interrelation that exists between biomedical big data and relational databases. This chapter will review the pros and cons of using relational databases to store biomedical big data that previous researches have discussed and used.
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GDRMS: a system for automatic extraction of the disease-centre relation
NASA Astrophysics Data System (ADS)
Yang, Ronggen; Zhang, Yue; Gong, Lejun
2012-01-01
With the rapidly increasing of biomedical literature, the deluge of new articles is leading to information overload. Extracting the available knowledge from the huge amount of biomedical literature has become a major challenge. GDRMS is developed as a tool that extracts the relationship between disease and gene, gene and gene from biomedical literatures using text mining technology. It is a ruled-based system which also provides disease-centre network visualization, constructs the disease-gene database, and represents a gene engine for understanding the function of the gene. The main focus of GDRMS is to provide a valuable opportunity to explore the relationship between disease and gene for the research community about etiology of disease.
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Alkemio: association of chemicals with biomedical topics by text and data mining
Gijón-Correas, José A.; Andrade-Navarro, Miguel A.; Fontaine, Jean F.
2014-01-01
The PubMed® database of biomedical citations allows the retrieval of scientific articles studying the function of chemicals in biology and medicine. Mining millions of available citations to search reported associations between chemicals and topics of interest would require substantial human time. We have implemented the Alkemio text mining web tool and SOAP web service to help in this task. The tool uses biomedical articles discussing chemicals (including drugs), predicts their relatedness to the query topic with a naïve Bayesian classifier and ranks all chemicals by P-values computed from random simulations. Benchmarks on seven human pathways showed good retrieval performance (areas under the receiver operating characteristic curves ranged from 73.6 to 94.5%). Comparison with existing tools to retrieve chemicals associated to eight diseases showed the higher precision and recall of Alkemio when considering the top 10 candidate chemicals. Alkemio is a high performing web tool ranking chemicals for any biomedical topics and it is free to non-commercial users. Availability: http://cbdm.mdc-berlin.de/∼medlineranker/cms/alkemio. PMID:24838570
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Singhal, Ayush; Simmons, Michael; Lu, Zhiyong
2016-11-01
The practice of precision medicine will ultimately require databases of genes and mutations for healthcare providers to reference in order to understand the clinical implications of each patient's genetic makeup. Although the highest quality databases require manual curation, text mining tools can facilitate the curation process, increasing accuracy, coverage, and productivity. However, to date there are no available text mining tools that offer high-accuracy performance for extracting such triplets from biomedical literature. In this paper we propose a high-performance machine learning approach to automate the extraction of disease-gene-variant triplets from biomedical literature. Our approach is unique because we identify the genes and protein products associated with each mutation from not just the local text content, but from a global context as well (from the Internet and from all literature in PubMed). Our approach also incorporates protein sequence validation and disease association using a novel text-mining-based machine learning approach. We extract disease-gene-variant triplets from all abstracts in PubMed related to a set of ten important diseases (breast cancer, prostate cancer, pancreatic cancer, lung cancer, acute myeloid leukemia, Alzheimer's disease, hemochromatosis, age-related macular degeneration (AMD), diabetes mellitus, and cystic fibrosis). We then evaluate our approach in two ways: (1) a direct comparison with the state of the art using benchmark datasets; (2) a validation study comparing the results of our approach with entries in a popular human-curated database (UniProt) for each of the previously mentioned diseases. In the benchmark comparison, our full approach achieves a 28% improvement in F1-measure (from 0.62 to 0.79) over the state-of-the-art results. For the validation study with UniProt Knowledgebase (KB), we present a thorough analysis of the results and errors. Across all diseases, our approach returned 272 triplets (disease-gene-variant) that overlapped with entries in UniProt and 5,384 triplets without overlap in UniProt. Analysis of the overlapping triplets and of a stratified sample of the non-overlapping triplets revealed accuracies of 93% and 80% for the respective categories (cumulative accuracy, 77%). We conclude that our process represents an important and broadly applicable improvement to the state of the art for curation of disease-gene-variant relationships.
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Document Exploration and Automatic Knowledge Extraction for Unstructured Biomedical Text
NASA Astrophysics Data System (ADS)
Chu, S.; Totaro, G.; Doshi, N.; Thapar, S.; Mattmann, C. A.; Ramirez, P.
2015-12-01
We describe our work on building a web-browser based document reader with built-in exploration tool and automatic concept extraction of medical entities for biomedical text. Vast amounts of biomedical information are offered in unstructured text form through scientific publications and R&D reports. Utilizing text mining can help us to mine information and extract relevant knowledge from a plethora of biomedical text. The ability to employ such technologies to aid researchers in coping with information overload is greatly desirable. In recent years, there has been an increased interest in automatic biomedical concept extraction [1, 2] and intelligent PDF reader tools with the ability to search on content and find related articles [3]. Such reader tools are typically desktop applications and are limited to specific platforms. Our goal is to provide researchers with a simple tool to aid them in finding, reading, and exploring documents. Thus, we propose a web-based document explorer, which we called Shangri-Docs, which combines a document reader with automatic concept extraction and highlighting of relevant terms. Shangri-Docsalso provides the ability to evaluate a wide variety of document formats (e.g. PDF, Words, PPT, text, etc.) and to exploit the linked nature of the Web and personal content by performing searches on content from public sites (e.g. Wikipedia, PubMed) and private cataloged databases simultaneously. Shangri-Docsutilizes Apache cTAKES (clinical Text Analysis and Knowledge Extraction System) [4] and Unified Medical Language System (UMLS) to automatically identify and highlight terms and concepts, such as specific symptoms, diseases, drugs, and anatomical sites, mentioned in the text. cTAKES was originally designed specially to extract information from clinical medical records. Our investigation leads us to extend the automatic knowledge extraction process of cTAKES for biomedical research domain by improving the ontology guided information extraction process. We will describe our experience and implementation of our system and share lessons learned from our development. We will also discuss ways in which this could be adapted to other science fields. [1] Funk et al., 2014. [2] Kang et al., 2014. [3] Utopia Documents, http://utopiadocs.com [4] Apache cTAKES, http://ctakes.apache.org
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Image query and indexing for digital x rays
NASA Astrophysics Data System (ADS)
Long, L. Rodney; Thoma, George R.
1998-12-01
The web-based medical information retrieval system (WebMIRS) allows interned access to databases containing 17,000 digitized x-ray spine images and associated text data from National Health and Nutrition Examination Surveys (NHANES). WebMIRS allows SQL query of the text, and viewing of the returned text records and images using a standard browser. We are now working (1) to determine utility of data directly derived from the images in our databases, and (2) to investigate the feasibility of computer-assisted or automated indexing of the images to support image retrieval of images of interest to biomedical researchers in the field of osteoarthritis. To build an initial database based on image data, we are manually segmenting a subset of the vertebrae, using techniques from vertebral morphometry. From this, we will derive and add to the database vertebral features. This image-derived data will enhance the user's data access capability by enabling the creation of combined SQL/image-content queries.
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Concept annotation in the CRAFT corpus.
Bada, Michael; Eckert, Miriam; Evans, Donald; Garcia, Kristin; Shipley, Krista; Sitnikov, Dmitry; Baumgartner, William A; Cohen, K Bretonnel; Verspoor, Karin; Blake, Judith A; Hunter, Lawrence E
2012-07-09
Manually annotated corpora are critical for the training and evaluation of automated methods to identify concepts in biomedical text. This paper presents the concept annotations of the Colorado Richly Annotated Full-Text (CRAFT) Corpus, a collection of 97 full-length, open-access biomedical journal articles that have been annotated both semantically and syntactically to serve as a research resource for the biomedical natural-language-processing (NLP) community. CRAFT identifies all mentions of nearly all concepts from nine prominent biomedical ontologies and terminologies: the Cell Type Ontology, the Chemical Entities of Biological Interest ontology, the NCBI Taxonomy, the Protein Ontology, the Sequence Ontology, the entries of the Entrez Gene database, and the three subontologies of the Gene Ontology. The first public release includes the annotations for 67 of the 97 articles, reserving two sets of 15 articles for future text-mining competitions (after which these too will be released). Concept annotations were created based on a single set of guidelines, which has enabled us to achieve consistently high interannotator agreement. As the initial 67-article release contains more than 560,000 tokens (and the full set more than 790,000 tokens), our corpus is among the largest gold-standard annotated biomedical corpora. Unlike most others, the journal articles that comprise the corpus are drawn from diverse biomedical disciplines and are marked up in their entirety. Additionally, with a concept-annotation count of nearly 100,000 in the 67-article subset (and more than 140,000 in the full collection), the scale of conceptual markup is also among the largest of comparable corpora. The concept annotations of the CRAFT Corpus have the potential to significantly advance biomedical text mining by providing a high-quality gold standard for NLP systems. The corpus, annotation guidelines, and other associated resources are freely available at http://bionlp-corpora.sourceforge.net/CRAFT/index.shtml.
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Concept annotation in the CRAFT corpus
2012-01-01
Background Manually annotated corpora are critical for the training and evaluation of automated methods to identify concepts in biomedical text. Results This paper presents the concept annotations of the Colorado Richly Annotated Full-Text (CRAFT) Corpus, a collection of 97 full-length, open-access biomedical journal articles that have been annotated both semantically and syntactically to serve as a research resource for the biomedical natural-language-processing (NLP) community. CRAFT identifies all mentions of nearly all concepts from nine prominent biomedical ontologies and terminologies: the Cell Type Ontology, the Chemical Entities of Biological Interest ontology, the NCBI Taxonomy, the Protein Ontology, the Sequence Ontology, the entries of the Entrez Gene database, and the three subontologies of the Gene Ontology. The first public release includes the annotations for 67 of the 97 articles, reserving two sets of 15 articles for future text-mining competitions (after which these too will be released). Concept annotations were created based on a single set of guidelines, which has enabled us to achieve consistently high interannotator agreement. Conclusions As the initial 67-article release contains more than 560,000 tokens (and the full set more than 790,000 tokens), our corpus is among the largest gold-standard annotated biomedical corpora. Unlike most others, the journal articles that comprise the corpus are drawn from diverse biomedical disciplines and are marked up in their entirety. Additionally, with a concept-annotation count of nearly 100,000 in the 67-article subset (and more than 140,000 in the full collection), the scale of conceptual markup is also among the largest of comparable corpora. The concept annotations of the CRAFT Corpus have the potential to significantly advance biomedical text mining by providing a high-quality gold standard for NLP systems. The corpus, annotation guidelines, and other associated resources are freely available at http://bionlp-corpora.sourceforge.net/CRAFT/index.shtml. PMID:22776079
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GeneView: a comprehensive semantic search engine for PubMed.
Thomas, Philippe; Starlinger, Johannes; Vowinkel, Alexander; Arzt, Sebastian; Leser, Ulf
2012-07-01
Research results are primarily published in scientific literature and curation efforts cannot keep up with the rapid growth of published literature. The plethora of knowledge remains hidden in large text repositories like MEDLINE. Consequently, life scientists have to spend a great amount of time searching for specific information. The enormous ambiguity among most names of biomedical objects such as genes, chemicals and diseases often produces too large and unspecific search results. We present GeneView, a semantic search engine for biomedical knowledge. GeneView is built upon a comprehensively annotated version of PubMed abstracts and openly available PubMed Central full texts. This semi-structured representation of biomedical texts enables a number of features extending classical search engines. For instance, users may search for entities using unique database identifiers or they may rank documents by the number of specific mentions they contain. Annotation is performed by a multitude of state-of-the-art text-mining tools for recognizing mentions from 10 entity classes and for identifying protein-protein interactions. GeneView currently contains annotations for >194 million entities from 10 classes for ∼21 million citations with 271,000 full text bodies. GeneView can be searched at http://bc3.informatik.hu-berlin.de/.
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Bravo, Àlex; Piñero, Janet; Queralt-Rosinach, Núria; Rautschka, Michael; Furlong, Laura I
2015-02-21
Current biomedical research needs to leverage and exploit the large amount of information reported in scientific publications. Automated text mining approaches, in particular those aimed at finding relationships between entities, are key for identification of actionable knowledge from free text repositories. We present the BeFree system aimed at identifying relationships between biomedical entities with a special focus on genes and their associated diseases. By exploiting morpho-syntactic information of the text, BeFree is able to identify gene-disease, drug-disease and drug-target associations with state-of-the-art performance. The application of BeFree to real-case scenarios shows its effectiveness in extracting information relevant for translational research. We show the value of the gene-disease associations extracted by BeFree through a number of analyses and integration with other data sources. BeFree succeeds in identifying genes associated to a major cause of morbidity worldwide, depression, which are not present in other public resources. Moreover, large-scale extraction and analysis of gene-disease associations, and integration with current biomedical knowledge, provided interesting insights on the kind of information that can be found in the literature, and raised challenges regarding data prioritization and curation. We found that only a small proportion of the gene-disease associations discovered by using BeFree is collected in expert-curated databases. Thus, there is a pressing need to find alternative strategies to manual curation, in order to review, prioritize and curate text-mining data and incorporate it into domain-specific databases. We present our strategy for data prioritization and discuss its implications for supporting biomedical research and applications. BeFree is a novel text mining system that performs competitively for the identification of gene-disease, drug-disease and drug-target associations. Our analyses show that mining only a small fraction of MEDLINE results in a large dataset of gene-disease associations, and only a small proportion of this dataset is actually recorded in curated resources (2%), raising several issues on data prioritization and curation. We propose that joint analysis of text mined data with data curated by experts appears as a suitable approach to both assess data quality and highlight novel and interesting information.
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Shatkay, Hagit; Pan, Fengxia; Rzhetsky, Andrey; Wilbur, W. John
2008-01-01
Motivation: Much current research in biomedical text mining is concerned with serving biologists by extracting certain information from scientific text. We note that there is no ‘average biologist’ client; different users have distinct needs. For instance, as noted in past evaluation efforts (BioCreative, TREC, KDD) database curators are often interested in sentences showing experimental evidence and methods. Conversely, lab scientists searching for known information about a protein may seek facts, typically stated with high confidence. Text-mining systems can target specific end-users and become more effective, if the system can first identify text regions rich in the type of scientific content that is of interest to the user, retrieve documents that have many such regions, and focus on fact extraction from these regions. Here, we study the ability to characterize and classify such text automatically. We have recently introduced a multi-dimensional categorization and annotation scheme, developed to be applicable to a wide variety of biomedical documents and scientific statements, while intended to support specific biomedical retrieval and extraction tasks. Results: The annotation scheme was applied to a large corpus in a controlled effort by eight independent annotators, where three individual annotators independently tagged each sentence. We then trained and tested machine learning classifiers to automatically categorize sentence fragments based on the annotation. We discuss here the issues involved in this task, and present an overview of the results. The latter strongly suggest that automatic annotation along most of the dimensions is highly feasible, and that this new framework for scientific sentence categorization is applicable in practice. Contact: shatkay@cs.queensu.ca PMID:18718948
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Knowledge based word-concept model estimation and refinement for biomedical text mining.
Jimeno Yepes, Antonio; Berlanga, Rafael
2015-02-01
Text mining of scientific literature has been essential for setting up large public biomedical databases, which are being widely used by the research community. In the biomedical domain, the existence of a large number of terminological resources and knowledge bases (KB) has enabled a myriad of machine learning methods for different text mining related tasks. Unfortunately, KBs have not been devised for text mining tasks but for human interpretation, thus performance of KB-based methods is usually lower when compared to supervised machine learning methods. The disadvantage of supervised methods though is they require labeled training data and therefore not useful for large scale biomedical text mining systems. KB-based methods do not have this limitation. In this paper, we describe a novel method to generate word-concept probabilities from a KB, which can serve as a basis for several text mining tasks. This method not only takes into account the underlying patterns within the descriptions contained in the KB but also those in texts available from large unlabeled corpora such as MEDLINE. The parameters of the model have been estimated without training data. Patterns from MEDLINE have been built using MetaMap for entity recognition and related using co-occurrences. The word-concept probabilities were evaluated on the task of word sense disambiguation (WSD). The results showed that our method obtained a higher degree of accuracy than other state-of-the-art approaches when evaluated on the MSH WSD data set. We also evaluated our method on the task of document ranking using MEDLINE citations. These results also showed an increase in performance over existing baseline retrieval approaches. Copyright © 2014 Elsevier Inc. All rights reserved.
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KaBOB: ontology-based semantic integration of biomedical databases.
Livingston, Kevin M; Bada, Michael; Baumgartner, William A; Hunter, Lawrence E
2015-04-23
The ability to query many independent biological databases using a common ontology-based semantic model would facilitate deeper integration and more effective utilization of these diverse and rapidly growing resources. Despite ongoing work moving toward shared data formats and linked identifiers, significant problems persist in semantic data integration in order to establish shared identity and shared meaning across heterogeneous biomedical data sources. We present five processes for semantic data integration that, when applied collectively, solve seven key problems. These processes include making explicit the differences between biomedical concepts and database records, aggregating sets of identifiers denoting the same biomedical concepts across data sources, and using declaratively represented forward-chaining rules to take information that is variably represented in source databases and integrating it into a consistent biomedical representation. We demonstrate these processes and solutions by presenting KaBOB (the Knowledge Base Of Biomedicine), a knowledge base of semantically integrated data from 18 prominent biomedical databases using common representations grounded in Open Biomedical Ontologies. An instance of KaBOB with data about humans and seven major model organisms can be built using on the order of 500 million RDF triples. All source code for building KaBOB is available under an open-source license. KaBOB is an integrated knowledge base of biomedical data representationally based in prominent, actively maintained Open Biomedical Ontologies, thus enabling queries of the underlying data in terms of biomedical concepts (e.g., genes and gene products, interactions and processes) rather than features of source-specific data schemas or file formats. KaBOB resolves many of the issues that routinely plague biomedical researchers intending to work with data from multiple data sources and provides a platform for ongoing data integration and development and for formal reasoning over a wealth of integrated biomedical data.
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Pub-Med-dot-com, here we come!
Pulst, Stefan M
2016-08-01
As of April 8, 2016, articles in Neurology® Genetics can be searched using PubMed. Launched in 1996, PubMed is a search engine that accesses citations and abstracts of more than 26 million articles. Its primary sources include the MEDLINE database, which was started in the 1960s, and biomedical and life sciences journal articles that date back to 1946. In addition, PubMed accesses other sources, for example, citations to those life sciences journals that submit full-text articles to PubMed Central (PMC). PubMed Central was launched in 2000 as a free archive of biomedical and life science journals.
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Alkemio: association of chemicals with biomedical topics by text and data mining.
Gijón-Correas, José A; Andrade-Navarro, Miguel A; Fontaine, Jean F
2014-07-01
The PubMed® database of biomedical citations allows the retrieval of scientific articles studying the function of chemicals in biology and medicine. Mining millions of available citations to search reported associations between chemicals and topics of interest would require substantial human time. We have implemented the Alkemio text mining web tool and SOAP web service to help in this task. The tool uses biomedical articles discussing chemicals (including drugs), predicts their relatedness to the query topic with a naïve Bayesian classifier and ranks all chemicals by P-values computed from random simulations. Benchmarks on seven human pathways showed good retrieval performance (areas under the receiver operating characteristic curves ranged from 73.6 to 94.5%). Comparison with existing tools to retrieve chemicals associated to eight diseases showed the higher precision and recall of Alkemio when considering the top 10 candidate chemicals. Alkemio is a high performing web tool ranking chemicals for any biomedical topics and it is free to non-commercial users. http://cbdm.mdc-berlin.de/∼medlineranker/cms/alkemio. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.
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Bachman, John A; Gyori, Benjamin M; Sorger, Peter K
2018-06-28
For automated reading of scientific publications to extract useful information about molecular mechanisms it is critical that genes, proteins and other entities be correctly associated with uniform identifiers, a process known as named entity linking or "grounding." Correct grounding is essential for resolving relationships among mined information, curated interaction databases, and biological datasets. The accuracy of this process is largely dependent on the availability of machine-readable resources associating synonyms and abbreviations commonly found in biomedical literature with uniform identifiers. In a task involving automated reading of ∼215,000 articles using the REACH event extraction software we found that grounding was disproportionately inaccurate for multi-protein families (e.g., "AKT") and complexes with multiple subunits (e.g."NF- κB"). To address this problem we constructed FamPlex, a manually curated resource defining protein families and complexes as they are commonly encountered in biomedical text. In FamPlex the gene-level constituents of families and complexes are defined in a flexible format allowing for multi-level, hierarchical membership. To create FamPlex, text strings corresponding to entities were identified empirically from literature and linked manually to uniform identifiers; these identifiers were also mapped to equivalent entries in multiple related databases. FamPlex also includes curated prefix and suffix patterns that improve named entity recognition and event extraction. Evaluation of REACH extractions on a test corpus of ∼54,000 articles showed that FamPlex significantly increased grounding accuracy for families and complexes (from 15 to 71%). The hierarchical organization of entities in FamPlex also made it possible to integrate otherwise unconnected mechanistic information across families, subfamilies, and individual proteins. Applications of FamPlex to the TRIPS/DRUM reading system and the Biocreative VI Bioentity Normalization Task dataset demonstrated the utility of FamPlex in other settings. FamPlex is an effective resource for improving named entity recognition, grounding, and relationship resolution in automated reading of biomedical text. The content in FamPlex is available in both tabular and Open Biomedical Ontology formats at https://github.com/sorgerlab/famplex under the Creative Commons CC0 license and has been integrated into the TRIPS/DRUM and REACH reading systems.
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Kafkas, Şenay; Kim, Jee-Hyub; Pi, Xingjun; McEntyre, Johanna R
2015-01-01
In this study, we present an analysis of data citation practices in full text research articles and their corresponding supplementary data files, made available in the Open Access set of articles from Europe PubMed Central. Our aim is to investigate whether supplementary data files should be considered as a source of information for integrating the literature with biomolecular databases. Using text-mining methods to identify and extract a variety of core biological database accession numbers, we found that the supplemental data files contain many more database citations than the body of the article, and that those citations often take the form of a relatively small number of articles citing large collections of accession numbers in text-based files. Moreover, citation of value-added databases derived from submission databases (such as Pfam, UniProt or Ensembl) is common, demonstrating the reuse of these resources as datasets in themselves. All the database accession numbers extracted from the supplementary data are publicly accessible from http://dx.doi.org/10.5281/zenodo.11771. Our study suggests that supplementary data should be considered when linking articles with data, in curation pipelines, and in information retrieval tasks in order to make full use of the entire research article. These observations highlight the need to improve the management of supplemental data in general, in order to make this information more discoverable and useful.
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Recent progress in automatically extracting information from the pharmacogenomic literature
Garten, Yael; Coulet, Adrien; Altman, Russ B
2011-01-01
The biomedical literature holds our understanding of pharmacogenomics, but it is dispersed across many journals. In order to integrate our knowledge, connect important facts across publications and generate new hypotheses we must organize and encode the contents of the literature. By creating databases of structured pharmocogenomic knowledge, we can make the value of the literature much greater than the sum of the individual reports. We can, for example, generate candidate gene lists or interpret surprising hits in genome-wide association studies. Text mining automatically adds structure to the unstructured knowledge embedded in millions of publications, and recent years have seen a surge in work on biomedical text mining, some specific to pharmacogenomics literature. These methods enable extraction of specific types of information and can also provide answers to general, systemic queries. In this article, we describe the main tasks of text mining in the context of pharmacogenomics, summarize recent applications and anticipate the next phase of text mining applications. PMID:21047206
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BioC implementations in Go, Perl, Python and Ruby
Liu, Wanli; Islamaj Doğan, Rezarta; Kwon, Dongseop; Marques, Hernani; Rinaldi, Fabio; Wilbur, W. John; Comeau, Donald C.
2014-01-01
As part of a communitywide effort for evaluating text mining and information extraction systems applied to the biomedical domain, BioC is focused on the goal of interoperability, currently a major barrier to wide-scale adoption of text mining tools. BioC is a simple XML format, specified by DTD, for exchanging data for biomedical natural language processing. With initial implementations in C++ and Java, BioC provides libraries of code for reading and writing BioC text documents and annotations. We extend BioC to Perl, Python, Go and Ruby. We used SWIG to extend the C++ implementation for Perl and one Python implementation. A second Python implementation and the Ruby implementation use native data structures and libraries. BioC is also implemented in the Google language Go. BioC modules are functional in all of these languages, which can facilitate text mining tasks. BioC implementations are freely available through the BioC site: http://bioc.sourceforge.net. Database URL: http://bioc.sourceforge.net/ PMID:24961236
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The BioLexicon: a large-scale terminological resource for biomedical text mining
2011-01-01
Background Due to the rapidly expanding body of biomedical literature, biologists require increasingly sophisticated and efficient systems to help them to search for relevant information. Such systems should account for the multiple written variants used to represent biomedical concepts, and allow the user to search for specific pieces of knowledge (or events) involving these concepts, e.g., protein-protein interactions. Such functionality requires access to detailed information about words used in the biomedical literature. Existing databases and ontologies often have a specific focus and are oriented towards human use. Consequently, biological knowledge is dispersed amongst many resources, which often do not attempt to account for the large and frequently changing set of variants that appear in the literature. Additionally, such resources typically do not provide information about how terms relate to each other in texts to describe events. Results This article provides an overview of the design, construction and evaluation of a large-scale lexical and conceptual resource for the biomedical domain, the BioLexicon. The resource can be exploited by text mining tools at several levels, e.g., part-of-speech tagging, recognition of biomedical entities, and the extraction of events in which they are involved. As such, the BioLexicon must account for real usage of words in biomedical texts. In particular, the BioLexicon gathers together different types of terms from several existing data resources into a single, unified repository, and augments them with new term variants automatically extracted from biomedical literature. Extraction of events is facilitated through the inclusion of biologically pertinent verbs (around which events are typically organized) together with information about typical patterns of grammatical and semantic behaviour, which are acquired from domain-specific texts. In order to foster interoperability, the BioLexicon is modelled using the Lexical Markup Framework, an ISO standard. Conclusions The BioLexicon contains over 2.2 M lexical entries and over 1.8 M terminological variants, as well as over 3.3 M semantic relations, including over 2 M synonymy relations. Its exploitation can benefit both application developers and users. We demonstrate some such benefits by describing integration of the resource into a number of different tools, and evaluating improvements in performance that this can bring. PMID:21992002
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The BioLexicon: a large-scale terminological resource for biomedical text mining.
Thompson, Paul; McNaught, John; Montemagni, Simonetta; Calzolari, Nicoletta; del Gratta, Riccardo; Lee, Vivian; Marchi, Simone; Monachini, Monica; Pezik, Piotr; Quochi, Valeria; Rupp, C J; Sasaki, Yutaka; Venturi, Giulia; Rebholz-Schuhmann, Dietrich; Ananiadou, Sophia
2011-10-12
Due to the rapidly expanding body of biomedical literature, biologists require increasingly sophisticated and efficient systems to help them to search for relevant information. Such systems should account for the multiple written variants used to represent biomedical concepts, and allow the user to search for specific pieces of knowledge (or events) involving these concepts, e.g., protein-protein interactions. Such functionality requires access to detailed information about words used in the biomedical literature. Existing databases and ontologies often have a specific focus and are oriented towards human use. Consequently, biological knowledge is dispersed amongst many resources, which often do not attempt to account for the large and frequently changing set of variants that appear in the literature. Additionally, such resources typically do not provide information about how terms relate to each other in texts to describe events. This article provides an overview of the design, construction and evaluation of a large-scale lexical and conceptual resource for the biomedical domain, the BioLexicon. The resource can be exploited by text mining tools at several levels, e.g., part-of-speech tagging, recognition of biomedical entities, and the extraction of events in which they are involved. As such, the BioLexicon must account for real usage of words in biomedical texts. In particular, the BioLexicon gathers together different types of terms from several existing data resources into a single, unified repository, and augments them with new term variants automatically extracted from biomedical literature. Extraction of events is facilitated through the inclusion of biologically pertinent verbs (around which events are typically organized) together with information about typical patterns of grammatical and semantic behaviour, which are acquired from domain-specific texts. In order to foster interoperability, the BioLexicon is modelled using the Lexical Markup Framework, an ISO standard. The BioLexicon contains over 2.2 M lexical entries and over 1.8 M terminological variants, as well as over 3.3 M semantic relations, including over 2 M synonymy relations. Its exploitation can benefit both application developers and users. We demonstrate some such benefits by describing integration of the resource into a number of different tools, and evaluating improvements in performance that this can bring.
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Text mining applications in psychiatry: a systematic literature review.
Abbe, Adeline; Grouin, Cyril; Zweigenbaum, Pierre; Falissard, Bruno
2016-06-01
The expansion of biomedical literature is creating the need for efficient tools to keep pace with increasing volumes of information. Text mining (TM) approaches are becoming essential to facilitate the automated extraction of useful biomedical information from unstructured text. We reviewed the applications of TM in psychiatry, and explored its advantages and limitations. A systematic review of the literature was carried out using the CINAHL, Medline, EMBASE, PsycINFO and Cochrane databases. In this review, 1103 papers were screened, and 38 were included as applications of TM in psychiatric research. Using TM and content analysis, we identified four major areas of application: (1) Psychopathology (i.e. observational studies focusing on mental illnesses) (2) the Patient perspective (i.e. patients' thoughts and opinions), (3) Medical records (i.e. safety issues, quality of care and description of treatments), and (4) Medical literature (i.e. identification of new scientific information in the literature). The information sources were qualitative studies, Internet postings, medical records and biomedical literature. Our work demonstrates that TM can contribute to complex research tasks in psychiatry. We discuss the benefits, limits, and further applications of this tool in the future. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.
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Using Ontology Fingerprints to disambiguate gene name entities in the biomedical literature
Chen, Guocai; Zhao, Jieyi; Cohen, Trevor; Tao, Cui; Sun, Jingchun; Xu, Hua; Bernstam, Elmer V.; Lawson, Andrew; Zeng, Jia; Johnson, Amber M.; Holla, Vijaykumar; Bailey, Ann M.; Lara-Guerra, Humberto; Litzenburger, Beate; Meric-Bernstam, Funda; Jim Zheng, W.
2015-01-01
Ambiguous gene names in the biomedical literature are a barrier to accurate information extraction. To overcome this hurdle, we generated Ontology Fingerprints for selected genes that are relevant for personalized cancer therapy. These Ontology Fingerprints were used to evaluate the association between genes and biomedical literature to disambiguate gene names. We obtained 93.6% precision for the test gene set and 80.4% for the area under a receiver-operating characteristics curve for gene and article association. The core algorithm was implemented using a graphics processing unit-based MapReduce framework to handle big data and to improve performance. We conclude that Ontology Fingerprints can help disambiguate gene names mentioned in text and analyse the association between genes and articles. Database URL: http://www.ontologyfingerprint.org PMID:25858285
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Reflective random indexing for semi-automatic indexing of the biomedical literature.
Vasuki, Vidya; Cohen, Trevor
2010-10-01
The rapid growth of biomedical literature is evident in the increasing size of the MEDLINE research database. Medical Subject Headings (MeSH), a controlled set of keywords, are used to index all the citations contained in the database to facilitate search and retrieval. This volume of citations calls for efficient tools to assist indexers at the US National Library of Medicine (NLM). Currently, the Medical Text Indexer (MTI) system provides assistance by recommending MeSH terms based on the title and abstract of an article using a combination of distributional and vocabulary-based methods. In this paper, we evaluate a novel approach toward indexer assistance by using nearest neighbor classification in combination with Reflective Random Indexing (RRI), a scalable alternative to the established methods of distributional semantics. On a test set provided by the NLM, our approach significantly outperforms the MTI system, suggesting that the RRI approach would make a useful addition to the current methodologies.
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Ji, Yanqing; Ying, Hao; Tran, John; Dews, Peter; Massanari, R Michael
2016-07-19
Finding highly relevant articles from biomedical databases is challenging not only because it is often difficult to accurately express a user's underlying intention through keywords but also because a keyword-based query normally returns a long list of hits with many citations being unwanted by the user. This paper proposes a novel biomedical literature search system, called BiomedSearch, which supports complex queries and relevance feedback. The system employed association mining techniques to build a k-profile representing a user's relevance feedback. More specifically, we developed a weighted interest measure and an association mining algorithm to find the strength of association between a query and each concept in the article(s) selected by the user as feedback. The top concepts were utilized to form a k-profile used for the next-round search. BiomedSearch relies on Unified Medical Language System (UMLS) knowledge sources to map text files to standard biomedical concepts. It was designed to support queries with any levels of complexity. A prototype of BiomedSearch software was made and it was preliminarily evaluated using the Genomics data from TREC (Text Retrieval Conference) 2006 Genomics Track. Initial experiment results indicated that BiomedSearch increased the mean average precision (MAP) for a set of queries. With UMLS and association mining techniques, BiomedSearch can effectively utilize users' relevance feedback to improve the performance of biomedical literature search.
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Sagace: A web-based search engine for biomedical databases in Japan
2012-01-01
Background In the big data era, biomedical research continues to generate a large amount of data, and the generated information is often stored in a database and made publicly available. Although combining data from multiple databases should accelerate further studies, the current number of life sciences databases is too large to grasp features and contents of each database. Findings We have developed Sagace, a web-based search engine that enables users to retrieve information from a range of biological databases (such as gene expression profiles and proteomics data) and biological resource banks (such as mouse models of disease and cell lines). With Sagace, users can search more than 300 databases in Japan. Sagace offers features tailored to biomedical research, including manually tuned ranking, a faceted navigation to refine search results, and rich snippets constructed with retrieved metadata for each database entry. Conclusions Sagace will be valuable for experts who are involved in biomedical research and drug development in both academia and industry. Sagace is freely available at http://sagace.nibio.go.jp/en/. PMID:23110816
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NASA Astrophysics Data System (ADS)
Antani, Sameer K.; Natarajan, Mukil; Long, Jonathan L.; Long, L. Rodney; Thoma, George R.
2005-04-01
The article describes the status of our ongoing R&D at the U.S. National Library of Medicine (NLM) towards the development of an advanced multimedia database biomedical information system that supports content-based image retrieval (CBIR). NLM maintains a collection of 17,000 digitized spinal X-rays along with text survey data from the Second National Health and Nutritional Examination Survey (NHANES II). These data serve as a rich data source for epidemiologists and researchers of osteoarthritis and musculoskeletal diseases. It is currently possible to access these through text keyword queries using our Web-based Medical Information Retrieval System (WebMIRS). CBIR methods developed specifically for biomedical images could offer direct visual searching of these images by means of example image or user sketch. We are building a system which supports hybrid queries that have text and image-content components. R&D goals include developing algorithms for robust image segmentation for localizing and identifying relevant anatomy, labeling the segmented anatomy based on its pathology, developing suitable indexing and similarity matching methods for images and image features, and associating the survey text information for query and retrieval along with the image data. Some highlights of the system developed in MATLAB and Java are: use of a networked or local centralized database for text and image data; flexibility to incorporate new research work; provides a means to control access to system components under development; and use of XML for structured reporting. The article details the design, features, and algorithms in this third revision of this prototype system, CBIR3.
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Text mining for traditional Chinese medical knowledge discovery: a survey.
Zhou, Xuezhong; Peng, Yonghong; Liu, Baoyan
2010-08-01
Extracting meaningful information and knowledge from free text is the subject of considerable research interest in the machine learning and data mining fields. Text data mining (or text mining) has become one of the most active research sub-fields in data mining. Significant developments in the area of biomedical text mining during the past years have demonstrated its great promise for supporting scientists in developing novel hypotheses and new knowledge from the biomedical literature. Traditional Chinese medicine (TCM) provides a distinct methodology with which to view human life. It is one of the most complete and distinguished traditional medicines with a history of several thousand years of studying and practicing the diagnosis and treatment of human disease. It has been shown that the TCM knowledge obtained from clinical practice has become a significant complementary source of information for modern biomedical sciences. TCM literature obtained from the historical period and from modern clinical studies has recently been transformed into digital data in the form of relational databases or text documents, which provide an effective platform for information sharing and retrieval. This motivates and facilitates research and development into knowledge discovery approaches and to modernize TCM. In order to contribute to this still growing field, this paper presents (1) a comparative introduction to TCM and modern biomedicine, (2) a survey of the related information sources of TCM, (3) a review and discussion of the state of the art and the development of text mining techniques with applications to TCM, (4) a discussion of the research issues around TCM text mining and its future directions. Copyright 2010 Elsevier Inc. All rights reserved.
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Islamaj Doğan, Rezarta; Comeau, Donald C; Yeganova, Lana; Wilbur, W John
2014-01-01
BioC is a recently created XML format to share text data and annotations, and an accompanying input/output library to promote interoperability of data and tools for natural language processing of biomedical text. This article reports the use of BioC to address a common challenge in processing biomedical text information-that of frequent entity name abbreviation. We selected three different abbreviation definition identification modules, and used the publicly available BioC code to convert these independent modules into BioC-compatible components that interact seamlessly with BioC-formatted data, and other BioC-compatible modules. In addition, we consider four manually annotated corpora of abbreviations in biomedical text: the Ab3P corpus of 1250 PubMed abstracts, the BIOADI corpus of 1201 PubMed abstracts, the old MEDSTRACT corpus of 199 PubMed(®) citations and the Schwartz and Hearst corpus of 1000 PubMed abstracts. Annotations in these corpora have been re-evaluated by four annotators and their consistency and quality levels have been improved. We converted them to BioC-format and described the representation of the annotations. These corpora are used to measure the three abbreviation-finding algorithms and the results are given. The BioC-compatible modules, when compared with their original form, have no difference in their efficiency, running time or any other comparable aspects. They can be conveniently used as a common pre-processing step for larger multi-layered text-mining endeavors. Database URL: Code and data are available for download at the BioC site: http://bioc.sourceforge.net. Published by Oxford University Press 2014. This work is written by US Government employees and is in the public domain in the US.
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Using Ontology Fingerprints to disambiguate gene name entities in the biomedical literature.
Chen, Guocai; Zhao, Jieyi; Cohen, Trevor; Tao, Cui; Sun, Jingchun; Xu, Hua; Bernstam, Elmer V; Lawson, Andrew; Zeng, Jia; Johnson, Amber M; Holla, Vijaykumar; Bailey, Ann M; Lara-Guerra, Humberto; Litzenburger, Beate; Meric-Bernstam, Funda; Jim Zheng, W
2015-01-01
Ambiguous gene names in the biomedical literature are a barrier to accurate information extraction. To overcome this hurdle, we generated Ontology Fingerprints for selected genes that are relevant for personalized cancer therapy. These Ontology Fingerprints were used to evaluate the association between genes and biomedical literature to disambiguate gene names. We obtained 93.6% precision for the test gene set and 80.4% for the area under a receiver-operating characteristics curve for gene and article association. The core algorithm was implemented using a graphics processing unit-based MapReduce framework to handle big data and to improve performance. We conclude that Ontology Fingerprints can help disambiguate gene names mentioned in text and analyse the association between genes and articles. Database URL: http://www.ontologyfingerprint.org © The Author(s) 2015. Published by Oxford University Press.
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SWS: accessing SRS sites contents through Web Services.
Romano, Paolo; Marra, Domenico
2008-03-26
Web Services and Workflow Management Systems can support creation and deployment of network systems, able to automate data analysis and retrieval processes in biomedical research. Web Services have been implemented at bioinformatics centres and workflow systems have been proposed for biological data analysis. New databanks are often developed by taking into account these technologies, but many existing databases do not allow a programmatic access. Only a fraction of available databanks can thus be queried through programmatic interfaces. SRS is a well know indexing and search engine for biomedical databanks offering public access to many databanks and analysis tools. Unfortunately, these data are not easily and efficiently accessible through Web Services. We have developed 'SRS by WS' (SWS), a tool that makes information available in SRS sites accessible through Web Services. Information on known sites is maintained in a database, srsdb. SWS consists in a suite of WS that can query both srsdb, for information on sites and databases, and SRS sites. SWS returns results in a text-only format and can be accessed through a WSDL compliant client. SWS enables interoperability between workflow systems and SRS implementations, by also managing access to alternative sites, in order to cope with network and maintenance problems, and selecting the most up-to-date among available systems. Development and implementation of Web Services, allowing to make a programmatic access to an exhaustive set of biomedical databases can significantly improve automation of in-silico analysis. SWS supports this activity by making biological databanks that are managed in public SRS sites available through a programmatic interface.
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Integrating text mining into the MGI biocuration workflow
Dowell, K.G.; McAndrews-Hill, M.S.; Hill, D.P.; Drabkin, H.J.; Blake, J.A.
2009-01-01
A major challenge for functional and comparative genomics resource development is the extraction of data from the biomedical literature. Although text mining for biological data is an active research field, few applications have been integrated into production literature curation systems such as those of the model organism databases (MODs). Not only are most available biological natural language (bioNLP) and information retrieval and extraction solutions difficult to adapt to existing MOD curation workflows, but many also have high error rates or are unable to process documents available in those formats preferred by scientific journals. In September 2008, Mouse Genome Informatics (MGI) at The Jackson Laboratory initiated a search for dictionary-based text mining tools that we could integrate into our biocuration workflow. MGI has rigorous document triage and annotation procedures designed to identify appropriate articles about mouse genetics and genome biology. We currently screen ∼1000 journal articles a month for Gene Ontology terms, gene mapping, gene expression, phenotype data and other key biological information. Although we do not foresee that curation tasks will ever be fully automated, we are eager to implement named entity recognition (NER) tools for gene tagging that can help streamline our curation workflow and simplify gene indexing tasks within the MGI system. Gene indexing is an MGI-specific curation function that involves identifying which mouse genes are being studied in an article, then associating the appropriate gene symbols with the article reference number in the MGI database. Here, we discuss our search process, performance metrics and success criteria, and how we identified a short list of potential text mining tools for further evaluation. We provide an overview of our pilot projects with NCBO's Open Biomedical Annotator and Fraunhofer SCAI's ProMiner. In doing so, we prove the potential for the further incorporation of semi-automated processes into the curation of the biomedical literature. PMID:20157492
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Integrating text mining into the MGI biocuration workflow.
Dowell, K G; McAndrews-Hill, M S; Hill, D P; Drabkin, H J; Blake, J A
2009-01-01
A major challenge for functional and comparative genomics resource development is the extraction of data from the biomedical literature. Although text mining for biological data is an active research field, few applications have been integrated into production literature curation systems such as those of the model organism databases (MODs). Not only are most available biological natural language (bioNLP) and information retrieval and extraction solutions difficult to adapt to existing MOD curation workflows, but many also have high error rates or are unable to process documents available in those formats preferred by scientific journals.In September 2008, Mouse Genome Informatics (MGI) at The Jackson Laboratory initiated a search for dictionary-based text mining tools that we could integrate into our biocuration workflow. MGI has rigorous document triage and annotation procedures designed to identify appropriate articles about mouse genetics and genome biology. We currently screen approximately 1000 journal articles a month for Gene Ontology terms, gene mapping, gene expression, phenotype data and other key biological information. Although we do not foresee that curation tasks will ever be fully automated, we are eager to implement named entity recognition (NER) tools for gene tagging that can help streamline our curation workflow and simplify gene indexing tasks within the MGI system. Gene indexing is an MGI-specific curation function that involves identifying which mouse genes are being studied in an article, then associating the appropriate gene symbols with the article reference number in the MGI database.Here, we discuss our search process, performance metrics and success criteria, and how we identified a short list of potential text mining tools for further evaluation. We provide an overview of our pilot projects with NCBO's Open Biomedical Annotator and Fraunhofer SCAI's ProMiner. In doing so, we prove the potential for the further incorporation of semi-automated processes into the curation of the biomedical literature.
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BioC implementations in Go, Perl, Python and Ruby.
Liu, Wanli; Islamaj Doğan, Rezarta; Kwon, Dongseop; Marques, Hernani; Rinaldi, Fabio; Wilbur, W John; Comeau, Donald C
2014-01-01
As part of a communitywide effort for evaluating text mining and information extraction systems applied to the biomedical domain, BioC is focused on the goal of interoperability, currently a major barrier to wide-scale adoption of text mining tools. BioC is a simple XML format, specified by DTD, for exchanging data for biomedical natural language processing. With initial implementations in C++ and Java, BioC provides libraries of code for reading and writing BioC text documents and annotations. We extend BioC to Perl, Python, Go and Ruby. We used SWIG to extend the C++ implementation for Perl and one Python implementation. A second Python implementation and the Ruby implementation use native data structures and libraries. BioC is also implemented in the Google language Go. BioC modules are functional in all of these languages, which can facilitate text mining tasks. BioC implementations are freely available through the BioC site: http://bioc.sourceforge.net. Database URL: http://bioc.sourceforge.net/ Published by Oxford University Press 2014. This work is written by US Government employees and is in the public domain in the US.
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Anatomical entity mention recognition at literature scale
Pyysalo, Sampo; Ananiadou, Sophia
2014-01-01
Motivation: Anatomical entities ranging from subcellular structures to organ systems are central to biomedical science, and mentions of these entities are essential to understanding the scientific literature. Despite extensive efforts to automatically analyze various aspects of biomedical text, there have been only few studies focusing on anatomical entities, and no dedicated methods for learning to automatically recognize anatomical entity mentions in free-form text have been introduced. Results: We present AnatomyTagger, a machine learning-based system for anatomical entity mention recognition. The system incorporates a broad array of approaches proposed to benefit tagging, including the use of Unified Medical Language System (UMLS)- and Open Biomedical Ontologies (OBO)-based lexical resources, word representations induced from unlabeled text, statistical truecasing and non-local features. We train and evaluate the system on a newly introduced corpus that substantially extends on previously available resources, and apply the resulting tagger to automatically annotate the entire open access scientific domain literature. The resulting analyses have been applied to extend services provided by the Europe PubMed Central literature database. Availability and implementation: All tools and resources introduced in this work are available from http://nactem.ac.uk/anatomytagger. Contact: sophia.ananiadou@manchester.ac.uk Supplementary Information: Supplementary data are available at Bioinformatics online. PMID:24162468
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Stewart, Robert; Soremekun, Mishael; Perera, Gayan; Broadbent, Matthew; Callard, Felicity; Denis, Mike; Hotopf, Matthew; Thornicroft, Graham; Lovestone, Simon
2009-08-12
Case registers have been used extensively in mental health research. Recent developments in electronic medical records, and in computer software to search and analyse these in anonymised format, have the potential to revolutionise this research tool. We describe the development of the South London and Maudsley NHS Foundation Trust (SLAM) Biomedical Research Centre (BRC) Case Register Interactive Search tool (CRIS) which allows research-accessible datasets to be derived from SLAM, the largest provider of secondary mental healthcare in Europe. All clinical data, including free text, are available for analysis in the form of anonymised datasets. Development involved both the building of the system and setting in place the necessary security (with both functional and procedural elements). Descriptive data are presented for the Register database as of October 2008. The database at that point included 122,440 cases, 35,396 of whom were receiving active case management under the Care Programme Approach. In terms of gender and ethnicity, the database was reasonably representative of the source population. The most common assigned primary diagnoses were within the ICD mood disorders (n = 12,756) category followed by schizophrenia and related disorders (8158), substance misuse (7749), neuroses (7105) and organic disorders (6414). The SLAM BRC Case Register represents a 'new generation' of this research design, built on a long-running system of fully electronic clinical records and allowing in-depth secondary analysis of both numerical, string and free text data, whilst preserving anonymity through technical and procedural safeguards.
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The use and misuse of biomedical data: is bigger really better?
Hoffman, Sharona; Podgurski, Andy
2013-01-01
Very large biomedical research databases, containing electronic health records (EHR) and genomic data from millions of patients, have been heralded recently for their potential to accelerate scientific discovery and produce dramatic improvements in medical treatments. Research enabled by these databases may also lead to profound changes in law, regulation, social policy, and even litigation strategies. Yet, is "big data" necessarily better data? This paper makes an original contribution to the legal literature by focusing on what can go wrong in the process of biomedical database research and what precautions are necessary to avoid critical mistakes. We address three main reasons for approaching such research with care and being cautious in relying on its outcomes for purposes of public policy or litigation. First, the data contained in biomedical databases is surprisingly likely to be incorrect or incomplete. Second, systematic biases, arising from both the nature of the data and the preconceptions of investigators, are serious threats to the validity of research results, especially in answering causal questions. Third, data mining of biomedical databases makes it easier for individuals with political, social, or economic agendas to generate ostensibly scientific but misleading research findings for the purpose of manipulating public opinion and swaying policymakers. In short, this paper sheds much-needed light on the problems of credulous and uninformed acceptance of research results derived from biomedical databases. An understanding of the pitfalls of big data analysis is of critical importance to anyone who will rely on or dispute its outcomes, including lawyers, policymakers, and the public at large. The Article also recommends technical, methodological, and educational interventions to combat the dangers of database errors and abuses.
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Cataloging the biomedical world of pain through semi-automated curation of molecular interactions
Jamieson, Daniel G.; Roberts, Phoebe M.; Robertson, David L.; Sidders, Ben; Nenadic, Goran
2013-01-01
The vast collection of biomedical literature and its continued expansion has presented a number of challenges to researchers who require structured findings to stay abreast of and analyze molecular mechanisms relevant to their domain of interest. By structuring literature content into topic-specific machine-readable databases, the aggregate data from multiple articles can be used to infer trends that can be compared and contrasted with similar findings from topic-independent resources. Our study presents a generalized procedure for semi-automatically creating a custom topic-specific molecular interaction database through the use of text mining to assist manual curation. We apply the procedure to capture molecular events that underlie ‘pain’, a complex phenomenon with a large societal burden and unmet medical need. We describe how existing text mining solutions are used to build a pain-specific corpus, extract molecular events from it, add context to the extracted events and assess their relevance. The pain-specific corpus contains 765 692 documents from Medline and PubMed Central, from which we extracted 356 499 unique normalized molecular events, with 261 438 single protein events and 93 271 molecular interactions supplied by BioContext. Event chains are annotated with negation, speculation, anatomy, Gene Ontology terms, mutations, pain and disease relevance, which collectively provide detailed insight into how that event chain is associated with pain. The extracted relations are visualized in a wiki platform (wiki-pain.org) that enables efficient manual curation and exploration of the molecular mechanisms that underlie pain. Curation of 1500 grouped event chains ranked by pain relevance revealed 613 accurately extracted unique molecular interactions that in the future can be used to study the underlying mechanisms involved in pain. Our approach demonstrates that combining existing text mining tools with domain-specific terms and wiki-based visualization can facilitate rapid curation of molecular interactions to create a custom database. Database URL: ••• PMID:23707966
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A Survey of Bioinformatics Database and Software Usage through Mining the Literature.
Duck, Geraint; Nenadic, Goran; Filannino, Michele; Brass, Andy; Robertson, David L; Stevens, Robert
2016-01-01
Computer-based resources are central to much, if not most, biological and medical research. However, while there is an ever expanding choice of bioinformatics resources to use, described within the biomedical literature, little work to date has provided an evaluation of the full range of availability or levels of usage of database and software resources. Here we use text mining to process the PubMed Central full-text corpus, identifying mentions of databases or software within the scientific literature. We provide an audit of the resources contained within the biomedical literature, and a comparison of their relative usage, both over time and between the sub-disciplines of bioinformatics, biology and medicine. We find that trends in resource usage differs between these domains. The bioinformatics literature emphasises novel resource development, while database and software usage within biology and medicine is more stable and conservative. Many resources are only mentioned in the bioinformatics literature, with a relatively small number making it out into general biology, and fewer still into the medical literature. In addition, many resources are seeing a steady decline in their usage (e.g., BLAST, SWISS-PROT), though some are instead seeing rapid growth (e.g., the GO, R). We find a striking imbalance in resource usage with the top 5% of resource names (133 names) accounting for 47% of total usage, and over 70% of resources extracted being only mentioned once each. While these results highlight the dynamic and creative nature of bioinformatics research they raise questions about software reuse, choice and the sharing of bioinformatics practice. Is it acceptable that so many resources are apparently never reused? Finally, our work is a step towards automated extraction of scientific method from text. We make the dataset generated by our study available under the CC0 license here: http://dx.doi.org/10.6084/m9.figshare.1281371.
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Cataloging the biomedical world of pain through semi-automated curation of molecular interactions.
Jamieson, Daniel G; Roberts, Phoebe M; Robertson, David L; Sidders, Ben; Nenadic, Goran
2013-01-01
The vast collection of biomedical literature and its continued expansion has presented a number of challenges to researchers who require structured findings to stay abreast of and analyze molecular mechanisms relevant to their domain of interest. By structuring literature content into topic-specific machine-readable databases, the aggregate data from multiple articles can be used to infer trends that can be compared and contrasted with similar findings from topic-independent resources. Our study presents a generalized procedure for semi-automatically creating a custom topic-specific molecular interaction database through the use of text mining to assist manual curation. We apply the procedure to capture molecular events that underlie 'pain', a complex phenomenon with a large societal burden and unmet medical need. We describe how existing text mining solutions are used to build a pain-specific corpus, extract molecular events from it, add context to the extracted events and assess their relevance. The pain-specific corpus contains 765 692 documents from Medline and PubMed Central, from which we extracted 356 499 unique normalized molecular events, with 261 438 single protein events and 93 271 molecular interactions supplied by BioContext. Event chains are annotated with negation, speculation, anatomy, Gene Ontology terms, mutations, pain and disease relevance, which collectively provide detailed insight into how that event chain is associated with pain. The extracted relations are visualized in a wiki platform (wiki-pain.org) that enables efficient manual curation and exploration of the molecular mechanisms that underlie pain. Curation of 1500 grouped event chains ranked by pain relevance revealed 613 accurately extracted unique molecular interactions that in the future can be used to study the underlying mechanisms involved in pain. Our approach demonstrates that combining existing text mining tools with domain-specific terms and wiki-based visualization can facilitate rapid curation of molecular interactions to create a custom database. Database URL: •••
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Dankar, Fida K; Ptitsyn, Andrey; Dankar, Samar K
2018-04-10
Contemporary biomedical databases include a wide range of information types from various observational and instrumental sources. Among the most important features that unite biomedical databases across the field are high volume of information and high potential to cause damage through data corruption, loss of performance, and loss of patient privacy. Thus, issues of data governance and privacy protection are essential for the construction of data depositories for biomedical research and healthcare. In this paper, we discuss various challenges of data governance in the context of population genome projects. The various challenges along with best practices and current research efforts are discussed through the steps of data collection, storage, sharing, analysis, and knowledge dissemination.
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State of reporting of primary biomedical research: a scoping review protocol
Mbuagbaw, Lawrence; Samaan, Zainab; Jin, Yanling; Nwosu, Ikunna; Levine, Mitchell A H; Adachi, Jonathan D; Thabane, Lehana
2017-01-01
Introduction Incomplete or inconsistent reporting remains a major concern in the biomedical literature. Incomplete or inconsistent reporting may yield the published findings unreliable, irreproducible or sometimes misleading. In this study based on evidence from systematic reviews and surveys that have evaluated the reporting issues in primary biomedical studies, we aim to conduct a scoping review with focuses on (1) the state-of-the-art extent of adherence to the emerging reporting guidelines in primary biomedical research, (2) the inconsistency between protocols or registrations and full reports and (3) the disagreement between abstracts and full-text articles. Methods and analyses We will use a comprehensive search strategy to retrieve all available and eligible systematic reviews and surveys in the literature. We will search the following electronic databases: Web of Science, Excerpta Medica Database (EMBASE), MEDLINE and Cumulative Index to Nursing and Allied Health Literature (CINAHL). Our outcomes are levels of adherence to reporting guidelines, levels of consistency between protocols or registrations and full reports and the agreement between abstracts and full reports, all of which will be expressed as percentages, quality scores or categorised rating (such as high, medium and low). No pooled analyses will be performed quantitatively given the heterogeneity of the included systematic reviews and surveys. Likewise, factors associated with improved completeness and consistency of reporting will be summarised qualitatively. The quality of the included systematic reviews will be evaluated using AMSTAR (a measurement tool to assess systematic reviews). Ethics and dissemination All findings will be published in peer-reviewed journals and relevant conferences. These results may advance our understanding of the extent of incomplete and inconsistent reporting, factors related to improved completeness and consistency of reporting and potential recommendations for various stakeholders in the biomedical community. PMID:28360252
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A scoping review of competencies for scientific editors of biomedical journals.
Galipeau, James; Barbour, Virginia; Baskin, Patricia; Bell-Syer, Sally; Cobey, Kelly; Cumpston, Miranda; Deeks, Jon; Garner, Paul; MacLehose, Harriet; Shamseer, Larissa; Straus, Sharon; Tugwell, Peter; Wager, Elizabeth; Winker, Margaret; Moher, David
2016-02-02
Biomedical journals are the main route for disseminating the results of health-related research. Despite this, their editors operate largely without formal training or certification. To our knowledge, no body of literature systematically identifying core competencies for scientific editors of biomedical journals exists. Therefore, we aimed to conduct a scoping review to determine what is known on the competency requirements for scientific editors of biomedical journals. We searched the MEDLINE®, Cochrane Library, Embase®, CINAHL, PsycINFO, and ERIC databases (from inception to November 2014) and conducted a grey literature search for research and non-research articles with competency-related statements (i.e. competencies, knowledge, skills, behaviors, and tasks) pertaining to the role of scientific editors of peer-reviewed health-related journals. We also conducted an environmental scan, searched the results of a previous environmental scan, and searched the websites of existing networks, major biomedical journal publishers, and organizations that offer resources for editors. A total of 225 full-text publications were included, 25 of which were research articles. We extracted a total of 1,566 statements possibly related to core competencies for scientific editors of biomedical journals from these publications. We then collated overlapping or duplicate statements which produced a list of 203 unique statements. Finally, we grouped these statements into seven emergent themes: (1) dealing with authors, (2) dealing with peer reviewers, (3) journal publishing, (4) journal promotion, (5) editing, (6) ethics and integrity, and (7) qualities and characteristics of editors. To our knowledge, this scoping review is the first attempt to systematically identify possible competencies of editors. Limitations are that (1) we may not have captured all aspects of a biomedical editor's work in our searches, (2) removing redundant and overlapping items may have led to the elimination of some nuances between items, (3) restricting to certain databases, and only French and English publications, may have excluded relevant publications, and (4) some statements may not necessarily be competencies. This scoping review is the first step of a program to develop a minimum set of core competencies for scientific editors of biomedical journals which will be followed by a training needs assessment, a Delphi exercise, and a consensus meeting.
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DrugQuest - a text mining workflow for drug association discovery.
Papanikolaou, Nikolas; Pavlopoulos, Georgios A; Theodosiou, Theodosios; Vizirianakis, Ioannis S; Iliopoulos, Ioannis
2016-06-06
Text mining and data integration methods are gaining ground in the field of health sciences due to the exponential growth of bio-medical literature and information stored in biological databases. While such methods mostly try to extract bioentity associations from PubMed, very few of them are dedicated in mining other types of repositories such as chemical databases. Herein, we apply a text mining approach on the DrugBank database in order to explore drug associations based on the DrugBank "Description", "Indication", "Pharmacodynamics" and "Mechanism of Action" text fields. We apply Name Entity Recognition (NER) techniques on these fields to identify chemicals, proteins, genes, pathways, diseases, and we utilize the TextQuest algorithm to find additional biologically significant words. Using a plethora of similarity and partitional clustering techniques, we group the DrugBank records based on their common terms and investigate possible scenarios why these records are clustered together. Different views such as clustered chemicals based on their textual information, tag clouds consisting of Significant Terms along with the terms that were used for clustering are delivered to the user through a user-friendly web interface. DrugQuest is a text mining tool for knowledge discovery: it is designed to cluster DrugBank records based on text attributes in order to find new associations between drugs. The service is freely available at http://bioinformatics.med.uoc.gr/drugquest .
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PPInterFinder--a mining tool for extracting causal relations on human proteins from literature.
Raja, Kalpana; Subramani, Suresh; Natarajan, Jeyakumar
2013-01-01
One of the most common and challenging problem in biomedical text mining is to mine protein-protein interactions (PPIs) from MEDLINE abstracts and full-text research articles because PPIs play a major role in understanding the various biological processes and the impact of proteins in diseases. We implemented, PPInterFinder--a web-based text mining tool to extract human PPIs from biomedical literature. PPInterFinder uses relation keyword co-occurrences with protein names to extract information on PPIs from MEDLINE abstracts and consists of three phases. First, it identifies the relation keyword using a parser with Tregex and a relation keyword dictionary. Next, it automatically identifies the candidate PPI pairs with a set of rules related to PPI recognition. Finally, it extracts the relations by matching the sentence with a set of 11 specific patterns based on the syntactic nature of PPI pair. We find that PPInterFinder is capable of predicting PPIs with the accuracy of 66.05% on AIMED corpus and outperforms most of the existing systems. DATABASE URL: http://www.biomining-bu.in/ppinterfinder/
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PPInterFinder—a mining tool for extracting causal relations on human proteins from literature
Raja, Kalpana; Subramani, Suresh; Natarajan, Jeyakumar
2013-01-01
One of the most common and challenging problem in biomedical text mining is to mine protein–protein interactions (PPIs) from MEDLINE abstracts and full-text research articles because PPIs play a major role in understanding the various biological processes and the impact of proteins in diseases. We implemented, PPInterFinder—a web-based text mining tool to extract human PPIs from biomedical literature. PPInterFinder uses relation keyword co-occurrences with protein names to extract information on PPIs from MEDLINE abstracts and consists of three phases. First, it identifies the relation keyword using a parser with Tregex and a relation keyword dictionary. Next, it automatically identifies the candidate PPI pairs with a set of rules related to PPI recognition. Finally, it extracts the relations by matching the sentence with a set of 11 specific patterns based on the syntactic nature of PPI pair. We find that PPInterFinder is capable of predicting PPIs with the accuracy of 66.05% on AIMED corpus and outperforms most of the existing systems. Database URL: http://www.biomining-bu.in/ppinterfinder/ PMID:23325628
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Text mining for the biocuration workflow
Hirschman, Lynette; Burns, Gully A. P. C; Krallinger, Martin; Arighi, Cecilia; Cohen, K. Bretonnel; Valencia, Alfonso; Wu, Cathy H.; Chatr-Aryamontri, Andrew; Dowell, Karen G.; Huala, Eva; Lourenço, Anália; Nash, Robert; Veuthey, Anne-Lise; Wiegers, Thomas; Winter, Andrew G.
2012-01-01
Molecular biology has become heavily dependent on biological knowledge encoded in expert curated biological databases. As the volume of biological literature increases, biocurators need help in keeping up with the literature; (semi-) automated aids for biocuration would seem to be an ideal application for natural language processing and text mining. However, to date, there have been few documented successes for improving biocuration throughput using text mining. Our initial investigations took place for the workshop on ‘Text Mining for the BioCuration Workflow’ at the third International Biocuration Conference (Berlin, 2009). We interviewed biocurators to obtain workflows from eight biological databases. This initial study revealed high-level commonalities, including (i) selection of documents for curation; (ii) indexing of documents with biologically relevant entities (e.g. genes); and (iii) detailed curation of specific relations (e.g. interactions); however, the detailed workflows also showed many variabilities. Following the workshop, we conducted a survey of biocurators. The survey identified biocurator priorities, including the handling of full text indexed with biological entities and support for the identification and prioritization of documents for curation. It also indicated that two-thirds of the biocuration teams had experimented with text mining and almost half were using text mining at that time. Analysis of our interviews and survey provide a set of requirements for the integration of text mining into the biocuration workflow. These can guide the identification of common needs across curated databases and encourage joint experimentation involving biocurators, text mining developers and the larger biomedical research community. PMID:22513129
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Text mining for the biocuration workflow.
Hirschman, Lynette; Burns, Gully A P C; Krallinger, Martin; Arighi, Cecilia; Cohen, K Bretonnel; Valencia, Alfonso; Wu, Cathy H; Chatr-Aryamontri, Andrew; Dowell, Karen G; Huala, Eva; Lourenço, Anália; Nash, Robert; Veuthey, Anne-Lise; Wiegers, Thomas; Winter, Andrew G
2012-01-01
Molecular biology has become heavily dependent on biological knowledge encoded in expert curated biological databases. As the volume of biological literature increases, biocurators need help in keeping up with the literature; (semi-) automated aids for biocuration would seem to be an ideal application for natural language processing and text mining. However, to date, there have been few documented successes for improving biocuration throughput using text mining. Our initial investigations took place for the workshop on 'Text Mining for the BioCuration Workflow' at the third International Biocuration Conference (Berlin, 2009). We interviewed biocurators to obtain workflows from eight biological databases. This initial study revealed high-level commonalities, including (i) selection of documents for curation; (ii) indexing of documents with biologically relevant entities (e.g. genes); and (iii) detailed curation of specific relations (e.g. interactions); however, the detailed workflows also showed many variabilities. Following the workshop, we conducted a survey of biocurators. The survey identified biocurator priorities, including the handling of full text indexed with biological entities and support for the identification and prioritization of documents for curation. It also indicated that two-thirds of the biocuration teams had experimented with text mining and almost half were using text mining at that time. Analysis of our interviews and survey provide a set of requirements for the integration of text mining into the biocuration workflow. These can guide the identification of common needs across curated databases and encourage joint experimentation involving biocurators, text mining developers and the larger biomedical research community.
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Stewart, Robert; Soremekun, Mishael; Perera, Gayan; Broadbent, Matthew; Callard, Felicity; Denis, Mike; Hotopf, Matthew; Thornicroft, Graham; Lovestone, Simon
2009-01-01
Background Case registers have been used extensively in mental health research. Recent developments in electronic medical records, and in computer software to search and analyse these in anonymised format, have the potential to revolutionise this research tool. Methods We describe the development of the South London and Maudsley NHS Foundation Trust (SLAM) Biomedical Research Centre (BRC) Case Register Interactive Search tool (CRIS) which allows research-accessible datasets to be derived from SLAM, the largest provider of secondary mental healthcare in Europe. All clinical data, including free text, are available for analysis in the form of anonymised datasets. Development involved both the building of the system and setting in place the necessary security (with both functional and procedural elements). Results Descriptive data are presented for the Register database as of October 2008. The database at that point included 122,440 cases, 35,396 of whom were receiving active case management under the Care Programme Approach. In terms of gender and ethnicity, the database was reasonably representative of the source population. The most common assigned primary diagnoses were within the ICD mood disorders (n = 12,756) category followed by schizophrenia and related disorders (8158), substance misuse (7749), neuroses (7105) and organic disorders (6414). Conclusion The SLAM BRC Case Register represents a 'new generation' of this research design, built on a long-running system of fully electronic clinical records and allowing in-depth secondary analysis of both numerical, string and free text data, whilst preserving anonymity through technical and procedural safeguards. PMID:19674459
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[Scientometrics and bibliometrics of biomedical engineering periodicals and papers].
Zhao, Ping; Xu, Ping; Li, Bingyan; Wang, Zhengrong
2003-09-01
This investigation was made to reveal the current status, research trend and research level of biomedical engineering in Chinese mainland by means of scientometrics and to assess the quality of the four domestic publications by bibliometrics. We identified all articles of four related publications by searching Chinese and foreign databases from 1997 to 2001. All articles collected or cited by these databases were searched and statistically analyzed for finding out the relevant distributions, including databases, years, authors, institutions, subject headings and subheadings. The source of sustentation funds and the related articles were analyzed too. The results showed that two journals were cited by two foreign databases and five Chinese databases simultaneously. The output of Journal of Biomedical Engineering was the highest. Its quantity of original papers cited by EI, CA and the totality of papers sponsored by funds were higher than those of the others, but the quantity and percentage per year of biomedical articles cited by EI were decreased in all. Inland core authors and institutions had come into being in the field of biomedical engineering. Their research topics were mainly concentrated on ten subject headings which included biocompatible materials, computer-assisted signal processing, electrocardiography, computer-assisted image processing, biomechanics, algorithms, electroencephalography, automatic data processing, mechanical stress, hemodynamics, mathematical computing, microcomputers, theoretical models, etc. The main subheadings were concentrated on instrumentation, physiopathology, diagnosis, therapy, ultrasonography, physiology, analysis, surgery, pathology, method, etc.
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[The biomedical periodicals of Hungarian editions--historical overview].
Berhidi, Anna; Geges, József; Vasas, Lívia
2006-03-12
The majority of Hungarian scientific results are published in international periodicals in foreign languages. Yet the publications in Hungarian scientific periodicals also should not be ignored. This study analyses biomedical periodicals of Hungarian edition from different points of view. Based on different databases a list of titles consisting of 119 items resulted, which contains both the core and the peripheral journals of the biomedical field. These periodicals were analysed empirically, one by one: checking out the titles. 13 of the titles are ceased, among the rest 106 Hungarian scientific journals 10 are published in English language. From the remaining majority of Hungarian language and publishing only a few show up in international databases. Although quarter of the Hungarian biomedical journals meet the requirements, which means they could be represented in international databases, these periodicals are not indexed. 42 biomedical periodicals are available online. Although quarter of these journals come with restricted access. 2/3 of the Hungarian biomedical journals have detailed instructions to authors. These instructions inform the publishing doctors and researchers of the requirements of a biomedical periodical. The increasing number of Hungarian biomedical journals published is welcome news. But it would be important for quality publications which are cited a lot to appear in the Hungarian journals. The more publications are cited, the more journals and authors gain in prestige on home and international level.
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Vieira, A.
2010-01-01
Background: In relation to pharmacognosy, an objective of many ethnobotanical studies is to identify plant species to be further investigated, for example, tested in disease models related to the ethnomedicinal application. To further warrant such testing, research evidence for medicinal applications of these plants (or of their major phytochemical constituents and metabolic derivatives) is typically analyzed in biomedical databases. Methods: As a model of this process, the current report presents novel information regarding traditional anti-inflammation and anti-infection medicinal plant use. This information was obtained from an interview-based ethnobotanical study; and was compared with current biomedical evidence using the Medline® database. Results: Of the 8 anti-infection plant species identified in the ethnobotanical study, 7 have related activities reported in the database; and of the 6 anti-inflammation plants, 4 have related activities in the database. Conclusion: Based on novel and complimentary results from the ethnobotanical and biomedical database analyses, it is suggested that some of these plants warrant additional investigation of potential anti-inflammatory or anti-infection activities in related disease models, and also additional studies in other population groups. PMID:21589754
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A Novel Multi-Class Ensemble Model for Classifying Imbalanced Biomedical Datasets
NASA Astrophysics Data System (ADS)
Bikku, Thulasi; Sambasiva Rao, N., Dr; Rao, Akepogu Ananda, Dr
2017-08-01
This paper mainly focuseson developing aHadoop based framework for feature selection and classification models to classify high dimensionality data in heterogeneous biomedical databases. Wide research has been performing in the fields of Machine learning, Big data and Data mining for identifying patterns. The main challenge is extracting useful features generated from diverse biological systems. The proposed model can be used for predicting diseases in various applications and identifying the features relevant to particular diseases. There is an exponential growth of biomedical repositories such as PubMed and Medline, an accurate predictive model is essential for knowledge discovery in Hadoop environment. Extracting key features from unstructured documents often lead to uncertain results due to outliers and missing values. In this paper, we proposed a two phase map-reduce framework with text preprocessor and classification model. In the first phase, mapper based preprocessing method was designed to eliminate irrelevant features, missing values and outliers from the biomedical data. In the second phase, a Map-Reduce based multi-class ensemble decision tree model was designed and implemented in the preprocessed mapper data to improve the true positive rate and computational time. The experimental results on the complex biomedical datasets show that the performance of our proposed Hadoop based multi-class ensemble model significantly outperforms state-of-the-art baselines.
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A New Pivoting and Iterative Text Detection Algorithm for Biomedical Images
DOE Office of Scientific and Technical Information (OSTI.GOV)
Xu, Songhua; Krauthammer, Prof. Michael
2010-01-01
There is interest to expand the reach of literature mining to include the analysis of biomedical images, which often contain a paper's key findings. Examples include recent studies that use Optical Character Recognition (OCR) to extract image text, which is used to boost biomedical image retrieval and classification. Such studies rely on the robust identification of text elements in biomedical images, which is a non-trivial task. In this work, we introduce a new text detection algorithm for biomedical images based on iterative projection histograms. We study the effectiveness of our algorithm by evaluating the performance on a set of manuallymore » labeled random biomedical images, and compare the performance against other state-of-the-art text detection algorithms. We demonstrate that our projection histogram-based text detection approach is well suited for text detection in biomedical images, and that the iterative application of the algorithm boosts performance to an F score of .60. We provide a C++ implementation of our algorithm freely available for academic use.« less
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MEDLINE: the options for health professionals.
Wood, E H
1994-01-01
The bibliographic database MEDLINE, produced by the National Library of Medicine (NLM), is a computerized index to the world's biomedical literature. The database can be searched back to 1966 and contains 6.8 million records. The various means of access are divided, for the purposes of this article, into three categories: logging onto a remote host computer by telephone and modem or by the Internet; subscribing to part or all of the database on compact disc (CD-ROM); and leasing the data on a transport medium such as magnetic tape or CDs for loading on a local host computer. Decisions about which method is preferable in a given situation depend on cost, availability of hardware and software, local expertise, and the size of the intended user population. Trends include increased access to the Internet by health professionals, increased network speed, links from MEDLINE records to full-text databases or online journals, and integration of MEDLINE into wider health information systems.
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Spatial and symbolic queries for 3D image data
NASA Astrophysics Data System (ADS)
Benson, Daniel C.; Zick, Gregory L.
1992-04-01
We present a query system for an object-oriented biomedical imaging database containing 3-D anatomical structures and their corresponding 2-D images. The graphical interface facilitates the formation of spatial queries, nonspatial or symbolic queries, and combined spatial/symbolic queries. A query editor is used for the creation and manipulation of 3-D query objects as volumes, surfaces, lines, and points. Symbolic predicates are formulated through a combination of text fields and multiple choice selections. Query results, which may include images, image contents, composite objects, graphics, and alphanumeric data, are displayed in multiple views. Objects returned by the query may be selected directly within the views for further inspection or modification, or for use as query objects in subsequent queries. Our image database query system provides visual feedback and manipulation of spatial query objects, multiple views of volume data, and the ability to combine spatial and symbolic queries. The system allows for incremental enhancement of existing objects and the addition of new objects and spatial relationships. The query system is designed for databases containing symbolic and spatial data. This paper discuses its application to data acquired in biomedical 3- D image reconstruction, but it is applicable to other areas such as CAD/CAM, geographical information systems, and computer vision.
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The BioGRID interaction database: 2013 update.
Chatr-Aryamontri, Andrew; Breitkreutz, Bobby-Joe; Heinicke, Sven; Boucher, Lorrie; Winter, Andrew; Stark, Chris; Nixon, Julie; Ramage, Lindsay; Kolas, Nadine; O'Donnell, Lara; Reguly, Teresa; Breitkreutz, Ashton; Sellam, Adnane; Chen, Daici; Chang, Christie; Rust, Jennifer; Livstone, Michael; Oughtred, Rose; Dolinski, Kara; Tyers, Mike
2013-01-01
The Biological General Repository for Interaction Datasets (BioGRID: http//thebiogrid.org) is an open access archive of genetic and protein interactions that are curated from the primary biomedical literature for all major model organism species. As of September 2012, BioGRID houses more than 500 000 manually annotated interactions from more than 30 model organisms. BioGRID maintains complete curation coverage of the literature for the budding yeast Saccharomyces cerevisiae, the fission yeast Schizosaccharomyces pombe and the model plant Arabidopsis thaliana. A number of themed curation projects in areas of biomedical importance are also supported. BioGRID has established collaborations and/or shares data records for the annotation of interactions and phenotypes with most major model organism databases, including Saccharomyces Genome Database, PomBase, WormBase, FlyBase and The Arabidopsis Information Resource. BioGRID also actively engages with the text-mining community to benchmark and deploy automated tools to expedite curation workflows. BioGRID data are freely accessible through both a user-defined interactive interface and in batch downloads in a wide variety of formats, including PSI-MI2.5 and tab-delimited files. BioGRID records can also be interrogated and analyzed with a series of new bioinformatics tools, which include a post-translational modification viewer, a graphical viewer, a REST service and a Cytoscape plugin.
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BioCreative V CDR task corpus: a resource for chemical disease relation extraction.
Li, Jiao; Sun, Yueping; Johnson, Robin J; Sciaky, Daniela; Wei, Chih-Hsuan; Leaman, Robert; Davis, Allan Peter; Mattingly, Carolyn J; Wiegers, Thomas C; Lu, Zhiyong
2016-01-01
Community-run, formal evaluations and manually annotated text corpora are critically important for advancing biomedical text-mining research. Recently in BioCreative V, a new challenge was organized for the tasks of disease named entity recognition (DNER) and chemical-induced disease (CID) relation extraction. Given the nature of both tasks, a test collection is required to contain both disease/chemical annotations and relation annotations in the same set of articles. Despite previous efforts in biomedical corpus construction, none was found to be sufficient for the task. Thus, we developed our own corpus called BC5CDR during the challenge by inviting a team of Medical Subject Headings (MeSH) indexers for disease/chemical entity annotation and Comparative Toxicogenomics Database (CTD) curators for CID relation annotation. To ensure high annotation quality and productivity, detailed annotation guidelines and automatic annotation tools were provided. The resulting BC5CDR corpus consists of 1500 PubMed articles with 4409 annotated chemicals, 5818 diseases and 3116 chemical-disease interactions. Each entity annotation includes both the mention text spans and normalized concept identifiers, using MeSH as the controlled vocabulary. To ensure accuracy, the entities were first captured independently by two annotators followed by a consensus annotation: The average inter-annotator agreement (IAA) scores were 87.49% and 96.05% for the disease and chemicals, respectively, in the test set according to the Jaccard similarity coefficient. Our corpus was successfully used for the BioCreative V challenge tasks and should serve as a valuable resource for the text-mining research community.Database URL: http://www.biocreative.org/tasks/biocreative-v/track-3-cdr/. Published by Oxford University Press 2016. This work is written by US Government employees and is in the public domain in the United States.
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Durack, Jeremy C.; Chao, Chih-Chien; Stevenson, Derek; Andriole, Katherine P.; Dev, Parvati
2002-01-01
Medical media collections are growing at a pace that exceeds the value they currently provide as research and educational resources. To address this issue, the Stanford MediaServer was designed to promote innovative multimedia-based application development. The nucleus of the MediaServer platform is a digital media database strategically designed to meet the information needs of many biomedical disciplines. Key features include an intuitive web-based interface for collaboratively populating the media database, flexible creation of media collections for diverse and specialized purposes, and the ability to construct a variety of end-user applications from the same database to support biomedical education and research. PMID:12463820
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Durack, Jeremy C; Chao, Chih-Chien; Stevenson, Derek; Andriole, Katherine P; Dev, Parvati
2002-01-01
Medical media collections are growing at a pace that exceeds the value they currently provide as research and educational resources. To address this issue, the Stanford MediaServer was designed to promote innovative multimedia-based application development. The nucleus of the MediaServer platform is a digital media database strategically designed to meet the information needs of many biomedical disciplines. Key features include an intuitive web-based interface for collaboratively populating the media database, flexible creation of media collections for diverse and specialized purposes, and the ability to construct a variety of end-user applications from the same database to support biomedical education and research.
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Text mining patents for biomedical knowledge.
Rodriguez-Esteban, Raul; Bundschus, Markus
2016-06-01
Biomedical text mining of scientific knowledge bases, such as Medline, has received much attention in recent years. Given that text mining is able to automatically extract biomedical facts that revolve around entities such as genes, proteins, and drugs, from unstructured text sources, it is seen as a major enabler to foster biomedical research and drug discovery. In contrast to the biomedical literature, research into the mining of biomedical patents has not reached the same level of maturity. Here, we review existing work and highlight the associated technical challenges that emerge from automatically extracting facts from patents. We conclude by outlining potential future directions in this domain that could help drive biomedical research and drug discovery. Copyright © 2016 Elsevier Ltd. All rights reserved.
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A new pivoting and iterative text detection algorithm for biomedical images.
Xu, Songhua; Krauthammer, Michael
2010-12-01
There is interest to expand the reach of literature mining to include the analysis of biomedical images, which often contain a paper's key findings. Examples include recent studies that use Optical Character Recognition (OCR) to extract image text, which is used to boost biomedical image retrieval and classification. Such studies rely on the robust identification of text elements in biomedical images, which is a non-trivial task. In this work, we introduce a new text detection algorithm for biomedical images based on iterative projection histograms. We study the effectiveness of our algorithm by evaluating the performance on a set of manually labeled random biomedical images, and compare the performance against other state-of-the-art text detection algorithms. We demonstrate that our projection histogram-based text detection approach is well suited for text detection in biomedical images, and that the iterative application of the algorithm boosts performance to an F score of .60. We provide a C++ implementation of our algorithm freely available for academic use. Copyright © 2010 Elsevier Inc. All rights reserved.
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Mehryary, Farrokh; Kaewphan, Suwisa; Hakala, Kai; Ginter, Filip
2016-01-01
Biomedical event extraction is one of the key tasks in biomedical text mining, supporting various applications such as database curation and hypothesis generation. Several systems, some of which have been applied at a large scale, have been introduced to solve this task. Past studies have shown that the identification of the phrases describing biological processes, also known as trigger detection, is a crucial part of event extraction, and notable overall performance gains can be obtained by solely focusing on this sub-task. In this paper we propose a novel approach for filtering falsely identified triggers from large-scale event databases, thus improving the quality of knowledge extraction. Our method relies on state-of-the-art word embeddings, event statistics gathered from the whole biomedical literature, and both supervised and unsupervised machine learning techniques. We focus on EVEX, an event database covering the whole PubMed and PubMed Central Open Access literature containing more than 40 million extracted events. The top most frequent EVEX trigger words are hierarchically clustered, and the resulting cluster tree is pruned to identify words that can never act as triggers regardless of their context. For rarely occurring trigger words we introduce a supervised approach trained on the combination of trigger word classification produced by the unsupervised clustering method and manual annotation. The method is evaluated on the official test set of BioNLP Shared Task on Event Extraction. The evaluation shows that the method can be used to improve the performance of the state-of-the-art event extraction systems. This successful effort also translates into removing 1,338,075 of potentially incorrect events from EVEX, thus greatly improving the quality of the data. The method is not solely bound to the EVEX resource and can be thus used to improve the quality of any event extraction system or database. The data and source code for this work are available at: http://bionlp-www.utu.fi/trigger-clustering/.
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Bosnian and Herzegovinian medical scientists in PubMed database.
Masic, Izet
2013-01-01
In this paper it is shortly presented PubMed as one of the most important on-line databases of the scientific biomedical literature. Also, the author has analyzed the most cited authors, professors of the medical faculties in Bosnia and Herzegovina, from the published papers in the biomedical journals abstracted and indexed in PubMed.
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A New Pivoting and Iterative Text Detection Algorithm for Biomedical Images
Xu, Songhua; Krauthammer, Michael
2010-01-01
There is interest to expand the reach of literature mining to include the analysis of biomedical images, which often contain a paper’s key findings. Examples include recent studies that use Optical Character Recognition (OCR) to extract image text, which is used to boost biomedical image retrieval and classification. Such studies rely on the robust identification of text elements in biomedical images, which is a non-trivial task. In this work, we introduce a new text detection algorithm for biomedical images based on iterative projection histograms. We study the effectiveness of our algorithm by evaluating the performance on a set of manually labeled random biomedical images, and compare the performance against other state-of-the-art text detection algorithms. In this paper, we demonstrate that a projection histogram-based text detection approach is well suited for text detection in biomedical images, with a performance of F score of .60. The approach performs better than comparable approaches for text detection. Further, we show that the iterative application of the algorithm is boosting overall detection performance. A C++ implementation of our algorithm is freely available through email request for academic use. PMID:20887803
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A new visual navigation system for exploring biomedical Open Educational Resource (OER) videos
Zhao, Baoquan; Xu, Songhua; Lin, Shujin; Luo, Xiaonan; Duan, Lian
2016-01-01
Objective Biomedical videos as open educational resources (OERs) are increasingly proliferating on the Internet. Unfortunately, seeking personally valuable content from among the vast corpus of quality yet diverse OER videos is nontrivial due to limitations of today’s keyword- and content-based video retrieval techniques. To address this need, this study introduces a novel visual navigation system that facilitates users’ information seeking from biomedical OER videos in mass quantity by interactively offering visual and textual navigational clues that are both semantically revealing and user-friendly. Materials and Methods The authors collected and processed around 25 000 YouTube videos, which collectively last for a total length of about 4000 h, in the broad field of biomedical sciences for our experiment. For each video, its semantic clues are first extracted automatically through computationally analyzing audio and visual signals, as well as text either accompanying or embedded in the video. These extracted clues are subsequently stored in a metadata database and indexed by a high-performance text search engine. During the online retrieval stage, the system renders video search results as dynamic web pages using a JavaScript library that allows users to interactively and intuitively explore video content both efficiently and effectively. Results The authors produced a prototype implementation of the proposed system, which is publicly accessible at https://patentq.njit.edu/oer. To examine the overall advantage of the proposed system for exploring biomedical OER videos, the authors further conducted a user study of a modest scale. The study results encouragingly demonstrate the functional effectiveness and user-friendliness of the new system for facilitating information seeking from and content exploration among massive biomedical OER videos. Conclusion Using the proposed tool, users can efficiently and effectively find videos of interest, precisely locate video segments delivering personally valuable information, as well as intuitively and conveniently preview essential content of a single or a collection of videos. PMID:26335986
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Corwin, John; Silberschatz, Avi; Miller, Perry L; Marenco, Luis
2007-01-01
Data sparsity and schema evolution issues affecting clinical informatics and bioinformatics communities have led to the adoption of vertical or object-attribute-value-based database schemas to overcome limitations posed when using conventional relational database technology. This paper explores these issues and discusses why biomedical data are difficult to model using conventional relational techniques. The authors propose a solution to these obstacles based on a relational database engine using a sparse, column-store architecture. The authors provide benchmarks comparing the performance of queries and schema-modification operations using three different strategies: (1) the standard conventional relational design; (2) past approaches used by biomedical informatics researchers; and (3) their sparse, column-store architecture. The performance results show that their architecture is a promising technique for storing and processing many types of data that are not handled well by the other two semantic data models.
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Dumitrascu, Dan L
2018-01-01
There is a competition between scientific journals in order to achieve leadership in their scientific field. There are several Romanian biomedical journals which are published in English and a smaller number in Romanian. We need a periodical analysis of their visibility and ranking according to scientometric measures. We searched all biomedical journals indexed on international data bases: Web of Science, PubMed, Scopus, Embase, Google Scholar. We analyzed their evaluation factors. Several journals from Romania in the biomedical field are indexed in international databases. Their scientometric indexes are not high. The best journal was acquired by an international publisher and is no longer listed for Romania. There are several Romanian biomedical journals indexed in international databases that deserve periodical analysis. There is a need to improve their ranking.
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Gururaj, Anupama E.; Chen, Xiaoling; Pournejati, Saeid; Alter, George; Hersh, William R.; Demner-Fushman, Dina; Ohno-Machado, Lucila
2017-01-01
Abstract The rapid proliferation of publicly available biomedical datasets has provided abundant resources that are potentially of value as a means to reproduce prior experiments, and to generate and explore novel hypotheses. However, there are a number of barriers to the re-use of such datasets, which are distributed across a broad array of dataset repositories, focusing on different data types and indexed using different terminologies. New methods are needed to enable biomedical researchers to locate datasets of interest within this rapidly expanding information ecosystem, and new resources are needed for the formal evaluation of these methods as they emerge. In this paper, we describe the design and generation of a benchmark for information retrieval of biomedical datasets, which was developed and used for the 2016 bioCADDIE Dataset Retrieval Challenge. In the tradition of the seminal Cranfield experiments, and as exemplified by the Text Retrieval Conference (TREC), this benchmark includes a corpus (biomedical datasets), a set of queries, and relevance judgments relating these queries to elements of the corpus. This paper describes the process through which each of these elements was derived, with a focus on those aspects that distinguish this benchmark from typical information retrieval reference sets. Specifically, we discuss the origin of our queries in the context of a larger collaborative effort, the biomedical and healthCAre Data Discovery Index Ecosystem (bioCADDIE) consortium, and the distinguishing features of biomedical dataset retrieval as a task. The resulting benchmark set has been made publicly available to advance research in the area of biomedical dataset retrieval. Database URL: https://biocaddie.org/benchmark-data PMID:29220453
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DEXTER: Disease-Expression Relation Extraction from Text.
Gupta, Samir; Dingerdissen, Hayley; Ross, Karen E; Hu, Yu; Wu, Cathy H; Mazumder, Raja; Vijay-Shanker, K
2018-01-01
Gene expression levels affect biological processes and play a key role in many diseases. Characterizing expression profiles is useful for clinical research, and diagnostics and prognostics of diseases. There are currently several high-quality databases that capture gene expression information, obtained mostly from large-scale studies, such as microarray and next-generation sequencing technologies, in the context of disease. The scientific literature is another rich source of information on gene expression-disease relationships that not only have been captured from large-scale studies but have also been observed in thousands of small-scale studies. Expression information obtained from literature through manual curation can extend expression databases. While many of the existing databases include information from literature, they are limited by the time-consuming nature of manual curation and have difficulty keeping up with the explosion of publications in the biomedical field. In this work, we describe an automated text-mining tool, Disease-Expression Relation Extraction from Text (DEXTER) to extract information from literature on gene and microRNA expression in the context of disease. One of the motivations in developing DEXTER was to extend the BioXpress database, a cancer-focused gene expression database that includes data derived from large-scale experiments and manual curation of publications. The literature-based portion of BioXpress lags behind significantly compared to expression information obtained from large-scale studies and can benefit from our text-mined results. We have conducted two different evaluations to measure the accuracy of our text-mining tool and achieved average F-scores of 88.51 and 81.81% for the two evaluations, respectively. Also, to demonstrate the ability to extract rich expression information in different disease-related scenarios, we used DEXTER to extract information on differential expression information for 2024 genes in lung cancer, 115 glycosyltransferases in 62 cancers and 826 microRNA in 171 cancers. All extractions using DEXTER are integrated in the literature-based portion of BioXpress.Database URL: http://biotm.cis.udel.edu/DEXTER.
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Adverse Drug Event Discovery Using Biomedical Literature: A Big Data Neural Network Adventure
Badger, Jonathan; LaRose, Eric; Shirzadi, Ehsan; Mahnke, Andrea; Mayer, John; Ye, Zhan; Page, David; Peissig, Peggy
2017-01-01
Background The study of adverse drug events (ADEs) is a tenured topic in medical literature. In recent years, increasing numbers of scientific articles and health-related social media posts have been generated and shared daily, albeit with very limited use for ADE study and with little known about the content with respect to ADEs. Objective The aim of this study was to develop a big data analytics strategy that mines the content of scientific articles and health-related Web-based social media to detect and identify ADEs. Methods We analyzed the following two data sources: (1) biomedical articles and (2) health-related social media blog posts. We developed an intelligent and scalable text mining solution on big data infrastructures composed of Apache Spark, natural language processing, and machine learning. This was combined with an Elasticsearch No-SQL distributed database to explore and visualize ADEs. Results The accuracy, precision, recall, and area under receiver operating characteristic of the system were 92.7%, 93.6%, 93.0%, and 0.905, respectively, and showed better results in comparison with traditional approaches in the literature. This work not only detected and classified ADE sentences from big data biomedical literature but also scientifically visualized ADE interactions. Conclusions To the best of our knowledge, this work is the first to investigate a big data machine learning strategy for ADE discovery on massive datasets downloaded from PubMed Central and social media. This contribution illustrates possible capacities in big data biomedical text analysis using advanced computational methods with real-time update from new data published on a daily basis. PMID:29222076
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Measuring use patterns of online journals and databases
De Groote, Sandra L.; Dorsch, Josephine L.
2003-01-01
Purpose: This research sought to determine use of online biomedical journals and databases and to assess current user characteristics associated with the use of online resources in an academic health sciences center. Setting: The Library of the Health Sciences–Peoria is a regional site of the University of Illinois at Chicago (UIC) Library with 350 print journals, more than 4,000 online journals, and multiple online databases. Methodology: A survey was designed to assess online journal use, print journal use, database use, computer literacy levels, and other library user characteristics. A survey was sent through campus mail to all (471) UIC Peoria faculty, residents, and students. Results: Forty-one percent (188) of the surveys were returned. Ninety-eight percent of the students, faculty, and residents reported having convenient access to a computer connected to the Internet. While 53% of the users indicated they searched MEDLINE at least once a week, other databases showed much lower usage. Overall, 71% of respondents indicated a preference for online over print journals when possible. Conclusions: Users prefer online resources to print, and many choose to access these online resources remotely. Convenience and full-text availability appear to play roles in selecting online resources. The findings of this study suggest that databases without links to full text and online journal collections without links from bibliographic databases will have lower use. These findings have implications for collection development, promotion of library resources, and end-user training. PMID:12883574
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Biomedical science journals in the Arab world.
Tadmouri, Ghazi O
2004-10-01
Medieval Arab scientists established the basis of medical practice and gave important attention to the publication of scientific results. At present, modern scientific publishing in the Arab world is in its developmental stage. Arab biomedical journals are less than 300, most of which are published in Egypt, Lebanon, and the Kingdom of Saudi Arabia. Yet, many of these journals do not have on-line access or are indexed in major bibliographic databases. The majority of indexed journals, however, do not have a stable presence in the popular PubMed database and their indexes are discontinued since 2001. The exposure of Arab biomedical journals in international indices undoubtedly plays an important role in improving the scientific quality of these journals. The successful examples discussed in this review encourage us to call for the formation of a consortium of Arab biomedical journal publishers to assist in redressing the balance of the region from biomedical data consumption to data production.
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A novel biomedical image indexing and retrieval system via deep preference learning.
Pang, Shuchao; Orgun, Mehmet A; Yu, Zhezhou
2018-05-01
The traditional biomedical image retrieval methods as well as content-based image retrieval (CBIR) methods originally designed for non-biomedical images either only consider using pixel and low-level features to describe an image or use deep features to describe images but still leave a lot of room for improving both accuracy and efficiency. In this work, we propose a new approach, which exploits deep learning technology to extract the high-level and compact features from biomedical images. The deep feature extraction process leverages multiple hidden layers to capture substantial feature structures of high-resolution images and represent them at different levels of abstraction, leading to an improved performance for indexing and retrieval of biomedical images. We exploit the current popular and multi-layered deep neural networks, namely, stacked denoising autoencoders (SDAE) and convolutional neural networks (CNN) to represent the discriminative features of biomedical images by transferring the feature representations and parameters of pre-trained deep neural networks from another domain. Moreover, in order to index all the images for finding the similarly referenced images, we also introduce preference learning technology to train and learn a kind of a preference model for the query image, which can output the similarity ranking list of images from a biomedical image database. To the best of our knowledge, this paper introduces preference learning technology for the first time into biomedical image retrieval. We evaluate the performance of two powerful algorithms based on our proposed system and compare them with those of popular biomedical image indexing approaches and existing regular image retrieval methods with detailed experiments over several well-known public biomedical image databases. Based on different criteria for the evaluation of retrieval performance, experimental results demonstrate that our proposed algorithms outperform the state-of-the-art techniques in indexing biomedical images. We propose a novel and automated indexing system based on deep preference learning to characterize biomedical images for developing computer aided diagnosis (CAD) systems in healthcare. Our proposed system shows an outstanding indexing ability and high efficiency for biomedical image retrieval applications and it can be used to collect and annotate the high-resolution images in a biomedical database for further biomedical image research and applications. Copyright © 2018 Elsevier B.V. All rights reserved.
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Lu, Zhiyong
2012-01-01
Today’s biomedical research has become heavily dependent on access to the biological knowledge encoded in expert curated biological databases. As the volume of biological literature grows rapidly, it becomes increasingly difficult for biocurators to keep up with the literature because manual curation is an expensive and time-consuming endeavour. Past research has suggested that computer-assisted curation can improve efficiency, but few text-mining systems have been formally evaluated in this regard. Through participation in the interactive text-mining track of the BioCreative 2012 workshop, we developed PubTator, a PubMed-like system that assists with two specific human curation tasks: document triage and bioconcept annotation. On the basis of evaluation results from two external user groups, we find that the accuracy of PubTator-assisted curation is comparable with that of manual curation and that PubTator can significantly increase human curatorial speed. These encouraging findings warrant further investigation with a larger number of publications to be annotated. Database URL: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/PubTator/ PMID:23160414
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[Presence of the biomedical periodicals of Hungarian editions in international databases].
Vasas, Lívia; Hercsel, Imréné
2006-01-15
Presence of the biomedical periodicals of Hungarian editions in international databases. The majority of Hungarian scientific results in medical and related sciences are published in scientific periodicals of foreign edition with high impact factor (IF) values, and they appear in international scientific literature in foreign languages. In this study the authors dealt with the presence and registered citation in international databases of those periodicals only, which had been published in Hungary and/or in cooperation with foreign publishing companies. The examination went back to year 1980 and covered a 25-year long period. 110 periodicals were selected for more detailed examination. The authors analyzed the situation of the current periodicals in the three most often visited databases (MEDLINE, EMBASE, Web of Science), and discovered, that the biomedical scientific periodicals of Hungarian interests were not represented with reasonable emphasis in the relevant international bibliographic databases. Because of the great number of data the scientific literature of medicine and related sciences could not be represented in its entirety, this publication, however, might give useful information for the inquirers, and call the attention of the competent people.
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NCBI2RDF: enabling full RDF-based access to NCBI databases.
Anguita, Alberto; García-Remesal, Miguel; de la Iglesia, Diana; Maojo, Victor
2013-01-01
RDF has become the standard technology for enabling interoperability among heterogeneous biomedical databases. The NCBI provides access to a large set of life sciences databases through a common interface called Entrez. However, the latter does not provide RDF-based access to such databases, and, therefore, they cannot be integrated with other RDF-compliant databases and accessed via SPARQL query interfaces. This paper presents the NCBI2RDF system, aimed at providing RDF-based access to the complete NCBI data repository. This API creates a virtual endpoint for servicing SPARQL queries over different NCBI repositories and presenting to users the query results in SPARQL results format, thus enabling this data to be integrated and/or stored with other RDF-compliant repositories. SPARQL queries are dynamically resolved, decomposed, and forwarded to the NCBI-provided E-utilities programmatic interface to access the NCBI data. Furthermore, we show how our approach increases the expressiveness of the native NCBI querying system, allowing several databases to be accessed simultaneously. This feature significantly boosts productivity when working with complex queries and saves time and effort to biomedical researchers. Our approach has been validated with a large number of SPARQL queries, thus proving its reliability and enhanced capabilities in biomedical environments.
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Yu, Hong; Agarwal, Shashank; Johnston, Mark; Cohen, Aaron
2009-01-06
Biomedical scientists need to access figures to validate research facts and to formulate or to test novel research hypotheses. However, figures are difficult to comprehend without associated text (e.g., figure legend and other reference text). We are developing automated systems to extract the relevant explanatory information along with figures extracted from full text articles. Such systems could be very useful in improving figure retrieval and in reducing the workload of biomedical scientists, who otherwise have to retrieve and read the entire full-text journal article to determine which figures are relevant to their research. As a crucial step, we studied the importance of associated text in biomedical figure comprehension. Twenty subjects evaluated three figure-text combinations: figure+legend, figure+legend+title+abstract, and figure+full-text. Using a Likert scale, each subject scored each figure+text according to the extent to which the subject thought he/she understood the meaning of the figure and the confidence in providing the assigned score. Additionally, each subject entered a free text summary for each figure-text. We identified missing information using indicator words present within the text summaries. Both the Likert scores and the missing information were statistically analyzed for differences among the figure-text types. We also evaluated the quality of text summaries with the text-summarization evaluation method the ROUGE score. Our results showed statistically significant differences in figure comprehension when varying levels of text were provided. When the full-text article is not available, presenting just the figure+legend left biomedical researchers lacking 39-68% of the information about a figure as compared to having complete figure comprehension; adding the title and abstract improved the situation, but still left biomedical researchers missing 30% of the information. When the full-text article is available, figure comprehension increased to 86-97%; this indicates that researchers felt that only 3-14% of the necessary information for full figure comprehension was missing when full text was available to them. Clearly there is information in the abstract and in the full text that biomedical scientists deem important for understanding the figures that appear in full-text biomedical articles. We conclude that the texts that appear in full-text biomedical articles are useful for understanding the meaning of a figure, and an effective figure-mining system needs to unlock the information beyond figure legend. Our work provides important guidance to the figure mining systems that extract information only from figure and figure legend.
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2009-01-01
Background Biomedical scientists need to access figures to validate research facts and to formulate or to test novel research hypotheses. However, figures are difficult to comprehend without associated text (e.g., figure legend and other reference text). We are developing automated systems to extract the relevant explanatory information along with figures extracted from full text articles. Such systems could be very useful in improving figure retrieval and in reducing the workload of biomedical scientists, who otherwise have to retrieve and read the entire full-text journal article to determine which figures are relevant to their research. As a crucial step, we studied the importance of associated text in biomedical figure comprehension. Methods Twenty subjects evaluated three figure-text combinations: figure+legend, figure+legend+title+abstract, and figure+full-text. Using a Likert scale, each subject scored each figure+text according to the extent to which the subject thought he/she understood the meaning of the figure and the confidence in providing the assigned score. Additionally, each subject entered a free text summary for each figure-text. We identified missing information using indicator words present within the text summaries. Both the Likert scores and the missing information were statistically analyzed for differences among the figure-text types. We also evaluated the quality of text summaries with the text-summarization evaluation method the ROUGE score. Results Our results showed statistically significant differences in figure comprehension when varying levels of text were provided. When the full-text article is not available, presenting just the figure+legend left biomedical researchers lacking 39–68% of the information about a figure as compared to having complete figure comprehension; adding the title and abstract improved the situation, but still left biomedical researchers missing 30% of the information. When the full-text article is available, figure comprehension increased to 86–97%; this indicates that researchers felt that only 3–14% of the necessary information for full figure comprehension was missing when full text was available to them. Clearly there is information in the abstract and in the full text that biomedical scientists deem important for understanding the figures that appear in full-text biomedical articles. Conclusion We conclude that the texts that appear in full-text biomedical articles are useful for understanding the meaning of a figure, and an effective figure-mining system needs to unlock the information beyond figure legend. Our work provides important guidance to the figure mining systems that extract information only from figure and figure legend. PMID:19126221
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miRiaD: A Text Mining Tool for Detecting Associations of microRNAs with Diseases.
Gupta, Samir; Ross, Karen E; Tudor, Catalina O; Wu, Cathy H; Schmidt, Carl J; Vijay-Shanker, K
2016-04-29
MicroRNAs are increasingly being appreciated as critical players in human diseases, and questions concerning the role of microRNAs arise in many areas of biomedical research. There are several manually curated databases of microRNA-disease associations gathered from the biomedical literature; however, it is difficult for curators of these databases to keep up with the explosion of publications in the microRNA-disease field. Moreover, automated literature mining tools that assist manual curation of microRNA-disease associations currently capture only one microRNA property (expression) in the context of one disease (cancer). Thus, there is a clear need to develop more sophisticated automated literature mining tools that capture a variety of microRNA properties and relations in the context of multiple diseases to provide researchers with fast access to the most recent published information and to streamline and accelerate manual curation. We have developed miRiaD (microRNAs in association with Disease), a text-mining tool that automatically extracts associations between microRNAs and diseases from the literature. These associations are often not directly linked, and the intermediate relations are often highly informative for the biomedical researcher. Thus, miRiaD extracts the miR-disease pairs together with an explanation for their association. We also developed a procedure that assigns scores to sentences, marking their informativeness, based on the microRNA-disease relation observed within the sentence. miRiaD was applied to the entire Medline corpus, identifying 8301 PMIDs with miR-disease associations. These abstracts and the miR-disease associations are available for browsing at http://biotm.cis.udel.edu/miRiaD . We evaluated the recall and precision of miRiaD with respect to information of high interest to public microRNA-disease database curators (expression and target gene associations), obtaining a recall of 88.46-90.78. When we expanded the evaluation to include sentences with a wide range of microRNA-disease information that may be of interest to biomedical researchers, miRiaD also performed very well with a F-score of 89.4. The informativeness ranking of sentences was evaluated in terms of nDCG (0.977) and correlation metrics (0.678-0.727) when compared to an annotator's ranked list. miRiaD, a high performance system that can capture a wide variety of microRNA-disease related information, extends beyond the scope of existing microRNA-disease resources. It can be incorporated into manual curation pipelines and serve as a resource for biomedical researchers interested in the role of microRNAs in disease. In our ongoing work we are developing an improved miRiaD web interface that will facilitate complex queries about microRNA-disease relationships, such as "In what diseases does microRNA regulation of apoptosis play a role?" or "Is there overlap in the sets of genes targeted by microRNAs in different types of dementia?"."
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Text Mining in Biomedical Domain with Emphasis on Document Clustering.
Renganathan, Vinaitheerthan
2017-07-01
With the exponential increase in the number of articles published every year in the biomedical domain, there is a need to build automated systems to extract unknown information from the articles published. Text mining techniques enable the extraction of unknown knowledge from unstructured documents. This paper reviews text mining processes in detail and the software tools available to carry out text mining. It also reviews the roles and applications of text mining in the biomedical domain. Text mining processes, such as search and retrieval of documents, pre-processing of documents, natural language processing, methods for text clustering, and methods for text classification are described in detail. Text mining techniques can facilitate the mining of vast amounts of knowledge on a given topic from published biomedical research articles and draw meaningful conclusions that are not possible otherwise.
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Condie, Brian G; Urbanski, William M
2014-01-01
Effective tools for searching the biomedical literature are essential for identifying reagents or mouse strains as well as for effective experimental design and informed interpretation of experimental results. We have built the Textpresso Site Specific Recombinases (Textpresso SSR) Web server to enable researchers who use mice to perform in-depth searches of a rapidly growing and complex part of the mouse literature. Our Textpresso Web server provides an interface for searching the full text of most of the peer-reviewed publications that report the characterization or use of mouse strains that express Cre or Flp recombinase. The database also contains most of the publications that describe the characterization or analysis of strains carrying conditional alleles or transgenes that can be inactivated or activated by site-specific recombinases such as Cre or Flp. Textpresso SSR complements the existing online databases that catalog Cre and Flp expression patterns by providing a unique online interface for the in-depth text mining of the site specific recombinase literature.
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Large-Scale Event Extraction from Literature with Multi-Level Gene Normalization
Wei, Chih-Hsuan; Hakala, Kai; Pyysalo, Sampo; Ananiadou, Sophia; Kao, Hung-Yu; Lu, Zhiyong; Salakoski, Tapio; Van de Peer, Yves; Ginter, Filip
2013-01-01
Text mining for the life sciences aims to aid database curation, knowledge summarization and information retrieval through the automated processing of biomedical texts. To provide comprehensive coverage and enable full integration with existing biomolecular database records, it is crucial that text mining tools scale up to millions of articles and that their analyses can be unambiguously linked to information recorded in resources such as UniProt, KEGG, BioGRID and NCBI databases. In this study, we investigate how fully automated text mining of complex biomolecular events can be augmented with a normalization strategy that identifies biological concepts in text, mapping them to identifiers at varying levels of granularity, ranging from canonicalized symbols to unique gene and proteins and broad gene families. To this end, we have combined two state-of-the-art text mining components, previously evaluated on two community-wide challenges, and have extended and improved upon these methods by exploiting their complementary nature. Using these systems, we perform normalization and event extraction to create a large-scale resource that is publicly available, unique in semantic scope, and covers all 21.9 million PubMed abstracts and 460 thousand PubMed Central open access full-text articles. This dataset contains 40 million biomolecular events involving 76 million gene/protein mentions, linked to 122 thousand distinct genes from 5032 species across the full taxonomic tree. Detailed evaluations and analyses reveal promising results for application of this data in database and pathway curation efforts. The main software components used in this study are released under an open-source license. Further, the resulting dataset is freely accessible through a novel API, providing programmatic and customized access (http://www.evexdb.org/api/v001/). Finally, to allow for large-scale bioinformatic analyses, the entire resource is available for bulk download from http://evexdb.org/download/, under the Creative Commons – Attribution – Share Alike (CC BY-SA) license. PMID:23613707
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Adverse Drug Event Discovery Using Biomedical Literature: A Big Data Neural Network Adventure.
P Tafti, Ahmad; Badger, Jonathan; LaRose, Eric; Shirzadi, Ehsan; Mahnke, Andrea; Mayer, John; Ye, Zhan; Page, David; Peissig, Peggy
2017-12-08
The study of adverse drug events (ADEs) is a tenured topic in medical literature. In recent years, increasing numbers of scientific articles and health-related social media posts have been generated and shared daily, albeit with very limited use for ADE study and with little known about the content with respect to ADEs. The aim of this study was to develop a big data analytics strategy that mines the content of scientific articles and health-related Web-based social media to detect and identify ADEs. We analyzed the following two data sources: (1) biomedical articles and (2) health-related social media blog posts. We developed an intelligent and scalable text mining solution on big data infrastructures composed of Apache Spark, natural language processing, and machine learning. This was combined with an Elasticsearch No-SQL distributed database to explore and visualize ADEs. The accuracy, precision, recall, and area under receiver operating characteristic of the system were 92.7%, 93.6%, 93.0%, and 0.905, respectively, and showed better results in comparison with traditional approaches in the literature. This work not only detected and classified ADE sentences from big data biomedical literature but also scientifically visualized ADE interactions. To the best of our knowledge, this work is the first to investigate a big data machine learning strategy for ADE discovery on massive datasets downloaded from PubMed Central and social media. This contribution illustrates possible capacities in big data biomedical text analysis using advanced computational methods with real-time update from new data published on a daily basis. ©Ahmad P Tafti, Jonathan Badger, Eric LaRose, Ehsan Shirzadi, Andrea Mahnke, John Mayer, Zhan Ye, David Page, Peggy Peissig. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 08.12.2017.
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Text Mining in Biomedical Domain with Emphasis on Document Clustering
2017-01-01
Objectives With the exponential increase in the number of articles published every year in the biomedical domain, there is a need to build automated systems to extract unknown information from the articles published. Text mining techniques enable the extraction of unknown knowledge from unstructured documents. Methods This paper reviews text mining processes in detail and the software tools available to carry out text mining. It also reviews the roles and applications of text mining in the biomedical domain. Results Text mining processes, such as search and retrieval of documents, pre-processing of documents, natural language processing, methods for text clustering, and methods for text classification are described in detail. Conclusions Text mining techniques can facilitate the mining of vast amounts of knowledge on a given topic from published biomedical research articles and draw meaningful conclusions that are not possible otherwise. PMID:28875048
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Figure Text Extraction in Biomedical Literature
Kim, Daehyun; Yu, Hong
2011-01-01
Background Figures are ubiquitous in biomedical full-text articles, and they represent important biomedical knowledge. However, the sheer volume of biomedical publications has made it necessary to develop computational approaches for accessing figures. Therefore, we are developing the Biomedical Figure Search engine (http://figuresearch.askHERMES.org) to allow bioscientists to access figures efficiently. Since text frequently appears in figures, automatically extracting such text may assist the task of mining information from figures. Little research, however, has been conducted exploring text extraction from biomedical figures. Methodology We first evaluated an off-the-shelf Optical Character Recognition (OCR) tool on its ability to extract text from figures appearing in biomedical full-text articles. We then developed a Figure Text Extraction Tool (FigTExT) to improve the performance of the OCR tool for figure text extraction through the use of three innovative components: image preprocessing, character recognition, and text correction. We first developed image preprocessing to enhance image quality and to improve text localization. Then we adapted the off-the-shelf OCR tool on the improved text localization for character recognition. Finally, we developed and evaluated a novel text correction framework by taking advantage of figure-specific lexicons. Results/Conclusions The evaluation on 382 figures (9,643 figure texts in total) randomly selected from PubMed Central full-text articles shows that FigTExT performed with 84% precision, 98% recall, and 90% F1-score for text localization and with 62.5% precision, 51.0% recall and 56.2% F1-score for figure text extraction. When limiting figure texts to those judged by domain experts to be important content, FigTExT performed with 87.3% precision, 68.8% recall, and 77% F1-score. FigTExT significantly improved the performance of the off-the-shelf OCR tool we used, which on its own performed with 36.6% precision, 19.3% recall, and 25.3% F1-score for text extraction. In addition, our results show that FigTExT can extract texts that do not appear in figure captions or other associated text, further suggesting the potential utility of FigTExT for improving figure search. PMID:21249186
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Figure text extraction in biomedical literature.
Kim, Daehyun; Yu, Hong
2011-01-13
Figures are ubiquitous in biomedical full-text articles, and they represent important biomedical knowledge. However, the sheer volume of biomedical publications has made it necessary to develop computational approaches for accessing figures. Therefore, we are developing the Biomedical Figure Search engine (http://figuresearch.askHERMES.org) to allow bioscientists to access figures efficiently. Since text frequently appears in figures, automatically extracting such text may assist the task of mining information from figures. Little research, however, has been conducted exploring text extraction from biomedical figures. We first evaluated an off-the-shelf Optical Character Recognition (OCR) tool on its ability to extract text from figures appearing in biomedical full-text articles. We then developed a Figure Text Extraction Tool (FigTExT) to improve the performance of the OCR tool for figure text extraction through the use of three innovative components: image preprocessing, character recognition, and text correction. We first developed image preprocessing to enhance image quality and to improve text localization. Then we adapted the off-the-shelf OCR tool on the improved text localization for character recognition. Finally, we developed and evaluated a novel text correction framework by taking advantage of figure-specific lexicons. The evaluation on 382 figures (9,643 figure texts in total) randomly selected from PubMed Central full-text articles shows that FigTExT performed with 84% precision, 98% recall, and 90% F1-score for text localization and with 62.5% precision, 51.0% recall and 56.2% F1-score for figure text extraction. When limiting figure texts to those judged by domain experts to be important content, FigTExT performed with 87.3% precision, 68.8% recall, and 77% F1-score. FigTExT significantly improved the performance of the off-the-shelf OCR tool we used, which on its own performed with 36.6% precision, 19.3% recall, and 25.3% F1-score for text extraction. In addition, our results show that FigTExT can extract texts that do not appear in figure captions or other associated text, further suggesting the potential utility of FigTExT for improving figure search.
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NCBI2RDF: Enabling Full RDF-Based Access to NCBI Databases
Anguita, Alberto; García-Remesal, Miguel; de la Iglesia, Diana; Maojo, Victor
2013-01-01
RDF has become the standard technology for enabling interoperability among heterogeneous biomedical databases. The NCBI provides access to a large set of life sciences databases through a common interface called Entrez. However, the latter does not provide RDF-based access to such databases, and, therefore, they cannot be integrated with other RDF-compliant databases and accessed via SPARQL query interfaces. This paper presents the NCBI2RDF system, aimed at providing RDF-based access to the complete NCBI data repository. This API creates a virtual endpoint for servicing SPARQL queries over different NCBI repositories and presenting to users the query results in SPARQL results format, thus enabling this data to be integrated and/or stored with other RDF-compliant repositories. SPARQL queries are dynamically resolved, decomposed, and forwarded to the NCBI-provided E-utilities programmatic interface to access the NCBI data. Furthermore, we show how our approach increases the expressiveness of the native NCBI querying system, allowing several databases to be accessed simultaneously. This feature significantly boosts productivity when working with complex queries and saves time and effort to biomedical researchers. Our approach has been validated with a large number of SPARQL queries, thus proving its reliability and enhanced capabilities in biomedical environments. PMID:23984425
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Vlassov, Vasiliy V; Danishevskiy, Kirill D
2008-01-01
In the 20th century, Russian biomedical science experienced a decline from the blossom of the early years to a drastic state. Through the first decades of the USSR, it was transformed to suit the ideological requirements of a totalitarian state and biased directives of communist leaders. Later, depressing economic conditions and isolation from the international research community further impeded its development. Contemporary Russia has inherited a system of medical education quite different from the west as well as counterproductive regulations for the allocation of research funding. The methodology of medical and epidemiological research in Russia is largely outdated. Epidemiology continues to focus on infectious disease and results of the best studies tend to be published in international periodicals. MEDLINE continues to be the best database to search for Russian biomedical publications, despite only a small proportion being indexed. The database of the Moscow Central Medical Library is the largest national database of medical periodicals, but does not provide abstracts and full subject heading codes, and it does not cover even the entire collection of the Library. New databases and catalogs (e.g. Panteleimon) that have appeared recently are incomplete and do not enable effective searching. PMID:18826569
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Vlassov, Vasiliy V; Danishevskiy, Kirill D
2008-09-30
In the 20th century, Russian biomedical science experienced a decline from the blossom of the early years to a drastic state. Through the first decades of the USSR, it was transformed to suit the ideological requirements of a totalitarian state and biased directives of communist leaders. Later, depressing economic conditions and isolation from the international research community further impeded its development. Contemporary Russia has inherited a system of medical education quite different from the west as well as counterproductive regulations for the allocation of research funding. The methodology of medical and epidemiological research in Russia is largely outdated. Epidemiology continues to focus on infectious disease and results of the best studies tend to be published in international periodicals. MEDLINE continues to be the best database to search for Russian biomedical publications, despite only a small proportion being indexed. The database of the Moscow Central Medical Library is the largest national database of medical periodicals, but does not provide abstracts and full subject heading codes, and it does not cover even the entire collection of the Library. New databases and catalogs (e.g. Panteleimon) that have appeared recently are incomplete and do not enable effective searching.
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A new visual navigation system for exploring biomedical Open Educational Resource (OER) videos.
Zhao, Baoquan; Xu, Songhua; Lin, Shujin; Luo, Xiaonan; Duan, Lian
2016-04-01
Biomedical videos as open educational resources (OERs) are increasingly proliferating on the Internet. Unfortunately, seeking personally valuable content from among the vast corpus of quality yet diverse OER videos is nontrivial due to limitations of today's keyword- and content-based video retrieval techniques. To address this need, this study introduces a novel visual navigation system that facilitates users' information seeking from biomedical OER videos in mass quantity by interactively offering visual and textual navigational clues that are both semantically revealing and user-friendly. The authors collected and processed around 25 000 YouTube videos, which collectively last for a total length of about 4000 h, in the broad field of biomedical sciences for our experiment. For each video, its semantic clues are first extracted automatically through computationally analyzing audio and visual signals, as well as text either accompanying or embedded in the video. These extracted clues are subsequently stored in a metadata database and indexed by a high-performance text search engine. During the online retrieval stage, the system renders video search results as dynamic web pages using a JavaScript library that allows users to interactively and intuitively explore video content both efficiently and effectively.ResultsThe authors produced a prototype implementation of the proposed system, which is publicly accessible athttps://patentq.njit.edu/oer To examine the overall advantage of the proposed system for exploring biomedical OER videos, the authors further conducted a user study of a modest scale. The study results encouragingly demonstrate the functional effectiveness and user-friendliness of the new system for facilitating information seeking from and content exploration among massive biomedical OER videos. Using the proposed tool, users can efficiently and effectively find videos of interest, precisely locate video segments delivering personally valuable information, as well as intuitively and conveniently preview essential content of a single or a collection of videos. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
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Managing biomedical image metadata for search and retrieval of similar images.
Korenblum, Daniel; Rubin, Daniel; Napel, Sandy; Rodriguez, Cesar; Beaulieu, Chris
2011-08-01
Radiology images are generally disconnected from the metadata describing their contents, such as imaging observations ("semantic" metadata), which are usually described in text reports that are not directly linked to the images. We developed a system, the Biomedical Image Metadata Manager (BIMM) to (1) address the problem of managing biomedical image metadata and (2) facilitate the retrieval of similar images using semantic feature metadata. Our approach allows radiologists, researchers, and students to take advantage of the vast and growing repositories of medical image data by explicitly linking images to their associated metadata in a relational database that is globally accessible through a Web application. BIMM receives input in the form of standard-based metadata files using Web service and parses and stores the metadata in a relational database allowing efficient data query and maintenance capabilities. Upon querying BIMM for images, 2D regions of interest (ROIs) stored as metadata are automatically rendered onto preview images included in search results. The system's "match observations" function retrieves images with similar ROIs based on specific semantic features describing imaging observation characteristics (IOCs). We demonstrate that the system, using IOCs alone, can accurately retrieve images with diagnoses matching the query images, and we evaluate its performance on a set of annotated liver lesion images. BIMM has several potential applications, e.g., computer-aided detection and diagnosis, content-based image retrieval, automating medical analysis protocols, and gathering population statistics like disease prevalences. The system provides a framework for decision support systems, potentially improving their diagnostic accuracy and selection of appropriate therapies.
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Xenbase: Core features, data acquisition, and data processing.
James-Zorn, Christina; Ponferrada, Virgillio G; Burns, Kevin A; Fortriede, Joshua D; Lotay, Vaneet S; Liu, Yu; Brad Karpinka, J; Karimi, Kamran; Zorn, Aaron M; Vize, Peter D
2015-08-01
Xenbase, the Xenopus model organism database (www.xenbase.org), is a cloud-based, web-accessible resource that integrates the diverse genomic and biological data from Xenopus research. Xenopus frogs are one of the major vertebrate animal models used for biomedical research, and Xenbase is the central repository for the enormous amount of data generated using this model tetrapod. The goal of Xenbase is to accelerate discovery by enabling investigators to make novel connections between molecular pathways in Xenopus and human disease. Our relational database and user-friendly interface make these data easy to query and allows investigators to quickly interrogate and link different data types in ways that would otherwise be difficult, time consuming, or impossible. Xenbase also enhances the value of these data through high-quality gene expression curation and data integration, by providing bioinformatics tools optimized for Xenopus experiments, and by linking Xenopus data to other model organisms and to human data. Xenbase draws in data via pipelines that download data, parse the content, and save them into appropriate files and database tables. Furthermore, Xenbase makes these data accessible to the broader biomedical community by continually providing annotated data updates to organizations such as NCBI, UniProtKB, and Ensembl. Here, we describe our bioinformatics, genome-browsing tools, data acquisition and sharing, our community submitted and literature curation pipelines, text-mining support, gene page features, and the curation of gene nomenclature and gene models. © 2015 Wiley Periodicals, Inc.
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Extracting semantically enriched events from biomedical literature
2012-01-01
Background Research into event-based text mining from the biomedical literature has been growing in popularity to facilitate the development of advanced biomedical text mining systems. Such technology permits advanced search, which goes beyond document or sentence-based retrieval. However, existing event-based systems typically ignore additional information within the textual context of events that can determine, amongst other things, whether an event represents a fact, hypothesis, experimental result or analysis of results, whether it describes new or previously reported knowledge, and whether it is speculated or negated. We refer to such contextual information as meta-knowledge. The automatic recognition of such information can permit the training of systems allowing finer-grained searching of events according to the meta-knowledge that is associated with them. Results Based on a corpus of 1,000 MEDLINE abstracts, fully manually annotated with both events and associated meta-knowledge, we have constructed a machine learning-based system that automatically assigns meta-knowledge information to events. This system has been integrated into EventMine, a state-of-the-art event extraction system, in order to create a more advanced system (EventMine-MK) that not only extracts events from text automatically, but also assigns five different types of meta-knowledge to these events. The meta-knowledge assignment module of EventMine-MK performs with macro-averaged F-scores in the range of 57-87% on the BioNLP’09 Shared Task corpus. EventMine-MK has been evaluated on the BioNLP’09 Shared Task subtask of detecting negated and speculated events. Our results show that EventMine-MK can outperform other state-of-the-art systems that participated in this task. Conclusions We have constructed the first practical system that extracts both events and associated, detailed meta-knowledge information from biomedical literature. The automatically assigned meta-knowledge information can be used to refine search systems, in order to provide an extra search layer beyond entities and assertions, dealing with phenomena such as rhetorical intent, speculations, contradictions and negations. This finer grained search functionality can assist in several important tasks, e.g., database curation (by locating new experimental knowledge) and pathway enrichment (by providing information for inference). To allow easy integration into text mining systems, EventMine-MK is provided as a UIMA component that can be used in the interoperable text mining infrastructure, U-Compare. PMID:22621266
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Extracting semantically enriched events from biomedical literature.
Miwa, Makoto; Thompson, Paul; McNaught, John; Kell, Douglas B; Ananiadou, Sophia
2012-05-23
Research into event-based text mining from the biomedical literature has been growing in popularity to facilitate the development of advanced biomedical text mining systems. Such technology permits advanced search, which goes beyond document or sentence-based retrieval. However, existing event-based systems typically ignore additional information within the textual context of events that can determine, amongst other things, whether an event represents a fact, hypothesis, experimental result or analysis of results, whether it describes new or previously reported knowledge, and whether it is speculated or negated. We refer to such contextual information as meta-knowledge. The automatic recognition of such information can permit the training of systems allowing finer-grained searching of events according to the meta-knowledge that is associated with them. Based on a corpus of 1,000 MEDLINE abstracts, fully manually annotated with both events and associated meta-knowledge, we have constructed a machine learning-based system that automatically assigns meta-knowledge information to events. This system has been integrated into EventMine, a state-of-the-art event extraction system, in order to create a more advanced system (EventMine-MK) that not only extracts events from text automatically, but also assigns five different types of meta-knowledge to these events. The meta-knowledge assignment module of EventMine-MK performs with macro-averaged F-scores in the range of 57-87% on the BioNLP'09 Shared Task corpus. EventMine-MK has been evaluated on the BioNLP'09 Shared Task subtask of detecting negated and speculated events. Our results show that EventMine-MK can outperform other state-of-the-art systems that participated in this task. We have constructed the first practical system that extracts both events and associated, detailed meta-knowledge information from biomedical literature. The automatically assigned meta-knowledge information can be used to refine search systems, in order to provide an extra search layer beyond entities and assertions, dealing with phenomena such as rhetorical intent, speculations, contradictions and negations. This finer grained search functionality can assist in several important tasks, e.g., database curation (by locating new experimental knowledge) and pathway enrichment (by providing information for inference). To allow easy integration into text mining systems, EventMine-MK is provided as a UIMA component that can be used in the interoperable text mining infrastructure, U-Compare.
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Moradi, Milad; Ghadiri, Nasser
2018-01-01
Automatic text summarization tools help users in the biomedical domain to acquire their intended information from various textual resources more efficiently. Some of biomedical text summarization systems put the basis of their sentence selection approach on the frequency of concepts extracted from the input text. However, it seems that exploring other measures rather than the raw frequency for identifying valuable contents within an input document, or considering correlations existing between concepts, may be more useful for this type of summarization. In this paper, we describe a Bayesian summarization method for biomedical text documents. The Bayesian summarizer initially maps the input text to the Unified Medical Language System (UMLS) concepts; then it selects the important ones to be used as classification features. We introduce six different feature selection approaches to identify the most important concepts of the text and select the most informative contents according to the distribution of these concepts. We show that with the use of an appropriate feature selection approach, the Bayesian summarizer can improve the performance of biomedical summarization. Using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) toolkit, we perform extensive evaluations on a corpus of scientific papers in the biomedical domain. The results show that when the Bayesian summarizer utilizes the feature selection methods that do not use the raw frequency, it can outperform the biomedical summarizers that rely on the frequency of concepts, domain-independent and baseline methods. Copyright © 2017 Elsevier B.V. All rights reserved.
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Since the early 1970s, the National Library of Medicine (NLM) has made searching the biomedical literature faster and easier by providing online information on NLMs family of databases -- (currently 40 online databases). MEDLINE?, NLMs premier database, has over 8.5 million citat...
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Identification of biomedical journals in Spain and Latin America.
Bonfill, Xavier; Osorio, Dimelza; Posso, Margarita; Solà, Ivan; Rada, Gabriel; Torres, Ania; García Dieguez, Marcelo; Piña-Pozas, Maricela; Díaz-García, Luisa; Tristán, Mario; Gandarilla, Omar; Rincón-Valenzuela, David A; Martí, Arturo; Hidalgo, Ricardo; Simancas-Racines, Daniel; López, Luis; Correa, Ricardo; Rojas-De-Arias, Antonieta; Loza, César; Gianneo, Óscar; Pardo, Hector
2015-12-01
Journals in languages other than English that publish original clinical research are often not well covered in the main biomedical databases and therefore often not included in systematic reviews. This study aimed to identify Spanish language biomedical journals from Spain and Latin America and to describe their main features. Journals were identified in electronic databases, publishers' catalogues and local registries. Eligibility was determined by assessing data from these sources or the journals' websites, when available. A total of 2457 journals were initially identified; 1498 met inclusion criteria. Spain (27.3%), Mexico (16.0%), Argentina (15.1%) and Chile (11.9%) had the highest number of journals. Most (85.8%) are currently active; 87.8% have an ISSN. The median and mean length of publication were 22 and 29 years, respectively. A total of 66.0% were indexed in at least one database; 3.0% had an impact factor in 2012. A total of 845 journals had websites (56.4%), of which 700 (82.8%) were searchable and 681 (80.6%) free of charge. Most of the identified journals have no impact factor or are not indexed in any of the major databases. The list of identified biomedical journals can be a useful resource when conducting hand searching activities and identifying clinical trials that otherwise would not be retrieved. © 2015 Health Libraries Group.
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AST commercial human space flight biomedical data collection
DOT National Transportation Integrated Search
2007-02-01
Recommendations are made for specific biomedical data, equipment, and a database that will increase the knowledge and understanding of how short duration, suborbital space flight missions with brief exposure to microgravity affects the human body. Th...
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[Over- or underestimated? Bibliographic survey of the biomedical periodicals published in Hungary].
Berhidi, Anna; Horváth, Katalin; Horváth, Gabriella; Vasas, Lívia
2013-06-30
This publication - based on an article published in 2006 - emphasises the qualities of the current biomedical periodicals of Hungarian editions. The aim of this study was to analyse how Hungarian journals meet the requirements of the scientific aspect and international visibility. Authors evaluated 93 Hungarian biomedical periodicals by 4 viewpoints of the two criteria mentioned above. 35% of the analysed journals complete the attributes of scientific aspect, 5% the international visibility, 6% fulfill all examined criteria, and 25% are indexed in international databases. 6 biomedical Hungarian periodicals covered by each of the three main bibliographic databases (Medline, Scopus, Web of Science) have the best qualities. Authors recommend to improve viewpoints of the scientific aspect and international visibility. The basis of qualitative adequacy are the accurate authors' guidelines, title, abstract, keywords of the articles in English, and the ability to publish on time.
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How to Search, Write, Prepare and Publish the Scientific Papers in the Biomedical Journals
Masic, Izet
2011-01-01
This article describes the methodology of preparation, writing and publishing scientific papers in biomedical journals. given is a concise overview of the concept and structure of the System of biomedical scientific and technical information and the way of biomedical literature retreival from worldwide biomedical databases. Described are the scientific and professional medical journals that are currently published in Bosnia and Herzegovina. Also, given is the comparative review on the number and structure of papers published in indexed journals in Bosnia and Herzegovina, which are listed in the Medline database. Analyzed are three B&H journals indexed in MEDLINE database: Medical Archives (Medicinski Arhiv), Bosnian Journal of Basic Medical Sciences and Medical Gazette (Medicinki Glasnik) in 2010. The largest number of original papers was published in the Medical Archives. There is a statistically significant difference in the number of papers published by local authors in relation to international journals in favor of the Medical Archives. True, the Journal Bosnian Journal of Basic Medical Sciences does not categorize the articles and we could not make comparisons. Journal Medical Archives and Bosnian Journal of Basic Medical Sciences by percentage published the largest number of articles by authors from Sarajevo and Tuzla, the two oldest and largest university medical centers in Bosnia and Herzegovina. The author believes that it is necessary to make qualitative changes in the reception and reviewing of papers for publication in biomedical journals published in Bosnia and Herzegovina which should be the responsibility of the separate scientific authority/ committee composed of experts in the field of medicine at the state level. PMID:23572850
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Organization and integration of biomedical knowledge with concept maps for key peroxisomal pathways.
Willemsen, A M; Jansen, G A; Komen, J C; van Hooff, S; Waterham, H R; Brites, P M T; Wanders, R J A; van Kampen, A H C
2008-08-15
One important area of clinical genomics research involves the elucidation of molecular mechanisms underlying (complex) disorders which eventually may lead to new diagnostic or drug targets. To further advance this area of clinical genomics one of the main challenges is the acquisition and integration of data, information and expert knowledge for specific biomedical domains and diseases. Currently the required information is not very well organized but scattered over biological and biomedical databases, basic text books, scientific literature and experts' minds and may be highly specific, heterogeneous, complex and voluminous. We present a new framework to construct knowledge bases with concept maps for presentation of information and the web ontology language OWL for the representation of information. We demonstrate this framework through the construction of a peroxisomal knowledge base, which focuses on four key peroxisomal pathways and several related genetic disorders. All 155 concept maps in our knowledge base are linked to at least one other concept map, which allows the visualization of one big network of related pieces of information. The peroxisome knowledge base is available from www.bioinformaticslaboratory.nl (Support-->Web applications). Supplementary data is available from www.bioinformaticslaboratory.nl (Research-->Output--> Publications--> KB_SuppInfo)
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A Simple and Practical Dictionary-based Approach for Identification of Proteins in Medline Abstracts
Egorov, Sergei; Yuryev, Anton; Daraselia, Nikolai
2004-01-01
Objective: The aim of this study was to develop a practical and efficient protein identification system for biomedical corpora. Design: The developed system, called ProtScan, utilizes a carefully constructed dictionary of mammalian proteins in conjunction with a specialized tokenization algorithm to identify and tag protein name occurrences in biomedical texts and also takes advantage of Medline “Name-of-Substance” (NOS) annotation. The dictionaries for ProtScan were constructed in a semi-automatic way from various public-domain sequence databases followed by an intensive expert curation step. Measurements: The recall and precision of the system have been determined using 1,000 randomly selected and hand-tagged Medline abstracts. Results: The developed system is capable of identifying protein occurrences in Medline abstracts with a 98% precision and 88% recall. It was also found to be capable of processing approximately 300 abstracts per second. Without utilization of NOS annotation, precision and recall were found to be 98.5% and 84%, respectively. Conclusion: The developed system appears to be well suited for protein-based Medline indexing and can help to improve biomedical information retrieval. Further approaches to ProtScan's recall improvement also are discussed. PMID:14764613
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Probabilistic and machine learning-based retrieval approaches for biomedical dataset retrieval
Karisani, Payam; Qin, Zhaohui S; Agichtein, Eugene
2018-01-01
Abstract The bioCADDIE dataset retrieval challenge brought together different approaches to retrieval of biomedical datasets relevant to a user’s query, expressed as a text description of a needed dataset. We describe experiments in applying a data-driven, machine learning-based approach to biomedical dataset retrieval as part of this challenge. We report on a series of experiments carried out to evaluate the performance of both probabilistic and machine learning-driven techniques from information retrieval, as applied to this challenge. Our experiments with probabilistic information retrieval methods, such as query term weight optimization, automatic query expansion and simulated user relevance feedback, demonstrate that automatically boosting the weights of important keywords in a verbose query is more effective than other methods. We also show that although there is a rich space of potential representations and features available in this domain, machine learning-based re-ranking models are not able to improve on probabilistic information retrieval techniques with the currently available training data. The models and algorithms presented in this paper can serve as a viable implementation of a search engine to provide access to biomedical datasets. The retrieval performance is expected to be further improved by using additional training data that is created by expert annotation, or gathered through usage logs, clicks and other processes during natural operation of the system. Database URL: https://github.com/emory-irlab/biocaddie PMID:29688379
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OntoMate: a text-mining tool aiding curation at the Rat Genome Database
Liu, Weisong; Laulederkind, Stanley J. F.; Hayman, G. Thomas; Wang, Shur-Jen; Nigam, Rajni; Smith, Jennifer R.; De Pons, Jeff; Dwinell, Melinda R.; Shimoyama, Mary
2015-01-01
The Rat Genome Database (RGD) is the premier repository of rat genomic, genetic and physiologic data. Converting data from free text in the scientific literature to a structured format is one of the main tasks of all model organism databases. RGD spends considerable effort manually curating gene, Quantitative Trait Locus (QTL) and strain information. The rapidly growing volume of biomedical literature and the active research in the biological natural language processing (bioNLP) community have given RGD the impetus to adopt text-mining tools to improve curation efficiency. Recently, RGD has initiated a project to use OntoMate, an ontology-driven, concept-based literature search engine developed at RGD, as a replacement for the PubMed (http://www.ncbi.nlm.nih.gov/pubmed) search engine in the gene curation workflow. OntoMate tags abstracts with gene names, gene mutations, organism name and most of the 16 ontologies/vocabularies used at RGD. All terms/ entities tagged to an abstract are listed with the abstract in the search results. All listed terms are linked both to data entry boxes and a term browser in the curation tool. OntoMate also provides user-activated filters for species, date and other parameters relevant to the literature search. Using the system for literature search and import has streamlined the process compared to using PubMed. The system was built with a scalable and open architecture, including features specifically designed to accelerate the RGD gene curation process. With the use of bioNLP tools, RGD has added more automation to its curation workflow. Database URL: http://rgd.mcw.edu PMID:25619558
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Enhancing biomedical text summarization using semantic relation extraction.
Shang, Yue; Li, Yanpeng; Lin, Hongfei; Yang, Zhihao
2011-01-01
Automatic text summarization for a biomedical concept can help researchers to get the key points of a certain topic from large amount of biomedical literature efficiently. In this paper, we present a method for generating text summary for a given biomedical concept, e.g., H1N1 disease, from multiple documents based on semantic relation extraction. Our approach includes three stages: 1) We extract semantic relations in each sentence using the semantic knowledge representation tool SemRep. 2) We develop a relation-level retrieval method to select the relations most relevant to each query concept and visualize them in a graphic representation. 3) For relations in the relevant set, we extract informative sentences that can interpret them from the document collection to generate text summary using an information retrieval based method. Our major focus in this work is to investigate the contribution of semantic relation extraction to the task of biomedical text summarization. The experimental results on summarization for a set of diseases show that the introduction of semantic knowledge improves the performance and our results are better than the MEAD system, a well-known tool for text summarization.
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Yoo, Illhoi; Hu, Xiaohua; Song, Il-Yeol
2007-11-27
A huge amount of biomedical textual information has been produced and collected in MEDLINE for decades. In order to easily utilize biomedical information in the free text, document clustering and text summarization together are used as a solution for text information overload problem. In this paper, we introduce a coherent graph-based semantic clustering and summarization approach for biomedical literature. Our extensive experimental results show the approach shows 45% cluster quality improvement and 72% clustering reliability improvement, in terms of misclassification index, over Bisecting K-means as a leading document clustering approach. In addition, our approach provides concise but rich text summary in key concepts and sentences. Our coherent biomedical literature clustering and summarization approach that takes advantage of ontology-enriched graphical representations significantly improves the quality of document clusters and understandability of documents through summaries.
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Yoo, Illhoi; Hu, Xiaohua; Song, Il-Yeol
2007-01-01
Background A huge amount of biomedical textual information has been produced and collected in MEDLINE for decades. In order to easily utilize biomedical information in the free text, document clustering and text summarization together are used as a solution for text information overload problem. In this paper, we introduce a coherent graph-based semantic clustering and summarization approach for biomedical literature. Results Our extensive experimental results show the approach shows 45% cluster quality improvement and 72% clustering reliability improvement, in terms of misclassification index, over Bisecting K-means as a leading document clustering approach. In addition, our approach provides concise but rich text summary in key concepts and sentences. Conclusion Our coherent biomedical literature clustering and summarization approach that takes advantage of ontology-enriched graphical representations significantly improves the quality of document clusters and understandability of documents through summaries. PMID:18047705
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Concept recognition for extracting protein interaction relations from biomedical text
Baumgartner, William A; Lu, Zhiyong; Johnson, Helen L; Caporaso, J Gregory; Paquette, Jesse; Lindemann, Anna; White, Elizabeth K; Medvedeva, Olga; Cohen, K Bretonnel; Hunter, Lawrence
2008-01-01
Background: Reliable information extraction applications have been a long sought goal of the biomedical text mining community, a goal that if reached would provide valuable tools to benchside biologists in their increasingly difficult task of assimilating the knowledge contained in the biomedical literature. We present an integrated approach to concept recognition in biomedical text. Concept recognition provides key information that has been largely missing from previous biomedical information extraction efforts, namely direct links to well defined knowledge resources that explicitly cement the concept's semantics. The BioCreative II tasks discussed in this special issue have provided a unique opportunity to demonstrate the effectiveness of concept recognition in the field of biomedical language processing. Results: Through the modular construction of a protein interaction relation extraction system, we present several use cases of concept recognition in biomedical text, and relate these use cases to potential uses by the benchside biologist. Conclusion: Current information extraction technologies are approaching performance standards at which concept recognition can begin to deliver high quality data to the benchside biologist. Our system is available as part of the BioCreative Meta-Server project and on the internet . PMID:18834500
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Automated extraction and semantic analysis of mutation impacts from the biomedical literature
2012-01-01
Background Mutations as sources of evolution have long been the focus of attention in the biomedical literature. Accessing the mutational information and their impacts on protein properties facilitates research in various domains, such as enzymology and pharmacology. However, manually curating the rich and fast growing repository of biomedical literature is expensive and time-consuming. As a solution, text mining approaches have increasingly been deployed in the biomedical domain. While the detection of single-point mutations is well covered by existing systems, challenges still exist in grounding impacts to their respective mutations and recognizing the affected protein properties, in particular kinetic and stability properties together with physical quantities. Results We present an ontology model for mutation impacts, together with a comprehensive text mining system for extracting and analysing mutation impact information from full-text articles. Organisms, as sources of proteins, are extracted to help disambiguation of genes and proteins. Our system then detects mutation series to correctly ground detected impacts using novel heuristics. It also extracts the affected protein properties, in particular kinetic and stability properties, as well as the magnitude of the effects and validates these relations against the domain ontology. The output of our system can be provided in various formats, in particular by populating an OWL-DL ontology, which can then be queried to provide structured information. The performance of the system is evaluated on our manually annotated corpora. In the impact detection task, our system achieves a precision of 70.4%-71.1%, a recall of 71.3%-71.5%, and grounds the detected impacts with an accuracy of 76.5%-77%. The developed system, including resources, evaluation data and end-user and developer documentation is freely available under an open source license at http://www.semanticsoftware.info/open-mutation-miner. Conclusion We present Open Mutation Miner (OMM), the first comprehensive, fully open-source approach to automatically extract impacts and related relevant information from the biomedical literature. We assessed the performance of our work on manually annotated corpora and the results show the reliability of our approach. The representation of the extracted information into a structured format facilitates knowledge management and aids in database curation and correction. Furthermore, access to the analysis results is provided through multiple interfaces, including web services for automated data integration and desktop-based solutions for end user interactions. PMID:22759648
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Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders.
Hamosh, Ada; Scott, Alan F; Amberger, Joanna S; Bocchini, Carol A; McKusick, Victor A
2005-01-01
Online Mendelian Inheritance in Man (OMIM) is a comprehensive, authoritative and timely knowledgebase of human genes and genetic disorders compiled to support human genetics research and education and the practice of clinical genetics. Started by Dr Victor A. McKusick as the definitive reference Mendelian Inheritance in Man, OMIM (http://www.ncbi.nlm.nih.gov/omim/) is now distributed electronically by the National Center for Biotechnology Information, where it is integrated with the Entrez suite of databases. Derived from the biomedical literature, OMIM is written and edited at Johns Hopkins University with input from scientists and physicians around the world. Each OMIM entry has a full-text summary of a genetically determined phenotype and/or gene and has numerous links to other genetic databases such as DNA and protein sequence, PubMed references, general and locus-specific mutation databases, HUGO nomenclature, MapViewer, GeneTests, patient support groups and many others. OMIM is an easy and straightforward portal to the burgeoning information in human genetics.
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MimoSA: a system for minimotif annotation
2010-01-01
Background Minimotifs are short peptide sequences within one protein, which are recognized by other proteins or molecules. While there are now several minimotif databases, they are incomplete. There are reports of many minimotifs in the primary literature, which have yet to be annotated, while entirely novel minimotifs continue to be published on a weekly basis. Our recently proposed function and sequence syntax for minimotifs enables us to build a general tool that will facilitate structured annotation and management of minimotif data from the biomedical literature. Results We have built the MimoSA application for minimotif annotation. The application supports management of the Minimotif Miner database, literature tracking, and annotation of new minimotifs. MimoSA enables the visualization, organization, selection and editing functions of minimotifs and their attributes in the MnM database. For the literature components, Mimosa provides paper status tracking and scoring of papers for annotation through a freely available machine learning approach, which is based on word correlation. The paper scoring algorithm is also available as a separate program, TextMine. Form-driven annotation of minimotif attributes enables entry of new minimotifs into the MnM database. Several supporting features increase the efficiency of annotation. The layered architecture of MimoSA allows for extensibility by separating the functions of paper scoring, minimotif visualization, and database management. MimoSA is readily adaptable to other annotation efforts that manually curate literature into a MySQL database. Conclusions MimoSA is an extensible application that facilitates minimotif annotation and integrates with the Minimotif Miner database. We have built MimoSA as an application that integrates dynamic abstract scoring with a high performance relational model of minimotif syntax. MimoSA's TextMine, an efficient paper-scoring algorithm, can be used to dynamically rank papers with respect to context. PMID:20565705
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PuReD-MCL: a graph-based PubMed document clustering methodology.
Theodosiou, T; Darzentas, N; Angelis, L; Ouzounis, C A
2008-09-01
Biomedical literature is the principal repository of biomedical knowledge, with PubMed being the most complete database collecting, organizing and analyzing such textual knowledge. There are numerous efforts that attempt to exploit this information by using text mining and machine learning techniques. We developed a novel approach, called PuReD-MCL (Pubmed Related Documents-MCL), which is based on the graph clustering algorithm MCL and relevant resources from PubMed. PuReD-MCL avoids using natural language processing (NLP) techniques directly; instead, it takes advantage of existing resources, available from PubMed. PuReD-MCL then clusters documents efficiently using the MCL graph clustering algorithm, which is based on graph flow simulation. This process allows users to analyse the results by highlighting important clues, and finally to visualize the clusters and all relevant information using an interactive graph layout algorithm, for instance BioLayout Express 3D. The methodology was applied to two different datasets, previously used for the validation of the document clustering tool TextQuest. The first dataset involves the organisms Escherichia coli and yeast, whereas the second is related to Drosophila development. PuReD-MCL successfully reproduces the annotated results obtained from TextQuest, while at the same time provides additional insights into the clusters and the corresponding documents. Source code in perl and R are available from http://tartara.csd.auth.gr/~theodos/
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Lee, Kyubum; Kim, Byounggun; Jeon, Minji; Kim, Jihye; Tan, Aik Choon
2018-01-01
Background With the development of artificial intelligence (AI) technology centered on deep-learning, the computer has evolved to a point where it can read a given text and answer a question based on the context of the text. Such a specific task is known as the task of machine comprehension. Existing machine comprehension tasks mostly use datasets of general texts, such as news articles or elementary school-level storybooks. However, no attempt has been made to determine whether an up-to-date deep learning-based machine comprehension model can also process scientific literature containing expert-level knowledge, especially in the biomedical domain. Objective This study aims to investigate whether a machine comprehension model can process biomedical articles as well as general texts. Since there is no dataset for the biomedical literature comprehension task, our work includes generating a large-scale question answering dataset using PubMed and manually evaluating the generated dataset. Methods We present an attention-based deep neural model tailored to the biomedical domain. To further enhance the performance of our model, we used a pretrained word vector and biomedical entity type embedding. We also developed an ensemble method of combining the results of several independent models to reduce the variance of the answers from the models. Results The experimental results showed that our proposed deep neural network model outperformed the baseline model by more than 7% on the new dataset. We also evaluated human performance on the new dataset. The human evaluation result showed that our deep neural model outperformed humans in comprehension by 22% on average. Conclusions In this work, we introduced a new task of machine comprehension in the biomedical domain using a deep neural model. Since there was no large-scale dataset for training deep neural models in the biomedical domain, we created the new cloze-style datasets Biomedical Knowledge Comprehension Title (BMKC_T) and Biomedical Knowledge Comprehension Last Sentence (BMKC_LS) (together referred to as BioMedical Knowledge Comprehension) using the PubMed corpus. The experimental results showed that the performance of our model is much higher than that of humans. We observed that our model performed consistently better regardless of the degree of difficulty of a text, whereas humans have difficulty when performing biomedical literature comprehension tasks that require expert level knowledge. PMID:29305341
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Spectral signature verification using statistical analysis and text mining
NASA Astrophysics Data System (ADS)
DeCoster, Mallory E.; Firpi, Alexe H.; Jacobs, Samantha K.; Cone, Shelli R.; Tzeng, Nigel H.; Rodriguez, Benjamin M.
2016-05-01
In the spectral science community, numerous spectral signatures are stored in databases representative of many sample materials collected from a variety of spectrometers and spectroscopists. Due to the variety and variability of the spectra that comprise many spectral databases, it is necessary to establish a metric for validating the quality of spectral signatures. This has been an area of great discussion and debate in the spectral science community. This paper discusses a method that independently validates two different aspects of a spectral signature to arrive at a final qualitative assessment; the textual meta-data and numerical spectral data. Results associated with the spectral data stored in the Signature Database1 (SigDB) are proposed. The numerical data comprising a sample material's spectrum is validated based on statistical properties derived from an ideal population set. The quality of the test spectrum is ranked based on a spectral angle mapper (SAM) comparison to the mean spectrum derived from the population set. Additionally, the contextual data of a test spectrum is qualitatively analyzed using lexical analysis text mining. This technique analyzes to understand the syntax of the meta-data to provide local learning patterns and trends within the spectral data, indicative of the test spectrum's quality. Text mining applications have successfully been implemented for security2 (text encryption/decryption), biomedical3 , and marketing4 applications. The text mining lexical analysis algorithm is trained on the meta-data patterns of a subset of high and low quality spectra, in order to have a model to apply to the entire SigDB data set. The statistical and textual methods combine to assess the quality of a test spectrum existing in a database without the need of an expert user. This method has been compared to other validation methods accepted by the spectral science community, and has provided promising results when a baseline spectral signature is present for comparison. The spectral validation method proposed is described from a practical application and analytical perspective.
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A token centric part-of-speech tagger for biomedical text.
Barrett, Neil; Weber-Jahnke, Jens
2014-05-01
Difficulties with part-of-speech (POS) tagging of biomedical text is accessing and annotating appropriate training corpora. These difficulties may result in POS taggers trained on corpora that differ from the tagger's target biomedical text (cross-domain tagging). In such cases where training and target corpora differ tagging accuracy decreases. This paper presents a POS tagger for cross-domain tagging called TcT. TcT estimates a tag's likelihood for a given token by combining token collocation probabilities and the token's tag probabilities calculated using a Naive Bayes classifier. We compared TcT to three POS taggers used in the biomedical domain (mxpost, Brill and TnT). We trained each tagger on a non-biomedical corpus and evaluated it on biomedical corpora. TcT was more accurate in cross-domain tagging than mxpost, Brill and TnT (respective averages 83.9, 81.0, 79.5 and 78.8). Our analysis of tagger performance suggests that lexical differences between corpora have more effect on tagging accuracy than originally considered by previous research work. Biomedical POS tagging algorithms may be modified to improve their cross-domain tagging accuracy without requiring extra training or large training data sets. Future work should reexamine POS tagging methods for biomedical text. This differs from the work to date that has focused on retraining existing POS taggers. Copyright © 2014 Elsevier B.V. All rights reserved.
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Improving imbalanced scientific text classification using sampling strategies and dictionaries.
Borrajo, L; Romero, R; Iglesias, E L; Redondo Marey, C M
2011-09-15
Many real applications have the imbalanced class distribution problem, where one of the classes is represented by a very small number of cases compared to the other classes. One of the systems affected are those related to the recovery and classification of scientific documentation. Sampling strategies such as Oversampling and Subsampling are popular in tackling the problem of class imbalance. In this work, we study their effects on three types of classifiers (Knn, SVM and Naive-Bayes) when they are applied to search on the PubMed scientific database. Another purpose of this paper is to study the use of dictionaries in the classification of biomedical texts. Experiments are conducted with three different dictionaries (BioCreative, NLPBA, and an ad-hoc subset of the UniProt database named Protein) using the mentioned classifiers and sampling strategies. Best results were obtained with NLPBA and Protein dictionaries and the SVM classifier using the Subsampling balancing technique. These results were compared with those obtained by other authors using the TREC Genomics 2005 public corpus. Copyright 2011 The Author(s). Published by Journal of Integrative Bioinformatics.
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PIPE: a protein–protein interaction passage extraction module for BioCreative challenge
Chu, Chun-Han; Su, Yu-Chen; Chen, Chien Chin; Hsu, Wen-Lian
2016-01-01
Identifying the interactions between proteins mentioned in biomedical literatures is one of the frequently discussed topics of text mining in the life science field. In this article, we propose PIPE, an interaction pattern generation module used in the Collaborative Biocurator Assistant Task at BioCreative V (http://www.biocreative.org/) to capture frequent protein-protein interaction (PPI) patterns within text. We also present an interaction pattern tree (IPT) kernel method that integrates the PPI patterns with convolution tree kernel (CTK) to extract PPIs. Methods were evaluated on LLL, IEPA, HPRD50, AIMed and BioInfer corpora using cross-validation, cross-learning and cross-corpus evaluation. Empirical evaluations demonstrate that our method is effective and outperforms several well-known PPI extraction methods. Database URL: PMID:27524807
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Enhancing Biomedical Text Summarization Using Semantic Relation Extraction
Shang, Yue; Li, Yanpeng; Lin, Hongfei; Yang, Zhihao
2011-01-01
Automatic text summarization for a biomedical concept can help researchers to get the key points of a certain topic from large amount of biomedical literature efficiently. In this paper, we present a method for generating text summary for a given biomedical concept, e.g., H1N1 disease, from multiple documents based on semantic relation extraction. Our approach includes three stages: 1) We extract semantic relations in each sentence using the semantic knowledge representation tool SemRep. 2) We develop a relation-level retrieval method to select the relations most relevant to each query concept and visualize them in a graphic representation. 3) For relations in the relevant set, we extract informative sentences that can interpret them from the document collection to generate text summary using an information retrieval based method. Our major focus in this work is to investigate the contribution of semantic relation extraction to the task of biomedical text summarization. The experimental results on summarization for a set of diseases show that the introduction of semantic knowledge improves the performance and our results are better than the MEAD system, a well-known tool for text summarization. PMID:21887336
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Clique-based data mining for related genes in a biomedical database.
Matsunaga, Tsutomu; Yonemori, Chikara; Tomita, Etsuji; Muramatsu, Masaaki
2009-07-01
Progress in the life sciences cannot be made without integrating biomedical knowledge on numerous genes in order to help formulate hypotheses on the genetic mechanisms behind various biological phenomena, including diseases. There is thus a strong need for a way to automatically and comprehensively search from biomedical databases for related genes, such as genes in the same families and genes encoding components of the same pathways. Here we address the extraction of related genes by searching for densely-connected subgraphs, which are modeled as cliques, in a biomedical relational graph. We constructed a graph whose nodes were gene or disease pages, and edges were the hyperlink connections between those pages in the Online Mendelian Inheritance in Man (OMIM) database. We obtained over 20,000 sets of related genes (called 'gene modules') by enumerating cliques computationally. The modules included genes in the same family, genes for proteins that form a complex, and genes for components of the same signaling pathway. The results of experiments using 'metabolic syndrome'-related gene modules show that the gene modules can be used to get a coherent holistic picture helpful for interpreting relations among genes. We presented a data mining approach extracting related genes by enumerating cliques. The extracted gene sets provide a holistic picture useful for comprehending complex disease mechanisms.
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Biomedical databases: protecting privacy and promoting research.
Wylie, Jean E; Mineau, Geraldine P
2003-03-01
When combined with medical information, large electronic databases of information that identify individuals provide superlative resources for genetic, epidemiology and other biomedical research. Such research resources increasingly need to balance the protection of privacy and confidentiality with the promotion of research. Models that do not allow the use of such individual-identifying information constrain research; models that involve commercial interests raise concerns about what type of access is acceptable. Researchers, individuals representing the public interest and those developing regulatory guidelines must be involved in an ongoing dialogue to identify practical models.
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Agarwal, Shashank; Yu, Hong
2009-12-01
Biomedical texts can be typically represented by four rhetorical categories: Introduction, Methods, Results and Discussion (IMRAD). Classifying sentences into these categories can benefit many other text-mining tasks. Although many studies have applied different approaches for automatically classifying sentences in MEDLINE abstracts into the IMRAD categories, few have explored the classification of sentences that appear in full-text biomedical articles. We first evaluated whether sentences in full-text biomedical articles could be reliably annotated into the IMRAD format and then explored different approaches for automatically classifying these sentences into the IMRAD categories. Our results show an overall annotation agreement of 82.14% with a Kappa score of 0.756. The best classification system is a multinomial naïve Bayes classifier trained on manually annotated data that achieved 91.95% accuracy and an average F-score of 91.55%, which is significantly higher than baseline systems. A web version of this system is available online at-http://wood.ims.uwm.edu/full_text_classifier/.
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A Bioinformatics Module for Use in an Introductory Biology Laboratory
ERIC Educational Resources Information Center
Alaie, Adrienne; Teller, Virginia; Qiu, Wei-gang
2012-01-01
Since biomedical science has become increasingly data-intensive, acquisition of computational and quantitative skills by science students has become more important. For non-science students, an introduction to biomedical databases and their applications promotes the development of a scientifically literate population. Because typical college…
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The GAAIN Entity Mapper: An Active-Learning System for Medical Data Mapping.
Ashish, Naveen; Dewan, Peehoo; Toga, Arthur W
2015-01-01
This work is focused on mapping biomedical datasets to a common representation, as an integral part of data harmonization for integrated biomedical data access and sharing. We present GEM, an intelligent software assistant for automated data mapping across different datasets or from a dataset to a common data model. The GEM system automates data mapping by providing precise suggestions for data element mappings. It leverages the detailed metadata about elements in associated dataset documentation such as data dictionaries that are typically available with biomedical datasets. It employs unsupervised text mining techniques to determine similarity between data elements and also employs machine-learning classifiers to identify element matches. It further provides an active-learning capability where the process of training the GEM system is optimized. Our experimental evaluations show that the GEM system provides highly accurate data mappings (over 90% accuracy) for real datasets of thousands of data elements each, in the Alzheimer's disease research domain. Further, the effort in training the system for new datasets is also optimized. We are currently employing the GEM system to map Alzheimer's disease datasets from around the globe into a common representation, as part of a global Alzheimer's disease integrated data sharing and analysis network called GAAIN. GEM achieves significantly higher data mapping accuracy for biomedical datasets compared to other state-of-the-art tools for database schema matching that have similar functionality. With the use of active-learning capabilities, the user effort in training the system is minimal.
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The GAAIN Entity Mapper: An Active-Learning System for Medical Data Mapping
Ashish, Naveen; Dewan, Peehoo; Toga, Arthur W.
2016-01-01
This work is focused on mapping biomedical datasets to a common representation, as an integral part of data harmonization for integrated biomedical data access and sharing. We present GEM, an intelligent software assistant for automated data mapping across different datasets or from a dataset to a common data model. The GEM system automates data mapping by providing precise suggestions for data element mappings. It leverages the detailed metadata about elements in associated dataset documentation such as data dictionaries that are typically available with biomedical datasets. It employs unsupervised text mining techniques to determine similarity between data elements and also employs machine-learning classifiers to identify element matches. It further provides an active-learning capability where the process of training the GEM system is optimized. Our experimental evaluations show that the GEM system provides highly accurate data mappings (over 90% accuracy) for real datasets of thousands of data elements each, in the Alzheimer's disease research domain. Further, the effort in training the system for new datasets is also optimized. We are currently employing the GEM system to map Alzheimer's disease datasets from around the globe into a common representation, as part of a global Alzheimer's disease integrated data sharing and analysis network called GAAIN1. GEM achieves significantly higher data mapping accuracy for biomedical datasets compared to other state-of-the-art tools for database schema matching that have similar functionality. With the use of active-learning capabilities, the user effort in training the system is minimal. PMID:26793094
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USDA-ARS?s Scientific Manuscript database
The use of swine in biomedical research has increased dramatically in the last decade. Diverse genomic- and proteomic databases have been developed to facilitate research using human and rodent models. Current porcine gene databases, however, lack the robust annotation to study pig models that are...
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Blockchain distributed ledger technologies for biomedical and health care applications.
Kuo, Tsung-Ting; Kim, Hyeon-Eui; Ohno-Machado, Lucila
2017-11-01
To introduce blockchain technologies, including their benefits, pitfalls, and the latest applications, to the biomedical and health care domains. Biomedical and health care informatics researchers who would like to learn about blockchain technologies and their applications in the biomedical/health care domains. The covered topics include: (1) introduction to the famous Bitcoin crypto-currency and the underlying blockchain technology; (2) features of blockchain; (3) review of alternative blockchain technologies; (4) emerging nonfinancial distributed ledger technologies and applications; (5) benefits of blockchain for biomedical/health care applications when compared to traditional distributed databases; (6) overview of the latest biomedical/health care applications of blockchain technologies; and (7) discussion of the potential challenges and proposed solutions of adopting blockchain technologies in biomedical/health care domains. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association.
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Kim, Seongsoon; Park, Donghyeon; Choi, Yonghwa; Lee, Kyubum; Kim, Byounggun; Jeon, Minji; Kim, Jihye; Tan, Aik Choon; Kang, Jaewoo
2018-01-05
With the development of artificial intelligence (AI) technology centered on deep-learning, the computer has evolved to a point where it can read a given text and answer a question based on the context of the text. Such a specific task is known as the task of machine comprehension. Existing machine comprehension tasks mostly use datasets of general texts, such as news articles or elementary school-level storybooks. However, no attempt has been made to determine whether an up-to-date deep learning-based machine comprehension model can also process scientific literature containing expert-level knowledge, especially in the biomedical domain. This study aims to investigate whether a machine comprehension model can process biomedical articles as well as general texts. Since there is no dataset for the biomedical literature comprehension task, our work includes generating a large-scale question answering dataset using PubMed and manually evaluating the generated dataset. We present an attention-based deep neural model tailored to the biomedical domain. To further enhance the performance of our model, we used a pretrained word vector and biomedical entity type embedding. We also developed an ensemble method of combining the results of several independent models to reduce the variance of the answers from the models. The experimental results showed that our proposed deep neural network model outperformed the baseline model by more than 7% on the new dataset. We also evaluated human performance on the new dataset. The human evaluation result showed that our deep neural model outperformed humans in comprehension by 22% on average. In this work, we introduced a new task of machine comprehension in the biomedical domain using a deep neural model. Since there was no large-scale dataset for training deep neural models in the biomedical domain, we created the new cloze-style datasets Biomedical Knowledge Comprehension Title (BMKC_T) and Biomedical Knowledge Comprehension Last Sentence (BMKC_LS) (together referred to as BioMedical Knowledge Comprehension) using the PubMed corpus. The experimental results showed that the performance of our model is much higher than that of humans. We observed that our model performed consistently better regardless of the degree of difficulty of a text, whereas humans have difficulty when performing biomedical literature comprehension tasks that require expert level knowledge. ©Seongsoon Kim, Donghyeon Park, Yonghwa Choi, Kyubum Lee, Byounggun Kim, Minji Jeon, Jihye Kim, Aik Choon Tan, Jaewoo Kang. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 05.01.2018.
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StemTextSearch: Stem cell gene database with evidence from abstracts.
Chen, Chou-Cheng; Ho, Chung-Liang
2017-05-01
Previous studies have used many methods to find biomarkers in stem cells, including text mining, experimental data and image storage. However, no text-mining methods have yet been developed which can identify whether a gene plays a positive or negative role in stem cells. StemTextSearch identifies the role of a gene in stem cells by using a text-mining method to find combinations of gene regulation, stem-cell regulation and cell processes in the same sentences of biomedical abstracts. The dataset includes 5797 genes, with 1534 genes having positive roles in stem cells, 1335 genes having negative roles, 1654 genes with both positive and negative roles, and 1274 with an uncertain role. The precision of gene role in StemTextSearch is 0.66, and the recall is 0.78. StemTextSearch is a web-based engine with queries that specify (i) gene, (ii) category of stem cell, (iii) gene role, (iv) gene regulation, (v) cell process, (vi) stem-cell regulation, and (vii) species. StemTextSearch is available through http://bio.yungyun.com.tw/StemTextSearch.aspx. Copyright © 2017. Published by Elsevier Inc.
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Glonti, Ketevan; Cauchi, Daniel; Cobo, Erik; Boutron, Isabelle; Moher, David; Hren, Darko
2017-10-22
The primary functions of peer reviewers are poorly defined. Thus far no body of literature has systematically identified the roles and tasks of peer reviewers of biomedical journals. A clear establishment of these can lead to improvements in the peer review process. The purpose of this scoping review is to determine what is known on the roles and tasks of peer reviewers. We will use the methodological framework first proposed by Arksey and O'Malley and subsequently adapted by Levac et al and the Joanna Briggs Institute. The scoping review will include all study designs, as well as editorials, commentaries and grey literature. The following eight electronic databases will be searched (from inception to May 2017): Cochrane Library, Cumulative Index to Nursing and Allied Health Literature, Educational Resources Information Center, EMBASE, MEDLINE, PsycINFO, Scopus and Web of Science. Two reviewers will use inclusion and exclusion criteria based on the 'Population-Concept-Context' framework to independently screen titles and abstracts of articles considered for inclusion. Full-text screening of relevant eligible articles will also be carried out by two reviewers. The search strategy for grey literature will include searching in websites of existing networks, biomedical journal publishers and organisations that offer resources for peer reviewers. In addition we will review journal guidelines to peer reviewers on how to perform the manuscript review. Journals will be selected using the 2016 journal impact factor. We will identify and assess the top five, middle five and lowest-ranking five journals across all medical specialties. This scoping review will undertake a secondary analysis of data already collected and does not require ethical approval. The results will be disseminated through journals and conferences targeting stakeholders involved in peer review in biomedical research. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.
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UMLS content views appropriate for NLP processing of the biomedical literature vs. clinical text.
Demner-Fushman, Dina; Mork, James G; Shooshan, Sonya E; Aronson, Alan R
2010-08-01
Identification of medical terms in free text is a first step in such Natural Language Processing (NLP) tasks as automatic indexing of biomedical literature and extraction of patients' problem lists from the text of clinical notes. Many tools developed to perform these tasks use biomedical knowledge encoded in the Unified Medical Language System (UMLS) Metathesaurus. We continue our exploration of automatic approaches to creation of subsets (UMLS content views) which can support NLP processing of either the biomedical literature or clinical text. We found that suppression of highly ambiguous terms in the conservative AutoFilter content view can partially replace manual filtering for literature applications, and suppression of two character mappings in the same content view achieves 89.5% precision at 78.6% recall for clinical applications. Published by Elsevier Inc.
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Munkhdalai, Tsendsuren; Li, Meijing; Batsuren, Khuyagbaatar; Park, Hyeon Ah; Choi, Nak Hyeon; Ryu, Keun Ho
2015-01-01
Chemical and biomedical Named Entity Recognition (NER) is an essential prerequisite task before effective text mining can begin for biochemical-text data. Exploiting unlabeled text data to leverage system performance has been an active and challenging research topic in text mining due to the recent growth in the amount of biomedical literature. We present a semi-supervised learning method that efficiently exploits unlabeled data in order to incorporate domain knowledge into a named entity recognition model and to leverage system performance. The proposed method includes Natural Language Processing (NLP) tasks for text preprocessing, learning word representation features from a large amount of text data for feature extraction, and conditional random fields for token classification. Other than the free text in the domain, the proposed method does not rely on any lexicon nor any dictionary in order to keep the system applicable to other NER tasks in bio-text data. We extended BANNER, a biomedical NER system, with the proposed method. This yields an integrated system that can be applied to chemical and drug NER or biomedical NER. We call our branch of the BANNER system BANNER-CHEMDNER, which is scalable over millions of documents, processing about 530 documents per minute, is configurable via XML, and can be plugged into other systems by using the BANNER Unstructured Information Management Architecture (UIMA) interface. BANNER-CHEMDNER achieved an 85.68% and an 86.47% F-measure on the testing sets of CHEMDNER Chemical Entity Mention (CEM) and Chemical Document Indexing (CDI) subtasks, respectively, and achieved an 87.04% F-measure on the official testing set of the BioCreative II gene mention task, showing remarkable performance in both chemical and biomedical NER. BANNER-CHEMDNER system is available at: https://bitbucket.org/tsendeemts/banner-chemdner.
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Zheng, Wu; Blake, Catherine
2015-10-01
Databases of curated biomedical knowledge, such as the protein-locations reflected in the UniProtKB database, provide an accurate and useful resource to researchers and decision makers. Our goal is to augment the manual efforts currently used to curate knowledge bases with automated approaches that leverage the increased availability of full-text scientific articles. This paper describes experiments that use distant supervised learning to identify protein subcellular localizations, which are important to understand protein function and to identify candidate drug targets. Experiments consider Swiss-Prot, the manually annotated subset of the UniProtKB protein knowledge base, and 43,000 full-text articles from the Journal of Biological Chemistry that contain just under 11.5 million sentences. The system achieves 0.81 precision and 0.49 recall at sentence level and an accuracy of 57% on held-out instances in a test set. Moreover, the approach identifies 8210 instances that are not in the UniProtKB knowledge base. Manual inspection of the 50 most likely relations showed that 41 (82%) were valid. These results have immediate benefit to researchers interested in protein function, and suggest that distant supervision should be explored to complement other manual data curation efforts. Copyright © 2015 Elsevier Inc. All rights reserved.
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[Application of the life sciences platform based on oracle to biomedical informations].
Zhao, Zhi-Yun; Li, Tai-Huan; Yang, Hong-Qiao
2008-03-01
The life sciences platform based on Oracle database technology is introduced in this paper. By providing a powerful data access, integrating a variety of data types, and managing vast quantities of data, the software presents a flexible, safe and scalable management platform for biomedical data processing.
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Biomedical text mining and its applications in cancer research.
Zhu, Fei; Patumcharoenpol, Preecha; Zhang, Cheng; Yang, Yang; Chan, Jonathan; Meechai, Asawin; Vongsangnak, Wanwipa; Shen, Bairong
2013-04-01
Cancer is a malignant disease that has caused millions of human deaths. Its study has a long history of well over 100years. There have been an enormous number of publications on cancer research. This integrated but unstructured biomedical text is of great value for cancer diagnostics, treatment, and prevention. The immense body and rapid growth of biomedical text on cancer has led to the appearance of a large number of text mining techniques aimed at extracting novel knowledge from scientific text. Biomedical text mining on cancer research is computationally automatic and high-throughput in nature. However, it is error-prone due to the complexity of natural language processing. In this review, we introduce the basic concepts underlying text mining and examine some frequently used algorithms, tools, and data sets, as well as assessing how much these algorithms have been utilized. We then discuss the current state-of-the-art text mining applications in cancer research and we also provide some resources for cancer text mining. With the development of systems biology, researchers tend to understand complex biomedical systems from a systems biology viewpoint. Thus, the full utilization of text mining to facilitate cancer systems biology research is fast becoming a major concern. To address this issue, we describe the general workflow of text mining in cancer systems biology and each phase of the workflow. We hope that this review can (i) provide a useful overview of the current work of this field; (ii) help researchers to choose text mining tools and datasets; and (iii) highlight how to apply text mining to assist cancer systems biology research. Copyright © 2012 Elsevier Inc. All rights reserved.
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2011-01-01
Background Tokenization is an important component of language processing yet there is no widely accepted tokenization method for English texts, including biomedical texts. Other than rule based techniques, tokenization in the biomedical domain has been regarded as a classification task. Biomedical classifier-based tokenizers either split or join textual objects through classification to form tokens. The idiosyncratic nature of each biomedical tokenizer’s output complicates adoption and reuse. Furthermore, biomedical tokenizers generally lack guidance on how to apply an existing tokenizer to a new domain (subdomain). We identify and complete a novel tokenizer design pattern and suggest a systematic approach to tokenizer creation. We implement a tokenizer based on our design pattern that combines regular expressions and machine learning. Our machine learning approach differs from the previous split-join classification approaches. We evaluate our approach against three other tokenizers on the task of tokenizing biomedical text. Results Medpost and our adapted Viterbi tokenizer performed best with a 92.9% and 92.4% accuracy respectively. Conclusions Our evaluation of our design pattern and guidelines supports our claim that the design pattern and guidelines are a viable approach to tokenizer construction (producing tokenizers matching leading custom-built tokenizers in a particular domain). Our evaluation also demonstrates that ambiguous tokenizations can be disambiguated through POS tagging. In doing so, POS tag sequences and training data have a significant impact on proper text tokenization. PMID:21658288
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Semantic annotation in biomedicine: the current landscape.
Jovanović, Jelena; Bagheri, Ebrahim
2017-09-22
The abundance and unstructured nature of biomedical texts, be it clinical or research content, impose significant challenges for the effective and efficient use of information and knowledge stored in such texts. Annotation of biomedical documents with machine intelligible semantics facilitates advanced, semantics-based text management, curation, indexing, and search. This paper focuses on annotation of biomedical entity mentions with concepts from relevant biomedical knowledge bases such as UMLS. As a result, the meaning of those mentions is unambiguously and explicitly defined, and thus made readily available for automated processing. This process is widely known as semantic annotation, and the tools that perform it are known as semantic annotators.Over the last dozen years, the biomedical research community has invested significant efforts in the development of biomedical semantic annotation technology. Aiming to establish grounds for further developments in this area, we review a selected set of state of the art biomedical semantic annotators, focusing particularly on general purpose annotators, that is, semantic annotation tools that can be customized to work with texts from any area of biomedicine. We also examine potential directions for further improvements of today's annotators which could make them even more capable of meeting the needs of real-world applications. To motivate and encourage further developments in this area, along the suggested and/or related directions, we review existing and potential practical applications and benefits of semantic annotators.
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PubMedPortable: A Framework for Supporting the Development of Text Mining Applications.
Döring, Kersten; Grüning, Björn A; Telukunta, Kiran K; Thomas, Philippe; Günther, Stefan
2016-01-01
Information extraction from biomedical literature is continuously growing in scope and importance. Many tools exist that perform named entity recognition, e.g. of proteins, chemical compounds, and diseases. Furthermore, several approaches deal with the extraction of relations between identified entities. The BioCreative community supports these developments with yearly open challenges, which led to a standardised XML text annotation format called BioC. PubMed provides access to the largest open biomedical literature repository, but there is no unified way of connecting its data to natural language processing tools. Therefore, an appropriate data environment is needed as a basis to combine different software solutions and to develop customised text mining applications. PubMedPortable builds a relational database and a full text index on PubMed citations. It can be applied either to the complete PubMed data set or an arbitrary subset of downloaded PubMed XML files. The software provides the infrastructure to combine stand-alone applications by exporting different data formats, e.g. BioC. The presented workflows show how to use PubMedPortable to retrieve, store, and analyse a disease-specific data set. The provided use cases are well documented in the PubMedPortable wiki. The open-source software library is small, easy to use, and scalable to the user's system requirements. It is freely available for Linux on the web at https://github.com/KerstenDoering/PubMedPortable and for other operating systems as a virtual container. The approach was tested extensively and applied successfully in several projects.
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PubMedPortable: A Framework for Supporting the Development of Text Mining Applications
Döring, Kersten; Grüning, Björn A.; Telukunta, Kiran K.; Thomas, Philippe; Günther, Stefan
2016-01-01
Information extraction from biomedical literature is continuously growing in scope and importance. Many tools exist that perform named entity recognition, e.g. of proteins, chemical compounds, and diseases. Furthermore, several approaches deal with the extraction of relations between identified entities. The BioCreative community supports these developments with yearly open challenges, which led to a standardised XML text annotation format called BioC. PubMed provides access to the largest open biomedical literature repository, but there is no unified way of connecting its data to natural language processing tools. Therefore, an appropriate data environment is needed as a basis to combine different software solutions and to develop customised text mining applications. PubMedPortable builds a relational database and a full text index on PubMed citations. It can be applied either to the complete PubMed data set or an arbitrary subset of downloaded PubMed XML files. The software provides the infrastructure to combine stand-alone applications by exporting different data formats, e.g. BioC. The presented workflows show how to use PubMedPortable to retrieve, store, and analyse a disease-specific data set. The provided use cases are well documented in the PubMedPortable wiki. The open-source software library is small, easy to use, and scalable to the user’s system requirements. It is freely available for Linux on the web at https://github.com/KerstenDoering/PubMedPortable and for other operating systems as a virtual container. The approach was tested extensively and applied successfully in several projects. PMID:27706202
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Discovering and visualizing indirect associations between biomedical concepts
Tsuruoka, Yoshimasa; Miwa, Makoto; Hamamoto, Kaisei; Tsujii, Jun'ichi; Ananiadou, Sophia
2011-01-01
Motivation: Discovering useful associations between biomedical concepts has been one of the main goals in biomedical text-mining, and understanding their biomedical contexts is crucial in the discovery process. Hence, we need a text-mining system that helps users explore various types of (possibly hidden) associations in an easy and comprehensible manner. Results: This article describes FACTA+, a real-time text-mining system for finding and visualizing indirect associations between biomedical concepts from MEDLINE abstracts. The system can be used as a text search engine like PubMed with additional features to help users discover and visualize indirect associations between important biomedical concepts such as genes, diseases and chemical compounds. FACTA+ inherits all functionality from its predecessor, FACTA, and extends it by incorporating three new features: (i) detecting biomolecular events in text using a machine learning model, (ii) discovering hidden associations using co-occurrence statistics between concepts, and (iii) visualizing associations to improve the interpretability of the output. To the best of our knowledge, FACTA+ is the first real-time web application that offers the functionality of finding concepts involving biomolecular events and visualizing indirect associations of concepts with both their categories and importance. Availability: FACTA+ is available as a web application at http://refine1-nactem.mc.man.ac.uk/facta/, and its visualizer is available at http://refine1-nactem.mc.man.ac.uk/facta-visualizer/. Contact: tsuruoka@jaist.ac.jp PMID:21685059
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2012-01-01
Background We introduce the linguistic annotation of a corpus of 97 full-text biomedical publications, known as the Colorado Richly Annotated Full Text (CRAFT) corpus. We further assess the performance of existing tools for performing sentence splitting, tokenization, syntactic parsing, and named entity recognition on this corpus. Results Many biomedical natural language processing systems demonstrated large differences between their previously published results and their performance on the CRAFT corpus when tested with the publicly available models or rule sets. Trainable systems differed widely with respect to their ability to build high-performing models based on this data. Conclusions The finding that some systems were able to train high-performing models based on this corpus is additional evidence, beyond high inter-annotator agreement, that the quality of the CRAFT corpus is high. The overall poor performance of various systems indicates that considerable work needs to be done to enable natural language processing systems to work well when the input is full-text journal articles. The CRAFT corpus provides a valuable resource to the biomedical natural language processing community for evaluation and training of new models for biomedical full text publications. PMID:22901054
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A multi-ontology approach to annotate scientific documents based on a modularization technique.
Gomes, Priscilla Corrêa E Castro; Moura, Ana Maria de Carvalho; Cavalcanti, Maria Cláudia
2015-12-01
Scientific text annotation has become an important task for biomedical scientists. Nowadays, there is an increasing need for the development of intelligent systems to support new scientific findings. Public databases available on the Web provide useful data, but much more useful information is only accessible in scientific texts. Text annotation may help as it relies on the use of ontologies to maintain annotations based on a uniform vocabulary. However, it is difficult to use an ontology, especially those that cover a large domain. In addition, since scientific texts explore multiple domains, which are covered by distinct ontologies, it becomes even more difficult to deal with such task. Moreover, there are dozens of ontologies in the biomedical area, and they are usually big in terms of the number of concepts. It is in this context that ontology modularization can be useful. This work presents an approach to annotate scientific documents using modules of different ontologies, which are built according to a module extraction technique. The main idea is to analyze a set of single-ontology annotations on a text to find out the user interests. Based on these annotations a set of modules are extracted from a set of distinct ontologies, and are made available for the user, for complementary annotation. The reduced size and focus of the extracted modules tend to facilitate the annotation task. An experiment was conducted to evaluate this approach, with the participation of a bioinformatician specialist of the Laboratory of Peptides and Proteins of the IOC/Fiocruz, who was interested in discovering new drug targets aiming at the combat of tropical diseases. Copyright © 2015 Elsevier Inc. All rights reserved.
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Constructing a Graph Database for Semantic Literature-Based Discovery.
Hristovski, Dimitar; Kastrin, Andrej; Dinevski, Dejan; Rindflesch, Thomas C
2015-01-01
Literature-based discovery (LBD) generates discoveries, or hypotheses, by combining what is already known in the literature. Potential discoveries have the form of relations between biomedical concepts; for example, a drug may be determined to treat a disease other than the one for which it was intended. LBD views the knowledge in a domain as a network; a set of concepts along with the relations between them. As a starting point, we used SemMedDB, a database of semantic relations between biomedical concepts extracted with SemRep from Medline. SemMedDB is distributed as a MySQL relational database, which has some problems when dealing with network data. We transformed and uploaded SemMedDB into the Neo4j graph database, and implemented the basic LBD discovery algorithms with the Cypher query language. We conclude that storing the data needed for semantic LBD is more natural in a graph database. Also, implementing LBD discovery algorithms is conceptually simpler with a graph query language when compared with standard SQL.
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Elayavilli, Ravikumar Komandur; Liu, Hongfang
2016-01-01
Computational modeling of biological cascades is of great interest to quantitative biologists. Biomedical text has been a rich source for quantitative information. Gathering quantitative parameters and values from biomedical text is one significant challenge in the early steps of computational modeling as it involves huge manual effort. While automatically extracting such quantitative information from bio-medical text may offer some relief, lack of ontological representation for a subdomain serves as impedance in normalizing textual extractions to a standard representation. This may render textual extractions less meaningful to the domain experts. In this work, we propose a rule-based approach to automatically extract relations involving quantitative data from biomedical text describing ion channel electrophysiology. We further translated the quantitative assertions extracted through text mining to a formal representation that may help in constructing ontology for ion channel events using a rule based approach. We have developed Ion Channel ElectroPhysiology Ontology (ICEPO) by integrating the information represented in closely related ontologies such as, Cell Physiology Ontology (CPO), and Cardiac Electro Physiology Ontology (CPEO) and the knowledge provided by domain experts. The rule-based system achieved an overall F-measure of 68.93% in extracting the quantitative data assertions system on an independently annotated blind data set. We further made an initial attempt in formalizing the quantitative data assertions extracted from the biomedical text into a formal representation that offers potential to facilitate the integration of text mining into ontological workflow, a novel aspect of this study. This work is a case study where we created a platform that provides formal interaction between ontology development and text mining. We have achieved partial success in extracting quantitative assertions from the biomedical text and formalizing them in ontological framework. The ICEPO ontology is available for download at http://openbionlp.org/mutd/supplementarydata/ICEPO/ICEPO.owl.
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Frontiers of biomedical text mining: current progress
Zweigenbaum, Pierre; Demner-Fushman, Dina; Yu, Hong; Cohen, Kevin B.
2008-01-01
It is now almost 15 years since the publication of the first paper on text mining in the genomics domain, and decades since the first paper on text mining in the medical domain. Enormous progress has been made in the areas of information retrieval, evaluation methodologies and resource construction. Some problems, such as abbreviation-handling, can essentially be considered solved problems, and others, such as identification of gene mentions in text, seem likely to be solved soon. However, a number of problems at the frontiers of biomedical text mining continue to present interesting challenges and opportunities for great improvements and interesting research. In this article we review the current state of the art in biomedical text mining or ‘BioNLP’ in general, focusing primarily on papers published within the past year. PMID:17977867
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DISEASES: text mining and data integration of disease-gene associations.
Pletscher-Frankild, Sune; Pallejà, Albert; Tsafou, Kalliopi; Binder, Janos X; Jensen, Lars Juhl
2015-03-01
Text mining is a flexible technology that can be applied to numerous different tasks in biology and medicine. We present a system for extracting disease-gene associations from biomedical abstracts. The system consists of a highly efficient dictionary-based tagger for named entity recognition of human genes and diseases, which we combine with a scoring scheme that takes into account co-occurrences both within and between sentences. We show that this approach is able to extract half of all manually curated associations with a false positive rate of only 0.16%. Nonetheless, text mining should not stand alone, but be combined with other types of evidence. For this reason, we have developed the DISEASES resource, which integrates the results from text mining with manually curated disease-gene associations, cancer mutation data, and genome-wide association studies from existing databases. The DISEASES resource is accessible through a web interface at http://diseases.jensenlab.org/, where the text-mining software and all associations are also freely available for download. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.
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Olelo: a web application for intuitive exploration of biomedical literature
Niedermeier, Julian; Jankrift, Marcel; Tietböhl, Sören; Stachewicz, Toni; Folkerts, Hendrik; Uflacker, Matthias; Neves, Mariana
2017-01-01
Abstract Researchers usually query the large biomedical literature in PubMed via keywords, logical operators and filters, none of which is very intuitive. Question answering systems are an alternative to keyword searches. They allow questions in natural language as input and results reflect the given type of question, such as short answers and summaries. Few of those systems are available online but they experience drawbacks in terms of long response times and they support a limited amount of question and result types. Additionally, user interfaces are usually restricted to only displaying the retrieved information. For our Olelo web application, we combined biomedical literature and terminologies in a fast in-memory database to enable real-time responses to researchers’ queries. Further, we extended the built-in natural language processing features of the database with question answering and summarization procedures. Combined with a new explorative approach of document filtering and a clean user interface, Olelo enables a fast and intelligent search through the ever-growing biomedical literature. Olelo is available at http://www.hpi.de/plattner/olelo. PMID:28472397
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Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders.
Hamosh, Ada; Scott, Alan F; Amberger, Joanna; Bocchini, Carol; Valle, David; McKusick, Victor A
2002-01-01
Online Mendelian Inheritance in Man (OMIM) is a comprehensive, authoritative and timely knowledgebase of human genes and genetic disorders compiled to support research and education in human genomics and the practice of clinical genetics. Started by Dr Victor A. McKusick as the definitive reference Mendelian Inheritance in Man, OMIM (www.ncbi.nlm.nih.gov/omim) is now distributed electronically by the National Center for Biotechnology Information (NCBI), where it is integrated with the Entrez suite of databases. Derived from the biomedical literature, OMIM is written and edited at Johns Hopkins University with input from scientists and physicians around the world. Each OMIM entry has a full-text summary of a genetically determined phenotype and/or gene and has numerous links to other genetic databases such as DNA and protein sequence, PubMed references, general and locus-specific mutation databases, approved gene nomenclature, and the highly detailed mapviewer, as well as patient support groups and many others. OMIM is an easy and straightforward portal to the burgeoning information in human genetics.
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Mining biomedical images towards valuable information retrieval in biomedical and life sciences
Ahmed, Zeeshan; Zeeshan, Saman; Dandekar, Thomas
2016-01-01
Biomedical images are helpful sources for the scientists and practitioners in drawing significant hypotheses, exemplifying approaches and describing experimental results in published biomedical literature. In last decades, there has been an enormous increase in the amount of heterogeneous biomedical image production and publication, which results in a need for bioimaging platforms for feature extraction and analysis of text and content in biomedical images to take advantage in implementing effective information retrieval systems. In this review, we summarize technologies related to data mining of figures. We describe and compare the potential of different approaches in terms of their developmental aspects, used methodologies, produced results, achieved accuracies and limitations. Our comparative conclusions include current challenges for bioimaging software with selective image mining, embedded text extraction and processing of complex natural language queries. PMID:27538578
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Library of molecular associations: curating the complex molecular basis of liver diseases.
Buchkremer, Stefan; Hendel, Jasmin; Krupp, Markus; Weinmann, Arndt; Schlamp, Kai; Maass, Thorsten; Staib, Frank; Galle, Peter R; Teufel, Andreas
2010-03-20
Systems biology approaches offer novel insights into the development of chronic liver diseases. Current genomic databases supporting systems biology analyses are mostly based on microarray data. Although these data often cover genome wide expression, the validity of single microarray experiments remains questionable. However, for systems biology approaches addressing the interactions of molecular networks comprehensive but also highly validated data are necessary. We have therefore generated the first comprehensive database for published molecular associations in human liver diseases. It is based on PubMed published abstracts and aimed to close the gap between genome wide coverage of low validity from microarray data and individual highly validated data from PubMed. After an initial text mining process, the extracted abstracts were all manually validated to confirm content and potential genetic associations and may therefore be highly trusted. All data were stored in a publicly available database, Library of Molecular Associations http://www.medicalgenomics.org/databases/loma/news, currently holding approximately 1260 confirmed molecular associations for chronic liver diseases such as HCC, CCC, liver fibrosis, NASH/fatty liver disease, AIH, PBC, and PSC. We furthermore transformed these data into a powerful resource for molecular liver research by connecting them to multiple biomedical information resources. Together, this database is the first available database providing a comprehensive view and analysis options for published molecular associations on multiple liver diseases.
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Semantator: semantic annotator for converting biomedical text to linked data.
Tao, Cui; Song, Dezhao; Sharma, Deepak; Chute, Christopher G
2013-10-01
More than 80% of biomedical data is embedded in plain text. The unstructured nature of these text-based documents makes it challenging to easily browse and query the data of interest in them. One approach to facilitate browsing and querying biomedical text is to convert the plain text to a linked web of data, i.e., converting data originally in free text to structured formats with defined meta-level semantics. In this paper, we introduce Semantator (Semantic Annotator), a semantic-web-based environment for annotating data of interest in biomedical documents, browsing and querying the annotated data, and interactively refining annotation results if needed. Through Semantator, information of interest can be either annotated manually or semi-automatically using plug-in information extraction tools. The annotated results will be stored in RDF and can be queried using the SPARQL query language. In addition, semantic reasoners can be directly applied to the annotated data for consistency checking and knowledge inference. Semantator has been released online and was used by the biomedical ontology community who provided positive feedbacks. Our evaluation results indicated that (1) Semantator can perform the annotation functionalities as designed; (2) Semantator can be adopted in real applications in clinical and transactional research; and (3) the annotated results using Semantator can be easily used in Semantic-web-based reasoning tools for further inference. Copyright © 2013 Elsevier Inc. All rights reserved.
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Toward a Bio-Medical Thesaurus: Building the Foundation of the UMLS
Tuttle, Mark S.; Blois, Marsden S.; Erlbaum, Mark S.; Nelson, Stuart J.; Sherertz, David D.
1988-01-01
The Unified Medical Language System (UMLS) is being designed to provide a uniform user interface to heterogeneous machine-readable bio-medical information resources, such as bibliographic databases, genetic databases, expert systems and patient records.1 Such an interface will have to recognize different ways of saying the same thing, and provide links to ways of saying related things. One way to represent the necessary associations is via a domain thesaurus. As no such thesaurus exists, and because, once built, it will be both sizable and in need of continuous maintenance, its design should include a methodology for building and maintaining it. We propose a methodology, utilizing lexically expanded schema inversion, and a design, called T. Lex, which together form one approach to the problem of defining and building a bio-medical thesaurus. We argue that the semantic locality implicit in such a thesaurus will support model-based reasoning in bio-medicine.2
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Use of patient safety culture instruments in operating rooms: A systematic literature review.
Zhao, Pujng; Li, Yaqin; Li, Zhi; Jia, Pengli; Zhang, Longhao; Zhang, Mingming
2017-05-01
To identify and qualitatively describe, in a literature review, how the instruments were used to evaluate patient safety culture in the operating rooms of published studies. Systematic searches of the literature were conducted using the major database including MEDLINE, EMbase, The Cochrane Library, and four Chinese databases including Chinese Biomedical Literature Database (CBM), Wanfang Data, Chinese Scientific Journal Database (VIP), and Chinese Journals Full-text Database (CNKI) for studies published up to March 2016. We summarized and analyzed the country scope, the instrument utilized in the study, the year when the instrument was used, and fields of operating rooms. Study populations, study settings, and the time span between baseline and follow-up phase were evaluated according to the study design. We identified 1025 references, of which 99 were obtained for full-text assessment; 47 of these studies were deemed relevant and included in the literature review. Most of the studies were from the USA. The most commonly used patient safety culture instrument was Safety Attitude Questionnaire. All identified instruments were used after 2002 and across many fields. Most included studies on patient safety culture were conducted in teaching hospitals or university hospitals. The study population in the cross-sectional studies was much more than that in the before-after studies. The time span between baseline and follow-up phase of before-after studies were almost over three months. Although patient safety culture is considered important in health care and patient safety, the number of studies in which patient safety culture has been estimated using the instruments in operating rooms, is fairly small. © 2017 Chinese Cochrane Center, West China Hospital of Sichuan University and John Wiley & Sons Australia, Ltd.
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New directions in biomedical text annotation: definitions, guidelines and corpus construction
Wilbur, W John; Rzhetsky, Andrey; Shatkay, Hagit
2006-01-01
Background While biomedical text mining is emerging as an important research area, practical results have proven difficult to achieve. We believe that an important first step towards more accurate text-mining lies in the ability to identify and characterize text that satisfies various types of information needs. We report here the results of our inquiry into properties of scientific text that have sufficient generality to transcend the confines of a narrow subject area, while supporting practical mining of text for factual information. Our ultimate goal is to annotate a significant corpus of biomedical text and train machine learning methods to automatically categorize such text along certain dimensions that we have defined. Results We have identified five qualitative dimensions that we believe characterize a broad range of scientific sentences, and are therefore useful for supporting a general approach to text-mining: focus, polarity, certainty, evidence, and directionality. We define these dimensions and describe the guidelines we have developed for annotating text with regard to them. To examine the effectiveness of the guidelines, twelve annotators independently annotated the same set of 101 sentences that were randomly selected from current biomedical periodicals. Analysis of these annotations shows 70–80% inter-annotator agreement, suggesting that our guidelines indeed present a well-defined, executable and reproducible task. Conclusion We present our guidelines defining a text annotation task, along with annotation results from multiple independently produced annotations, demonstrating the feasibility of the task. The annotation of a very large corpus of documents along these guidelines is currently ongoing. These annotations form the basis for the categorization of text along multiple dimensions, to support viable text mining for experimental results, methodology statements, and other forms of information. We are currently developing machine learning methods, to be trained and tested on the annotated corpus, that would allow for the automatic categorization of biomedical text along the general dimensions that we have presented. The guidelines in full detail, along with annotated examples, are publicly available. PMID:16867190
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Mining biomedical images towards valuable information retrieval in biomedical and life sciences.
Ahmed, Zeeshan; Zeeshan, Saman; Dandekar, Thomas
2016-01-01
Biomedical images are helpful sources for the scientists and practitioners in drawing significant hypotheses, exemplifying approaches and describing experimental results in published biomedical literature. In last decades, there has been an enormous increase in the amount of heterogeneous biomedical image production and publication, which results in a need for bioimaging platforms for feature extraction and analysis of text and content in biomedical images to take advantage in implementing effective information retrieval systems. In this review, we summarize technologies related to data mining of figures. We describe and compare the potential of different approaches in terms of their developmental aspects, used methodologies, produced results, achieved accuracies and limitations. Our comparative conclusions include current challenges for bioimaging software with selective image mining, embedded text extraction and processing of complex natural language queries. © The Author(s) 2016. Published by Oxford University Press.
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EXACT2: the semantics of biomedical protocols
2014-01-01
Background The reliability and reproducibility of experimental procedures is a cornerstone of scientific practice. There is a pressing technological need for the better representation of biomedical protocols to enable other agents (human or machine) to better reproduce results. A framework that ensures that all information required for the replication of experimental protocols is essential to achieve reproducibility. Methods We have developed the ontology EXACT2 (EXperimental ACTions) that is designed to capture the full semantics of biomedical protocols required for their reproducibility. To construct EXACT2 we manually inspected hundreds of published and commercial biomedical protocols from several areas of biomedicine. After establishing a clear pattern for extracting the required information we utilized text-mining tools to translate the protocols into a machine amenable format. We have verified the utility of EXACT2 through the successful processing of previously 'unseen' (not used for the construction of EXACT2) protocols. Results The paper reports on a fundamentally new version EXACT2 that supports the semantically-defined representation of biomedical protocols. The ability of EXACT2 to capture the semantics of biomedical procedures was verified through a text mining use case. In this EXACT2 is used as a reference model for text mining tools to identify terms pertinent to experimental actions, and their properties, in biomedical protocols expressed in natural language. An EXACT2-based framework for the translation of biomedical protocols to a machine amenable format is proposed. Conclusions The EXACT2 ontology is sufficient to record, in a machine processable form, the essential information about biomedical protocols. EXACT2 defines explicit semantics of experimental actions, and can be used by various computer applications. It can serve as a reference model for for the translation of biomedical protocols in natural language into a semantically-defined format. PMID:25472549
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Management of information in distributed biomedical collaboratories.
Keator, David B
2009-01-01
Organizing and annotating biomedical data in structured ways has gained much interest and focus in the last 30 years. Driven by decreases in digital storage costs and advances in genetics sequencing, imaging, electronic data collection, and microarray technologies, data is being collected at an alarming rate. The specialization of fields in biology and medicine demonstrates the need for somewhat different structures for storage and retrieval of data. For biologists, the need for structured information and integration across a number of domains drives development. For clinical researchers and hospitals, the need for a structured medical record accessible to, ideally, any medical practitioner who might require it during the course of research or patient treatment, patient confidentiality, and security are the driving developmental factors. Scientific data management systems generally consist of a few core services: a backend database system, a front-end graphical user interface, and an export/import mechanism or data interchange format to both get data into and out of the database and share data with collaborators. The chapter introduces some existing databases, distributed file systems, and interchange languages used within the biomedical research and clinical communities for scientific data management and exchange.
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Ebola Outbreak Response: The Role of Information Resources and the National Library of Medicine
Love, Cynthia B.; Arnesen, Stacey J.; Phillips, Steven J.
2016-01-01
The US National Library of Medicine (NLM) offers Internet-based, no-cost resources useful for responding to the 2014 West Africa Ebola outbreak. Resources for health professionals, planners, responders, and researchers include PubMed, Disaster Lit, the Web page “Ebola Outbreak 2014: Information Resources,” and the Virus Variation database of sequences for Ebolavirus. In cooperation with participating publishers, NLM offers free access to full-text articles from over 650 biomedical journals and 4000 online reference books through the Emergency Access Initiative. At the start of a prolonged disaster event or disease outbreak, the documents and information of most immediate use may not be in the peer-reviewed biomedical journal literature. To maintain current awareness may require using any of the following: news outlets; social media; preliminary online data, maps, and situation reports; and documents published by nongovernmental organizations, international associations, and government agencies. Similar to the pattern of interest shown in the news and social media, use of NLM Ebola-related resources is also increasing since the start of the outbreak was first reported in March 2014 PMID:25325189
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Ebola outbreak response: the role of information resources and the National Library of Medicine.
Love, Cynthia B; Arnesen, Stacey J; Phillips, Steven J
2015-02-01
The US National Library of Medicine (NLM) offers Internet-based, no-cost resources useful for responding to the 2014 West Africa Ebola outbreak. Resources for health professionals, planners, responders, and researchers include PubMed, Disaster Lit, the Web page "Ebola Outbreak 2014: Information Resources," and the Virus Variation database of sequences for Ebolavirus. In cooperation with participating publishers, NLM offers free access to full-text articles from over 650 biomedical journals and 4000 online reference books through the Emergency Access Initiative. At the start of a prolonged disaster event or disease outbreak, the documents and information of most immediate use may not be in the peer-reviewed biomedical journal literature. To maintain current awareness may require using any of the following: news outlets; social media; preliminary online data, maps, and situation reports; and documents published by nongovernmental organizations, international associations, and government agencies. Similar to the pattern of interest shown in the news and social media, use of NLM Ebola-related resources is also increasing since the start of the outbreak was first reported in March 2014.
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Biomedical data integration in computational drug design and bioinformatics.
Seoane, Jose A; Aguiar-Pulido, Vanessa; Munteanu, Cristian R; Rivero, Daniel; Rabunal, Juan R; Dorado, Julian; Pazos, Alejandro
2013-03-01
In recent years, in the post genomic era, more and more data is being generated by biological high throughput technologies, such as proteomics and transcriptomics. This omics data can be very useful, but the real challenge is to analyze all this data, as a whole, after integrating it. Biomedical data integration enables making queries to different, heterogeneous and distributed biomedical data sources. Data integration solutions can be very useful not only in the context of drug design, but also in biomedical information retrieval, clinical diagnosis, system biology, etc. In this review, we analyze the most common approaches to biomedical data integration, such as federated databases, data warehousing, multi-agent systems and semantic technology, as well as the solutions developed using these approaches in the past few years.
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Marafino, Ben J; Boscardin, W John; Dudley, R Adams
2015-04-01
Sparsity is often a desirable property of statistical models, and various feature selection methods exist so as to yield sparser and interpretable models. However, their application to biomedical text classification, particularly to mortality risk stratification among intensive care unit (ICU) patients, has not been thoroughly studied. To develop and characterize sparse classifiers based on the free text of nursing notes in order to predict ICU mortality risk and to discover text features most strongly associated with mortality. We selected nursing notes from the first 24h of ICU admission for 25,826 adult ICU patients from the MIMIC-II database. We then developed a pair of stochastic gradient descent-based classifiers with elastic-net regularization. We also studied the performance-sparsity tradeoffs of both classifiers as their regularization parameters were varied. The best-performing classifier achieved a 10-fold cross-validated AUC of 0.897 under the log loss function and full L2 regularization, while full L1 regularization used just 0.00025% of candidate input features and resulted in an AUC of 0.889. Using the log loss (range of AUCs 0.889-0.897) yielded better performance compared to the hinge loss (0.850-0.876), but the latter yielded even sparser models. Most features selected by both classifiers appear clinically relevant and correspond to predictors already present in existing ICU mortality models. The sparser classifiers were also able to discover a number of informative - albeit nonclinical - features. The elastic-net-regularized classifiers perform reasonably well and are capable of reducing the number of features required by over a thousandfold, with only a modest impact on performance. Copyright © 2015 Elsevier Inc. All rights reserved.
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Organization of Heterogeneous Scientific Data Using the EAV/CR Representation
Nadkarni, Prakash M.; Marenco, Luis; Chen, Roland; Skoufos, Emmanouil; Shepherd, Gordon; Miller, Perry
1999-01-01
Entity-attribute-value (EAV) representation is a means of organizing highly heterogeneous data using a relatively simple physical database schema. EAV representation is widely used in the medical domain, most notably in the storage of data related to clinical patient records. Its potential strengths suggest its use in other biomedical areas, in particular research databases whose schemas are complex as well as constantly changing to reflect evolving knowledge in rapidly advancing scientific domains. When deployed for such purposes, the basic EAV representation needs to be augmented significantly to handle the modeling of complex objects (classes) as well as to manage interobject relationships. The authors refer to their modification of the basic EAV paradigm as EAV/CR (EAV with classes and relationships). They describe EAV/CR representation with examples from two biomedical databases that use it. PMID:10579606
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ParaBTM: A Parallel Processing Framework for Biomedical Text Mining on Supercomputers.
Xing, Yuting; Wu, Chengkun; Yang, Xi; Wang, Wei; Zhu, En; Yin, Jianping
2018-04-27
A prevailing way of extracting valuable information from biomedical literature is to apply text mining methods on unstructured texts. However, the massive amount of literature that needs to be analyzed poses a big data challenge to the processing efficiency of text mining. In this paper, we address this challenge by introducing parallel processing on a supercomputer. We developed paraBTM, a runnable framework that enables parallel text mining on the Tianhe-2 supercomputer. It employs a low-cost yet effective load balancing strategy to maximize the efficiency of parallel processing. We evaluated the performance of paraBTM on several datasets, utilizing three types of named entity recognition tasks as demonstration. Results show that, in most cases, the processing efficiency can be greatly improved with parallel processing, and the proposed load balancing strategy is simple and effective. In addition, our framework can be readily applied to other tasks of biomedical text mining besides NER.
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Text processing through Web services: calling Whatizit.
Rebholz-Schuhmann, Dietrich; Arregui, Miguel; Gaudan, Sylvain; Kirsch, Harald; Jimeno, Antonio
2008-01-15
Text-mining (TM) solutions are developing into efficient services to researchers in the biomedical research community. Such solutions have to scale with the growing number and size of resources (e.g. available controlled vocabularies), with the amount of literature to be processed (e.g. about 17 million documents in PubMed) and with the demands of the user community (e.g. different methods for fact extraction). These demands motivated the development of a server-based solution for literature analysis. Whatizit is a suite of modules that analyse text for contained information, e.g. any scientific publication or Medline abstracts. Special modules identify terms and then link them to the corresponding entries in bioinformatics databases such as UniProtKb/Swiss-Prot data entries and gene ontology concepts. Other modules identify a set of selected annotation types like the set produced by the EBIMed analysis pipeline for proteins. In the case of Medline abstracts, Whatizit offers access to EBI's in-house installation via PMID or term query. For large quantities of the user's own text, the server can be operated in a streaming mode (http://www.ebi.ac.uk/webservices/whatizit).
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Burns, Gully A P C; Dasigi, Pradeep; de Waard, Anita; Hovy, Eduard H
2016-01-01
Automated machine-reading biocuration systems typically use sentence-by-sentence information extraction to construct meaning representations for use by curators. This does not directly reflect the typical discourse structure used by scientists to construct an argument from the experimental data available within a article, and is therefore less likely to correspond to representations typically used in biomedical informatics systems (let alone to the mental models that scientists have). In this study, we develop Natural Language Processing methods to locate, extract, and classify the individual passages of text from articles' Results sections that refer to experimental data. In our domain of interest (molecular biology studies of cancer signal transduction pathways), individual articles may contain as many as 30 small-scale individual experiments describing a variety of findings, upon which authors base their overall research conclusions. Our system automatically classifies discourse segments in these texts into seven categories (fact, hypothesis, problem, goal, method, result, implication) with an F-score of 0.68. These segments describe the essential building blocks of scientific discourse to (i) provide context for each experiment, (ii) report experimental details and (iii) explain the data's meaning in context. We evaluate our system on text passages from articles that were curated in molecular biology databases (the Pathway Logic Datum repository, the Molecular Interaction MINT and INTACT databases) linking individual experiments in articles to the type of assay used (coprecipitation, phosphorylation, translocation etc.). We use supervised machine learning techniques on text passages containing unambiguous references to experiments to obtain baseline F1 scores of 0.59 for MINT, 0.71 for INTACT and 0.63 for Pathway Logic. Although preliminary, these results support the notion that targeting information extraction methods to experimental results could provide accurate, automated methods for biocuration. We also suggest the need for finer-grained curation of experimental methods used when constructing molecular biology databases. © The Author(s) 2016. Published by Oxford University Press.
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Are we studying what matters? Health priorities and NIH-funded biomedical engineering research.
Rubin, Jessica B; Paltiel, A David; Saltzman, W Mark
2010-07-01
With the founding of the National Institute of Biomedical Imaging and Bioengineering (NIBIB) in 1999, the National Institutes of Health (NIH) made explicit its dedication to expanding research in biomedical engineering. Ten years later, we sought to examine how closely federal funding for biomedical engineering aligns with U.S. health priorities. Using a publicly accessible database of research projects funded by the NIH in 2008, we identified 641 grants focused on biomedical engineering, 48% of which targeted specific diseases. Overall, we found that these disease-specific NIH-funded biomedical engineering research projects align with national health priorities, as quantified by three commonly utilized measures of disease burden: cause of death, disability-adjusted survival losses, and expenditures. However, we also found some illnesses (e.g., cancer and heart disease) for which the number of research projects funded deviated from our expectations, given their disease burden. Our findings suggest several possibilities for future studies that would serve to further inform the allocation of limited research dollars within the field of biomedical engineering.
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Grethe, Jeffrey S; Baru, Chaitan; Gupta, Amarnath; James, Mark; Ludaescher, Bertram; Martone, Maryann E; Papadopoulos, Philip M; Peltier, Steven T; Rajasekar, Arcot; Santini, Simone; Zaslavsky, Ilya N; Ellisman, Mark H
2005-01-01
Through support from the National Institutes of Health's National Center for Research Resources, the Biomedical Informatics Research Network (BIRN) is pioneering the use of advanced cyberinfrastructure for medical research. By synchronizing developments in advanced wide area networking, distributed computing, distributed database federation, and other emerging capabilities of e-science, the BIRN has created a collaborative environment that is paving the way for biomedical research and clinical information management. The BIRN Coordinating Center (BIRN-CC) is orchestrating the development and deployment of key infrastructure components for immediate and long-range support of biomedical and clinical research being pursued by domain scientists in three neuroimaging test beds.
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Search and Graph Database Technologies for Biomedical Semantic Indexing: Experimental Analysis.
Segura Bedmar, Isabel; Martínez, Paloma; Carruana Martín, Adrián
2017-12-01
Biomedical semantic indexing is a very useful support tool for human curators in their efforts for indexing and cataloging the biomedical literature. The aim of this study was to describe a system to automatically assign Medical Subject Headings (MeSH) to biomedical articles from MEDLINE. Our approach relies on the assumption that similar documents should be classified by similar MeSH terms. Although previous work has already exploited the document similarity by using a k-nearest neighbors algorithm, we represent documents as document vectors by search engine indexing and then compute the similarity between documents using cosine similarity. Once the most similar documents for a given input document are retrieved, we rank their MeSH terms to choose the most suitable set for the input document. To do this, we define a scoring function that takes into account the frequency of the term into the set of retrieved documents and the similarity between the input document and each retrieved document. In addition, we implement guidelines proposed by human curators to annotate MEDLINE articles; in particular, the heuristic that says if 3 MeSH terms are proposed to classify an article and they share the same ancestor, they should be replaced by this ancestor. The representation of the MeSH thesaurus as a graph database allows us to employ graph search algorithms to quickly and easily capture hierarchical relationships such as the lowest common ancestor between terms. Our experiments show promising results with an F1 of 69% on the test dataset. To the best of our knowledge, this is the first work that combines search and graph database technologies for the task of biomedical semantic indexing. Due to its horizontal scalability, ElasticSearch becomes a real solution to index large collections of documents (such as the bibliographic database MEDLINE). Moreover, the use of graph search algorithms for accessing MeSH information could provide a support tool for cataloging MEDLINE abstracts in real time. ©Isabel Segura Bedmar, Paloma Martínez, Adrián Carruana Martín. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 01.12.2017.
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Plikus, Maksim V; Zhang, Zina; Chuong, Cheng-Ming
2006-01-01
Background Understanding research activity within any given biomedical field is important. Search outputs generated by MEDLINE/PubMed are not well classified and require lengthy manual citation analysis. Automation of citation analytics can be very useful and timesaving for both novices and experts. Results PubFocus web server automates analysis of MEDLINE/PubMed search queries by enriching them with two widely used human factor-based bibliometric indicators of publication quality: journal impact factor and volume of forward references. In addition to providing basic volumetric statistics, PubFocus also prioritizes citations and evaluates authors' impact on the field of search. PubFocus also analyses presence and occurrence of biomedical key terms within citations by utilizing controlled vocabularies. Conclusion We have developed citations' prioritisation algorithm based on journal impact factor, forward referencing volume, referencing dynamics, and author's contribution level. It can be applied either to the primary set of PubMed search results or to the subsets of these results identified through key terms from controlled biomedical vocabularies and ontologies. NCI (National Cancer Institute) thesaurus and MGD (Mouse Genome Database) mammalian gene orthology have been implemented for key terms analytics. PubFocus provides a scalable platform for the integration of multiple available ontology databases. PubFocus analytics can be adapted for input sources of biomedical citations other than PubMed. PMID:17014720
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PubRunner: A light-weight framework for updating text mining results.
Anekalla, Kishore R; Courneya, J P; Fiorini, Nicolas; Lever, Jake; Muchow, Michael; Busby, Ben
2017-01-01
Biomedical text mining promises to assist biologists in quickly navigating the combined knowledge in their domain. This would allow improved understanding of the complex interactions within biological systems and faster hypothesis generation. New biomedical research articles are published daily and text mining tools are only as good as the corpus from which they work. Many text mining tools are underused because their results are static and do not reflect the constantly expanding knowledge in the field. In order for biomedical text mining to become an indispensable tool used by researchers, this problem must be addressed. To this end, we present PubRunner, a framework for regularly running text mining tools on the latest publications. PubRunner is lightweight, simple to use, and can be integrated with an existing text mining tool. The workflow involves downloading the latest abstracts from PubMed, executing a user-defined tool, pushing the resulting data to a public FTP or Zenodo dataset, and publicizing the location of these results on the public PubRunner website. We illustrate the use of this tool by re-running the commonly used word2vec tool on the latest PubMed abstracts to generate up-to-date word vector representations for the biomedical domain. This shows a proof of concept that we hope will encourage text mining developers to build tools that truly will aid biologists in exploring the latest publications.
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Hsu, Yi-Yu; Chen, Hung-Yu; Kao, Hung-Yu
2013-01-01
Background Determining the semantic relatedness of two biomedical terms is an important task for many text-mining applications in the biomedical field. Previous studies, such as those using ontology-based and corpus-based approaches, measured semantic relatedness by using information from the structure of biomedical literature, but these methods are limited by the small size of training resources. To increase the size of training datasets, the outputs of search engines have been used extensively to analyze the lexical patterns of biomedical terms. Methodology/Principal Findings In this work, we propose the Mutually Reinforcing Lexical Pattern Ranking (ReLPR) algorithm for learning and exploring the lexical patterns of synonym pairs in biomedical text. ReLPR employs lexical patterns and their pattern containers to assess the semantic relatedness of biomedical terms. By combining sentence structures and the linking activities between containers and lexical patterns, our algorithm can explore the correlation between two biomedical terms. Conclusions/Significance The average correlation coefficient of the ReLPR algorithm was 0.82 for various datasets. The results of the ReLPR algorithm were significantly superior to those of previous methods. PMID:24348899
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Online Mendelian Inheritance in Man (OMIM).
Hamosh, A; Scott, A F; Amberger, J; Valle, D; McKusick, V A
2000-01-01
Online Mendelian Inheritance In Man (OMIM) is a public database of bibliographic information about human genes and genetic disorders. Begun by Dr. Victor McKusick as the authoritative reference Mendelian Inheritance in Man, it is now distributed electronically by the National Center for Biotechnology Information (NCBI). Material in OMIM is derived from the biomedical literature and is written by Dr. McKusick and his colleagues at Johns Hopkins University and elsewhere. Each OMIM entry has a full text summary of a genetic phenotype and/or gene and has copious links to other genetic resources such as DNA and protein sequence, PubMed references, mutation databases, approved gene nomenclature, and more. In addition, NCBI's neighboring feature allows users to identify related articles from PubMed selected on the basis of key words in the OMIM entry. Through its many features, OMIM is increasingly becoming a major gateway for clinicians, students, and basic researchers to the ever-growing literature and resources of human genetics. Copyright 2000 Wiley-Liss, Inc.
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Biomedical text mining for research rigor and integrity: tasks, challenges, directions.
Kilicoglu, Halil
2017-06-13
An estimated quarter of a trillion US dollars is invested in the biomedical research enterprise annually. There is growing alarm that a significant portion of this investment is wasted because of problems in reproducibility of research findings and in the rigor and integrity of research conduct and reporting. Recent years have seen a flurry of activities focusing on standardization and guideline development to enhance the reproducibility and rigor of biomedical research. Research activity is primarily communicated via textual artifacts, ranging from grant applications to journal publications. These artifacts can be both the source and the manifestation of practices leading to research waste. For example, an article may describe a poorly designed experiment, or the authors may reach conclusions not supported by the evidence presented. In this article, we pose the question of whether biomedical text mining techniques can assist the stakeholders in the biomedical research enterprise in doing their part toward enhancing research integrity and rigor. In particular, we identify four key areas in which text mining techniques can make a significant contribution: plagiarism/fraud detection, ensuring adherence to reporting guidelines, managing information overload and accurate citation/enhanced bibliometrics. We review the existing methods and tools for specific tasks, if they exist, or discuss relevant research that can provide guidance for future work. With the exponential increase in biomedical research output and the ability of text mining approaches to perform automatic tasks at large scale, we propose that such approaches can support tools that promote responsible research practices, providing significant benefits for the biomedical research enterprise. Published by Oxford University Press 2017. This work is written by a US Government employee and is in the public domain in the US.
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Unsupervised Biomedical Named Entity Recognition: Experiments with Clinical and Biological Texts
Zhang, Shaodian; Elhadad, Nóemie
2013-01-01
Named entity recognition is a crucial component of biomedical natural language processing, enabling information extraction and ultimately reasoning over and knowledge discovery from text. Much progress has been made in the design of rule-based and supervised tools, but they are often genre and task dependent. As such, adapting them to different genres of text or identifying new types of entities requires major effort in re-annotation or rule development. In this paper, we propose an unsupervised approach to extracting named entities from biomedical text. We describe a stepwise solution to tackle the challenges of entity boundary detection and entity type classification without relying on any handcrafted rules, heuristics, or annotated data. A noun phrase chunker followed by a filter based on inverse document frequency extracts candidate entities from free text. Classification of candidate entities into categories of interest is carried out by leveraging principles from distributional semantics. Experiments show that our system, especially the entity classification step, yields competitive results on two popular biomedical datasets of clinical notes and biological literature, and outperforms a baseline dictionary match approach. Detailed error analysis provides a road map for future work. PMID:23954592
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A neural joint model for entity and relation extraction from biomedical text.
Li, Fei; Zhang, Meishan; Fu, Guohong; Ji, Donghong
2017-03-31
Extracting biomedical entities and their relations from text has important applications on biomedical research. Previous work primarily utilized feature-based pipeline models to process this task. Many efforts need to be made on feature engineering when feature-based models are employed. Moreover, pipeline models may suffer error propagation and are not able to utilize the interactions between subtasks. Therefore, we propose a neural joint model to extract biomedical entities as well as their relations simultaneously, and it can alleviate the problems above. Our model was evaluated on two tasks, i.e., the task of extracting adverse drug events between drug and disease entities, and the task of extracting resident relations between bacteria and location entities. Compared with the state-of-the-art systems in these tasks, our model improved the F1 scores of the first task by 5.1% in entity recognition and 8.0% in relation extraction, and that of the second task by 9.2% in relation extraction. The proposed model achieves competitive performances with less work on feature engineering. We demonstrate that the model based on neural networks is effective for biomedical entity and relation extraction. In addition, parameter sharing is an alternative method for neural models to jointly process this task. Our work can facilitate the research on biomedical text mining.
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The BioMart community portal: an innovative alternative to large, centralized data repositories
USDA-ARS?s Scientific Manuscript database
The BioMart Community Portal (www.biomart.org) is a community-driven effort to provide a unified interface to biomedical databases that are distributed worldwide. The portal provides access to numerous database projects supported by 30 scientific organizations. It includes over 800 different biologi...
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75 FR 61761 - Renewal of Charter for the Chronic Fatigue Syndrome Advisory Committee
Federal Register 2010, 2011, 2012, 2013, 2014
2010-10-06
... professionals, and the biomedical, academic, and research communities about chronic fatigue syndrome advances... accessing the FACA database that is maintained by the Committee Management Secretariat under the General Services Administration. The Web site address for the FACA database is http://fido.gov/facadatabase . Dated...
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A sentence sliding window approach to extract protein annotations from biomedical articles
Krallinger, Martin; Padron, Maria; Valencia, Alfonso
2005-01-01
Background Within the emerging field of text mining and statistical natural language processing (NLP) applied to biomedical articles, a broad variety of techniques have been developed during the past years. Nevertheless, there is still a great ned of comparative assessment of the performance of the proposed methods and the development of common evaluation criteria. This issue was addressed by the Critical Assessment of Text Mining Methods in Molecular Biology (BioCreative) contest. The aim of this contest was to assess the performance of text mining systems applied to biomedical texts including tools which recognize named entities such as genes and proteins, and tools which automatically extract protein annotations. Results The "sentence sliding window" approach proposed here was found to efficiently extract text fragments from full text articles containing annotations on proteins, providing the highest number of correctly predicted annotations. Moreover, the number of correct extractions of individual entities (i.e. proteins and GO terms) involved in the relationships used for the annotations was significantly higher than the correct extractions of the complete annotations (protein-function relations). Conclusion We explored the use of averaging sentence sliding windows for information extraction, especially in a context where conventional training data is unavailable. The combination of our approach with more refined statistical estimators and machine learning techniques might be a way to improve annotation extraction for future biomedical text mining applications. PMID:15960831
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RysannMD: A biomedical semantic annotator balancing speed and accuracy.
Cuzzola, John; Jovanović, Jelena; Bagheri, Ebrahim
2017-07-01
Recently, both researchers and practitioners have explored the possibility of semantically annotating large and continuously evolving collections of biomedical texts such as research papers, medical reports, and physician notes in order to enable their efficient and effective management and use in clinical practice or research laboratories. Such annotations can be automatically generated by biomedical semantic annotators - tools that are specifically designed for detecting and disambiguating biomedical concepts mentioned in text. The biomedical community has already presented several solid automated semantic annotators. However, the existing tools are either strong in their disambiguation capacity, i.e., the ability to identify the correct biomedical concept for a given piece of text among several candidate concepts, or they excel in their processing time, i.e., work very efficiently, but none of the semantic annotation tools reported in the literature has both of these qualities. In this paper, we present RysannMD (Ryerson Semantic Annotator for Medical Domain), a biomedical semantic annotation tool that strikes a balance between processing time and performance while disambiguating biomedical terms. In other words, RysannMD provides reasonable disambiguation performance when choosing the right sense for a biomedical term in a given context, and does that in a reasonable time. To examine how RysannMD stands with respect to the state of the art biomedical semantic annotators, we have conducted a series of experiments using standard benchmarking corpora, including both gold and silver standards, and four modern biomedical semantic annotators, namely cTAKES, MetaMap, NOBLE Coder, and Neji. The annotators were compared with respect to the quality of the produced annotations measured against gold and silver standards using precision, recall, and F 1 measure and speed, i.e., processing time. In the experiments, RysannMD achieved the best median F 1 measure across the benchmarking corpora, independent of the standard used (silver/gold), biomedical subdomain, and document size. In terms of the annotation speed, RysannMD scored the second best median processing time across all the experiments. The obtained results indicate that RysannMD offers the best performance among the examined semantic annotators when both quality of annotation and speed are considered simultaneously. Copyright © 2017 Elsevier Inc. All rights reserved.
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Literature-based discovery of diabetes- and ROS-related targets
2010-01-01
Background Reactive oxygen species (ROS) are known mediators of cellular damage in multiple diseases including diabetic complications. Despite its importance, no comprehensive database is currently available for the genes associated with ROS. Methods We present ROS- and diabetes-related targets (genes/proteins) collected from the biomedical literature through a text mining technology. A web-based literature mining tool, SciMiner, was applied to 1,154 biomedical papers indexed with diabetes and ROS by PubMed to identify relevant targets. Over-represented targets in the ROS-diabetes literature were obtained through comparisons against randomly selected literature. The expression levels of nine genes, selected from the top ranked ROS-diabetes set, were measured in the dorsal root ganglia (DRG) of diabetic and non-diabetic DBA/2J mice in order to evaluate the biological relevance of literature-derived targets in the pathogenesis of diabetic neuropathy. Results SciMiner identified 1,026 ROS- and diabetes-related targets from the 1,154 biomedical papers (http://jdrf.neurology.med.umich.edu/ROSDiabetes/). Fifty-three targets were significantly over-represented in the ROS-diabetes literature compared to randomly selected literature. These over-represented targets included well-known members of the oxidative stress response including catalase, the NADPH oxidase family, and the superoxide dismutase family of proteins. Eight of the nine selected genes exhibited significant differential expression between diabetic and non-diabetic mice. For six genes, the direction of expression change in diabetes paralleled enhanced oxidative stress in the DRG. Conclusions Literature mining compiled ROS-diabetes related targets from the biomedical literature and led us to evaluate the biological relevance of selected targets in the pathogenesis of diabetic neuropathy. PMID:20979611
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Déjà vu: a database of highly similar citations in the scientific literature
Errami, Mounir; Sun, Zhaohui; Long, Tara C.; George, Angela C.; Garner, Harold R.
2009-01-01
In the scientific research community, plagiarism and covert multiple publications of the same data are considered unacceptable because they undermine the public confidence in the scientific integrity. Yet, little has been done to help authors and editors to identify highly similar citations, which sometimes may represent cases of unethical duplication. For this reason, we have made available Déjà vu, a publicly available database of highly similar Medline citations identified by the text similarity search engine eTBLAST. Following manual verification, highly similar citation pairs are classified into various categories ranging from duplicates with different authors to sanctioned duplicates. Déjà vu records also contain user-provided commentary and supporting information to substantiate each document's categorization. Déjà vu and eTBLAST are available to authors, editors, reviewers, ethicists and sociologists to study, intercept, annotate and deter questionable publication practices. These tools are part of a sustained effort to enhance the quality of Medline as ‘the’ biomedical corpus. The Déjà vu database is freely accessible at http://spore.swmed.edu/dejavu. The tool eTBLAST is also freely available at http://etblast.org. PMID:18757888
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Deja vu: a database of highly similar citations in the scientific literature.
Errami, Mounir; Sun, Zhaohui; Long, Tara C; George, Angela C; Garner, Harold R
2009-01-01
In the scientific research community, plagiarism and covert multiple publications of the same data are considered unacceptable because they undermine the public confidence in the scientific integrity. Yet, little has been done to help authors and editors to identify highly similar citations, which sometimes may represent cases of unethical duplication. For this reason, we have made available Déjà vu, a publicly available database of highly similar Medline citations identified by the text similarity search engine eTBLAST. Following manual verification, highly similar citation pairs are classified into various categories ranging from duplicates with different authors to sanctioned duplicates. Déjà vu records also contain user-provided commentary and supporting information to substantiate each document's categorization. Déjà vu and eTBLAST are available to authors, editors, reviewers, ethicists and sociologists to study, intercept, annotate and deter questionable publication practices. These tools are part of a sustained effort to enhance the quality of Medline as 'the' biomedical corpus. The Déjà vu database is freely accessible at http://spore.swmed.edu/dejavu. The tool eTBLAST is also freely available at http://etblast.org.
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Semantic SenseLab: implementing the vision of the Semantic Web in neuroscience
Samwald, Matthias; Chen, Huajun; Ruttenberg, Alan; Lim, Ernest; Marenco, Luis; Miller, Perry; Shepherd, Gordon; Cheung, Kei-Hoi
2011-01-01
Summary Objective Integrative neuroscience research needs a scalable informatics framework that enables semantic integration of diverse types of neuroscience data. This paper describes the use of the Web Ontology Language (OWL) and other Semantic Web technologies for the representation and integration of molecular-level data provided by several of SenseLab suite of neuroscience databases. Methods Based on the original database structure, we semi-automatically translated the databases into OWL ontologies with manual addition of semantic enrichment. The SenseLab ontologies are extensively linked to other biomedical Semantic Web resources, including the Subcellular Anatomy Ontology, Brain Architecture Management System, the Gene Ontology, BIRNLex and UniProt. The SenseLab ontologies have also been mapped to the Basic Formal Ontology and Relation Ontology, which helps ease interoperability with many other existing and future biomedical ontologies for the Semantic Web. In addition, approaches to representing contradictory research statements are described. The SenseLab ontologies are designed for use on the Semantic Web that enables their integration into a growing collection of biomedical information resources. Conclusion We demonstrate that our approach can yield significant potential benefits and that the Semantic Web is rapidly becoming mature enough to realize its anticipated promises. The ontologies are available online at http://neuroweb.med.yale.edu/senselab/ PMID:20006477
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Semantic SenseLab: Implementing the vision of the Semantic Web in neuroscience.
Samwald, Matthias; Chen, Huajun; Ruttenberg, Alan; Lim, Ernest; Marenco, Luis; Miller, Perry; Shepherd, Gordon; Cheung, Kei-Hoi
2010-01-01
Integrative neuroscience research needs a scalable informatics framework that enables semantic integration of diverse types of neuroscience data. This paper describes the use of the Web Ontology Language (OWL) and other Semantic Web technologies for the representation and integration of molecular-level data provided by several of SenseLab suite of neuroscience databases. Based on the original database structure, we semi-automatically translated the databases into OWL ontologies with manual addition of semantic enrichment. The SenseLab ontologies are extensively linked to other biomedical Semantic Web resources, including the Subcellular Anatomy Ontology, Brain Architecture Management System, the Gene Ontology, BIRNLex and UniProt. The SenseLab ontologies have also been mapped to the Basic Formal Ontology and Relation Ontology, which helps ease interoperability with many other existing and future biomedical ontologies for the Semantic Web. In addition, approaches to representing contradictory research statements are described. The SenseLab ontologies are designed for use on the Semantic Web that enables their integration into a growing collection of biomedical information resources. We demonstrate that our approach can yield significant potential benefits and that the Semantic Web is rapidly becoming mature enough to realize its anticipated promises. The ontologies are available online at http://neuroweb.med.yale.edu/senselab/. 2009 Elsevier B.V. All rights reserved.
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Introducing meta-services for biomedical information extraction
Leitner, Florian; Krallinger, Martin; Rodriguez-Penagos, Carlos; Hakenberg, Jörg; Plake, Conrad; Kuo, Cheng-Ju; Hsu, Chun-Nan; Tsai, Richard Tzong-Han; Hung, Hsi-Chuan; Lau, William W; Johnson, Calvin A; Sætre, Rune; Yoshida, Kazuhiro; Chen, Yan Hua; Kim, Sun; Shin, Soo-Yong; Zhang, Byoung-Tak; Baumgartner, William A; Hunter, Lawrence; Haddow, Barry; Matthews, Michael; Wang, Xinglong; Ruch, Patrick; Ehrler, Frédéric; Özgür, Arzucan; Erkan, Güneş; Radev, Dragomir R; Krauthammer, Michael; Luong, ThaiBinh; Hoffmann, Robert; Sander, Chris; Valencia, Alfonso
2008-01-01
We introduce the first meta-service for information extraction in molecular biology, the BioCreative MetaServer (BCMS; ). This prototype platform is a joint effort of 13 research groups and provides automatically generated annotations for PubMed/Medline abstracts. Annotation types cover gene names, gene IDs, species, and protein-protein interactions. The annotations are distributed by the meta-server in both human and machine readable formats (HTML/XML). This service is intended to be used by biomedical researchers and database annotators, and in biomedical language processing. The platform allows direct comparison, unified access, and result aggregation of the annotations. PMID:18834497
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Autism: biomedical complementary treatment approaches.
Hendren, Robert L
2013-07-01
This article provides a conceptual overview for the use of biomedical complementary and alternative medicine (CAM) treatments for autism spectrum disorders. Pharmaceutical agents with published studies are briefly mentioned; but the focus of the article is on possible biomedical CAM treatments, the rationale for their use, and the current database of mostly preliminary studies regarding their safety and efficacy. Of the more than 50 treatments currently listed here and in use by eager families, 9 are reviewed in more detail because of their promise from preliminary research studies or because of public interest. Copyright © 2013 Elsevier Inc. All rights reserved.
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Text categorization of biomedical data sets using graph kernels and a controlled vocabulary.
Bleik, Said; Mishra, Meenakshi; Huan, Jun; Song, Min
2013-01-01
Recently, graph representations of text have been showing improved performance over conventional bag-of-words representations in text categorization applications. In this paper, we present a graph-based representation for biomedical articles and use graph kernels to classify those articles into high-level categories. In our representation, common biomedical concepts and semantic relationships are identified with the help of an existing ontology and are used to build a rich graph structure that provides a consistent feature set and preserves additional semantic information that could improve a classifier's performance. We attempt to classify the graphs using both a set-based graph kernel that is capable of dealing with the disconnected nature of the graphs and a simple linear kernel. Finally, we report the results comparing the classification performance of the kernel classifiers to common text-based classifiers.
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Erick Peirson, B R; Kropp, Heather; Damerow, Julia; Laubichler, Manfred D
2017-05-01
Contrary to concerns of some critics, we present evidence that biomedical research is not dominated by a small handful of model organisms. An exhaustive analysis of research literature suggests that the diversity of experimental organisms in biomedical research has increased substantially since 1975. There has been a longstanding worry that organism-centric funding policies can lead to biases in experimental organism choice, and thus negatively impact the direction of research and the interpretation of results. Critics have argued that a focus on model organisms has unduly constrained the diversity of experimental organisms. The availability of large electronic databases of scientific literature, combined with interest in quantitative methods among philosophers of science, presents new opportunities for data-driven investigations into organism choice in biomedical research. The diversity of organisms used in NIH-funded research may be considerably lower than in the broader biomedical sciences, and may be subject to greater constraints on organism choice. © 2017 WILEY Periodicals, Inc.
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TU-F-BRD-01: Biomedical Informatics for Medical Physicists
DOE Office of Scientific and Technical Information (OSTI.GOV)
Phillips, M; Kalet, I; McNutt, T
Biomedical informatics encompasses a very large domain of knowledge and applications. This broad and loosely defined field can make it difficult to navigate. Physicists often are called upon to provide informatics services and/or to take part in projects involving principles of the field. The purpose of the presentations in this symposium is to help medical physicists gain some knowledge about the breadth of the field and how, in the current clinical and research environment, they can participate and contribute. Three talks have been designed to give an overview from the perspective of physicists and to provide a more in-depth discussionmore » in two areas. One of the primary purposes, and the main subject of the first talk, is to help physicists achieve a perspective about the range of the topics and concepts that fall under the heading of 'informatics'. The approach is to de-mystify topics and jargon and to help physicists find resources in the field should they need them. The other talks explore two areas of biomedical informatics in more depth. The goal is to highlight two domains of intense current interest--databases and models--in enough depth into current approaches so that an adequate background for independent inquiry is achieved. These two areas will serve as good examples of how physicists, using informatics principles, can contribute to oncology practice and research. Learning Objectives: To understand how the principles of biomedical informatics are used by medical physicists. To put the relevant informatics concepts in perspective with regard to biomedicine in general. To use clinical database design as an example of biomedical informatics. To provide a solid background into the problems and issues of the design and use of data and databases in radiation oncology. To use modeling in the service of decision support systems as an example of modeling methods and data use. To provide a background into how uncertainty in our data and knowledge can be incorporated into modeling methods.« less
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Undisclosed conflicts of interest among biomedical textbook authors.
Piper, Brian J; Lambert, Drew A; Keefe, Ryan C; Smukler, Phoebe U; Selemon, Nicolas A; Duperry, Zachary R
2018-02-05
Textbooks are a formative resource for health care providers during their education and are also an enduring reference for pathophysiology and treatment. Unlike the primary literature and clinical guidelines, biomedical textbook authors do not typically disclose potential financial conflicts of interest (pCoIs). The objective of this study was to evaluate whether the authors of textbooks used in the training of physicians, pharmacists, and dentists had appreciable undisclosed pCoIs in the form of patents or compensation received from pharmaceutical or biotechnology companies. The most recent editions of six medical textbooks, Harrison's Principles of Internal Medicine ( Har PIM), Katzung and Trevor's Basic and Clinical Pharmacology ( Kat BCP), the American Osteopathic Association's Foundations of Osteopathic Medicine ( AOA FOM), Remington: The Science and Practice of Pharmacy ( Rem SPP), Koda-Kimble and Young's Applied Therapeutics ( KKY AT), and Yagiela's Pharmacology and Therapeutics for Dentistry ( Yag PTD), were selected after consulting biomedical educators for evaluation. Author names (N = 1,152, 29.2% female) were submitted to databases to examine patents (Google Scholar) and compensation (ProPublica's Dollars for Docs [PDD]). Authors were listed as inventors on 677 patents (maximum/author = 23), with three-quarters (74.9%) to Har PIM authors. Females were significantly underrepresented among patent holders. The PDD 2009-2013 database revealed receipt of US$13.2 million, the majority to (83.9%) to Har PIM. The maximum compensation per author was $869,413. The PDD 2014 database identified receipt of $6.8 million, with 50.4% of eligible authors receiving compensation. The maximum compensation received by a single author was $560,021. Cardiovascular authors were most likely to have a PDD entry and neurologic disorders authors were least likely. An appreciable subset of biomedical authors have patents and have received remuneration from medical product companies and this information is not disclosed to readers. These findings indicate that full transparency of financial pCoI should become a standard practice among the authors of biomedical educational materials.
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Asymmetric author-topic model for knowledge discovering of big data in toxicogenomics.
Chung, Ming-Hua; Wang, Yuping; Tang, Hailin; Zou, Wen; Basinger, John; Xu, Xiaowei; Tong, Weida
2015-01-01
The advancement of high-throughput screening technologies facilitates the generation of massive amount of biological data, a big data phenomena in biomedical science. Yet, researchers still heavily rely on keyword search and/or literature review to navigate the databases and analyses are often done in rather small-scale. As a result, the rich information of a database has not been fully utilized, particularly for the information embedded in the interactive nature between data points that are largely ignored and buried. For the past 10 years, probabilistic topic modeling has been recognized as an effective machine learning algorithm to annotate the hidden thematic structure of massive collection of documents. The analogy between text corpus and large-scale genomic data enables the application of text mining tools, like probabilistic topic models, to explore hidden patterns of genomic data and to the extension of altered biological functions. In this paper, we developed a generalized probabilistic topic model to analyze a toxicogenomics dataset that consists of a large number of gene expression data from the rat livers treated with drugs in multiple dose and time-points. We discovered the hidden patterns in gene expression associated with the effect of doses and time-points of treatment. Finally, we illustrated the ability of our model to identify the evidence of potential reduction of animal use.
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The BioIntelligence Framework: a new computational platform for biomedical knowledge computing.
Farley, Toni; Kiefer, Jeff; Lee, Preston; Von Hoff, Daniel; Trent, Jeffrey M; Colbourn, Charles; Mousses, Spyro
2013-01-01
Breakthroughs in molecular profiling technologies are enabling a new data-intensive approach to biomedical research, with the potential to revolutionize how we study, manage, and treat complex diseases. The next great challenge for clinical applications of these innovations will be to create scalable computational solutions for intelligently linking complex biomedical patient data to clinically actionable knowledge. Traditional database management systems (DBMS) are not well suited to representing complex syntactic and semantic relationships in unstructured biomedical information, introducing barriers to realizing such solutions. We propose a scalable computational framework for addressing this need, which leverages a hypergraph-based data model and query language that may be better suited for representing complex multi-lateral, multi-scalar, and multi-dimensional relationships. We also discuss how this framework can be used to create rapid learning knowledge base systems to intelligently capture and relate complex patient data to biomedical knowledge in order to automate the recovery of clinically actionable information.
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Belmonte, M
In this article we review two of the main Internet information services for seeking references to bibliography and journals, and the electronic publications on the Internet, with particular emphasis on those related to neurosciencs. The main indices of bibliography are: 1. MEDLINE. By definition, this is the bibliography database. It is an 'on line' version of the magazine with a smaller format, published weekly with the title pages and summaries of most of the biomedical journals. It is based on the Index Medicus, a bibliographic index (on paper) which annually collects references to the most important biomedical journals. 2. EMBASE (Excerpta Medica). It is a direct competitor to MEDLINE, although it has the disadvantage of lack of government subsidies and is privately financed only. This bibliographic database, produced by the publishers Elsevier of Holland, covers approximately 3,500 biomedical journals from 110 countries, and is particularly useful for articles on drugs and toxicology. 3. Current Contents. It publishes the index Current Contents, a classic in this field, much appreciated by scientists in all areas: medicine, social, technology, arts and humanities. At present, it is available in an on line version known as CCC (Current Contents Connect), accessible through the web, but only to subscribers. There is a growing tendency towards the publication of biomedical journals on the Internet. Its full development, if correctly carried out, will mean the opportunity to have the best information available and will result in great benefit to all those who are already using new information technology.
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Alfonso, Fernando
2010-03-01
Biomedical journals must adhere to strict standards of editorial quality. In a globalized academic scenario, biomedical journals must compete firstly to publish the most relevant original research and secondly to obtain the broadest possible visibility and the widest dissemination of their scientific contents. The cornerstone of the scientific process is still the peer-review system but additional quality criteria should be met. Recently access to medical information has been revolutionized by electronic editions. Bibliometric databases such as MEDLINE, the ISI Web of Science and Scopus offer comprehensive online information on medical literature. Classically, the prestige of biomedical journals has been measured by their impact factor but, recently, other indicators such as SCImago SJR or the Eigenfactor are emerging as alternative indices of a journal's quality. Assessing the scholarly impact of research and the merits of individual scientists remains a major challenge. Allocation of authorship credit also remains controversial. Furthermore, in our Kafkaesque world, we prefer to count rather than read the articles we judge. Quantitative publication metrics (research output) and citations analyses (scientific influence) are key determinants of the scientific success of individual investigators. However, academia is embracing new objective indicators (such as the "h" index) to evaluate scholarly merit. The present review discusses some editorial issues affecting biomedical journals, currently available bibliometric databases, bibliometric indices of journal quality and, finally, indicators of research performance and scientific success. Copyright 2010 SEEN. Published by Elsevier Espana. All rights reserved.
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ERIC Educational Resources Information Center
Biomedical Interdisciplinary Curriculum Project, Berkeley, CA.
This student text presents instructional materials for a unit of mathematics within the Biomedical Interdisciplinary Curriculum Project (BICP), a two-year interdisciplinary precollege curriculum aimed at preparing high school students for entry into college and vocational programs leading to a career in the health field. Lessons concentrate on…
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Desiderata for ontologies to be used in semantic annotation of biomedical documents.
Bada, Michael; Hunter, Lawrence
2011-02-01
A wealth of knowledge valuable to the translational research scientist is contained within the vast biomedical literature, but this knowledge is typically in the form of natural language. Sophisticated natural-language-processing systems are needed to translate text into unambiguous formal representations grounded in high-quality consensus ontologies, and these systems in turn rely on gold-standard corpora of annotated documents for training and testing. To this end, we are constructing the Colorado Richly Annotated Full-Text (CRAFT) Corpus, a collection of 97 full-text biomedical journal articles that are being manually annotated with the entire sets of terms from select vocabularies, predominantly from the Open Biomedical Ontologies (OBO) library. Our efforts in building this corpus has illuminated infelicities of these ontologies with respect to the semantic annotation of biomedical documents, and we propose desiderata whose implementation could substantially improve their utility in this task; these include the integration of overlapping terms across OBOs, the resolution of OBO-specific ambiguities, the integration of the BFO with the OBOs and the use of mid-level ontologies, the inclusion of noncanonical instances, and the expansion of relations and realizable entities. Copyright © 2010 Elsevier Inc. All rights reserved.
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Valdez, Joshua; Rueschman, Michael; Kim, Matthew; Redline, Susan; Sahoo, Satya S
2016-10-01
Extraction of structured information from biomedical literature is a complex and challenging problem due to the complexity of biomedical domain and lack of appropriate natural language processing (NLP) techniques. High quality domain ontologies model both data and metadata information at a fine level of granularity, which can be effectively used to accurately extract structured information from biomedical text. Extraction of provenance metadata, which describes the history or source of information, from published articles is an important task to support scientific reproducibility. Reproducibility of results reported by previous research studies is a foundational component of scientific advancement. This is highlighted by the recent initiative by the US National Institutes of Health called "Principles of Rigor and Reproducibility". In this paper, we describe an effective approach to extract provenance metadata from published biomedical research literature using an ontology-enabled NLP platform as part of the Provenance for Clinical and Healthcare Research (ProvCaRe). The ProvCaRe-NLP tool extends the clinical Text Analysis and Knowledge Extraction System (cTAKES) platform using both provenance and biomedical domain ontologies. We demonstrate the effectiveness of ProvCaRe-NLP tool using a corpus of 20 peer-reviewed publications. The results of our evaluation demonstrate that the ProvCaRe-NLP tool has significantly higher recall in extracting provenance metadata as compared to existing NLP pipelines such as MetaMap.
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Do open access biomedical journals benefit smaller countries? The Slovenian experience.
Turk, Nana
2011-06-01
Scientists from smaller countries have problems gaining visibility for their research. Does open access publishing provide a solution? Slovenia is a small country with around 5000 medical doctors, 1300 dentists and 1000 pharmacists. A search of Slovenia's Bibliographic database was carried out to identity all biomedical journals and those which are open access. Slovenia has 18 medical open access journals, but none has an impact factor and only 10 are indexed by Slovenian and international bibliographic databases. The visibility and quality of medical papers is poor. The solution might be to reduce the number of journals and encourage Slovenian scientists to publish their best articles in them. © 2011 The authors. Health Information and Libraries Journal © 2011 Health Libraries Group.
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PubMed and beyond: a survey of web tools for searching biomedical literature
Lu, Zhiyong
2011-01-01
The past decade has witnessed the modern advances of high-throughput technology and rapid growth of research capacity in producing large-scale biological data, both of which were concomitant with an exponential growth of biomedical literature. This wealth of scholarly knowledge is of significant importance for researchers in making scientific discoveries and healthcare professionals in managing health-related matters. However, the acquisition of such information is becoming increasingly difficult due to its large volume and rapid growth. In response, the National Center for Biotechnology Information (NCBI) is continuously making changes to its PubMed Web service for improvement. Meanwhile, different entities have devoted themselves to developing Web tools for helping users quickly and efficiently search and retrieve relevant publications. These practices, together with maturity in the field of text mining, have led to an increase in the number and quality of various Web tools that provide comparable literature search service to PubMed. In this study, we review 28 such tools, highlight their respective innovations, compare them to the PubMed system and one another, and discuss directions for future development. Furthermore, we have built a website dedicated to tracking existing systems and future advances in the field of biomedical literature search. Taken together, our work serves information seekers in choosing tools for their needs and service providers and developers in keeping current in the field. Database URL: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/search PMID:21245076
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Development and Evaluation of Thesauri-Based Bibliographic Biomedical Search Engine
ERIC Educational Resources Information Center
Alghoson, Abdullah
2017-01-01
Due to the large volume and exponential growth of biomedical documents (e.g., books, journal articles), it has become increasingly challenging for biomedical search engines to retrieve relevant documents based on users' search queries. Part of the challenge is the matching mechanism of free-text indexing that performs matching based on…
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A survey on annotation tools for the biomedical literature.
Neves, Mariana; Leser, Ulf
2014-03-01
New approaches to biomedical text mining crucially depend on the existence of comprehensive annotated corpora. Such corpora, commonly called gold standards, are important for learning patterns or models during the training phase, for evaluating and comparing the performance of algorithms and also for better understanding the information sought for by means of examples. Gold standards depend on human understanding and manual annotation of natural language text. This process is very time-consuming and expensive because it requires high intellectual effort from domain experts. Accordingly, the lack of gold standards is considered as one of the main bottlenecks for developing novel text mining methods. This situation led the development of tools that support humans in annotating texts. Such tools should be intuitive to use, should support a range of different input formats, should include visualization of annotated texts and should generate an easy-to-parse output format. Today, a range of tools which implement some of these functionalities are available. In this survey, we present a comprehensive survey of tools for supporting annotation of biomedical texts. Altogether, we considered almost 30 tools, 13 of which were selected for an in-depth comparison. The comparison was performed using predefined criteria and was accompanied by hands-on experiences whenever possible. Our survey shows that current tools can support many of the tasks in biomedical text annotation in a satisfying manner, but also that no tool can be considered as a true comprehensive solution.
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Position-aware deep multi-task learning for drug-drug interaction extraction.
Zhou, Deyu; Miao, Lei; He, Yulan
2018-05-01
A drug-drug interaction (DDI) is a situation in which a drug affects the activity of another drug synergistically or antagonistically when being administered together. The information of DDIs is crucial for healthcare professionals to prevent adverse drug events. Although some known DDIs can be found in purposely-built databases such as DrugBank, most information is still buried in scientific publications. Therefore, automatically extracting DDIs from biomedical texts is sorely needed. In this paper, we propose a novel position-aware deep multi-task learning approach for extracting DDIs from biomedical texts. In particular, sentences are represented as a sequence of word embeddings and position embeddings. An attention-based bidirectional long short-term memory (BiLSTM) network is used to encode each sentence. The relative position information of words with the target drugs in text is combined with the hidden states of BiLSTM to generate the position-aware attention weights. Moreover, the tasks of predicting whether or not two drugs interact with each other and further distinguishing the types of interactions are learned jointly in multi-task learning framework. The proposed approach has been evaluated on the DDIExtraction challenge 2013 corpus and the results show that with the position-aware attention only, our proposed approach outperforms the state-of-the-art method by 0.99% for binary DDI classification, and with both position-aware attention and multi-task learning, our approach achieves a micro F-score of 72.99% on interaction type identification, outperforming the state-of-the-art approach by 1.51%, which demonstrates the effectiveness of the proposed approach. Copyright © 2018 Elsevier B.V. All rights reserved.
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Rule-based deduplication of article records from bibliographic databases.
Jiang, Yu; Lin, Can; Meng, Weiyi; Yu, Clement; Cohen, Aaron M; Smalheiser, Neil R
2014-01-01
We recently designed and deployed a metasearch engine, Metta, that sends queries and retrieves search results from five leading biomedical databases: PubMed, EMBASE, CINAHL, PsycINFO and the Cochrane Central Register of Controlled Trials. Because many articles are indexed in more than one of these databases, it is desirable to deduplicate the retrieved article records. This is not a trivial problem because data fields contain a lot of missing and erroneous entries, and because certain types of information are recorded differently (and inconsistently) in the different databases. The present report describes our rule-based method for deduplicating article records across databases and includes an open-source script module that can be deployed freely. Metta was designed to satisfy the particular needs of people who are writing systematic reviews in evidence-based medicine. These users want the highest possible recall in retrieval, so it is important to err on the side of not deduplicating any records that refer to distinct articles, and it is important to perform deduplication online in real time. Our deduplication module is designed with these constraints in mind. Articles that share the same publication year are compared sequentially on parameters including PubMed ID number, digital object identifier, journal name, article title and author list, using text approximation techniques. In a review of Metta searches carried out by public users, we found that the deduplication module was more effective at identifying duplicates than EndNote without making any erroneous assignments.
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Rule-based deduplication of article records from bibliographic databases
Jiang, Yu; Lin, Can; Meng, Weiyi; Yu, Clement; Cohen, Aaron M.; Smalheiser, Neil R.
2014-01-01
We recently designed and deployed a metasearch engine, Metta, that sends queries and retrieves search results from five leading biomedical databases: PubMed, EMBASE, CINAHL, PsycINFO and the Cochrane Central Register of Controlled Trials. Because many articles are indexed in more than one of these databases, it is desirable to deduplicate the retrieved article records. This is not a trivial problem because data fields contain a lot of missing and erroneous entries, and because certain types of information are recorded differently (and inconsistently) in the different databases. The present report describes our rule-based method for deduplicating article records across databases and includes an open-source script module that can be deployed freely. Metta was designed to satisfy the particular needs of people who are writing systematic reviews in evidence-based medicine. These users want the highest possible recall in retrieval, so it is important to err on the side of not deduplicating any records that refer to distinct articles, and it is important to perform deduplication online in real time. Our deduplication module is designed with these constraints in mind. Articles that share the same publication year are compared sequentially on parameters including PubMed ID number, digital object identifier, journal name, article title and author list, using text approximation techniques. In a review of Metta searches carried out by public users, we found that the deduplication module was more effective at identifying duplicates than EndNote without making any erroneous assignments. PMID:24434031
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Ontology-Oriented Programming for Biomedical Informatics.
Lamy, Jean-Baptiste
2016-01-01
Ontologies are now widely used in the biomedical domain. However, it is difficult to manipulate ontologies in a computer program and, consequently, it is not easy to integrate ontologies with databases or websites. Two main approaches have been proposed for accessing ontologies in a computer program: traditional API (Application Programming Interface) and ontology-oriented programming, either static or dynamic. In this paper, we will review these approaches and discuss their appropriateness for biomedical ontologies. We will also present an experience feedback about the integration of an ontology in a computer software during the VIIIP research project. Finally, we will present OwlReady, the solution we developed.
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Increased co-first authorships in biomedical and clinical publications: a call for recognition.
Conte, Marisa L; Maat, Stacy L; Omary, M Bishr
2013-10-01
There has been a dramatic increase in the number and percentage of publications in biomedical and clinical journals in which two or more coauthors claim first authorship, with a change in some journals from no joint first authorship in 1990 to co-first authorship of >30% of all research publications in 2012. As biomedical and clinical research become increasingly complex and team-driven, and given the importance attributed to first authorship by grant reviewers and promotion and tenure committees, the time is ripe for journals, bibliographic databases, and authors to highlight equal first author contributions of published original research.
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An Open-source Toolbox for Analysing and Processing PhysioNet Databases in MATLAB and Octave.
Silva, Ikaro; Moody, George B
The WaveForm DataBase (WFDB) Toolbox for MATLAB/Octave enables integrated access to PhysioNet's software and databases. Using the WFDB Toolbox for MATLAB/Octave, users have access to over 50 physiological databases in PhysioNet. The toolbox provides access over 4 TB of biomedical signals including ECG, EEG, EMG, and PLETH. Additionally, most signals are accompanied by metadata such as medical annotations of clinical events: arrhythmias, sleep stages, seizures, hypotensive episodes, etc. Users of this toolbox should easily be able to reproduce, validate, and compare results published based on PhysioNet's software and databases.
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2014-11-01
sematic type. Injury or Poisoning inpo T037 Anatomical Abnormality anab T190 Given a document D, a concept vector = {1, 2, … , ...integrating biomedical terminology . Nucleic acids research 32, Database issue (2004), 267–270. 5. Chapman, W.W., Hillert, D., Velupillai, S., et...Conference (TREC), (2011). 9. Koopman, B. and Zuccon, G. Understanding negation and family history to improve clinical information retrieval. Proceedings
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A novel method for efficient archiving and retrieval of biomedical images using MPEG-7
NASA Astrophysics Data System (ADS)
Meyer, Joerg; Pahwa, Ash
2004-10-01
Digital archiving and efficient retrieval of radiological scans have become critical steps in contemporary medical diagnostics. Since more and more images and image sequences (single scans or video) from various modalities (CT/MRI/PET/digital X-ray) are now available in digital formats (e.g., DICOM-3), hospitals and radiology clinics need to implement efficient protocols capable of managing the enormous amounts of data generated daily in a typical clinical routine. We present a method that appears to be a viable way to eliminate the tedious step of manually annotating image and video material for database indexing. MPEG-7 is a new framework that standardizes the way images are characterized in terms of color, shape, and other abstract, content-related criteria. A set of standardized descriptors that are automatically generated from an image is used to compare an image to other images in a database, and to compute the distance between two images for a given application domain. Text-based database queries can be replaced with image-based queries using MPEG-7. Consequently, image queries can be conducted without any prior knowledge of the keys that were used as indices in the database. Since the decoding and matching steps are not part of the MPEG-7 standard, this method also enables searches that were not planned by the time the keys were generated.
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Meta-analysis of Huangqi injection for the adjunctive therapy of aplastic anemia
Zhu, Changtai; Gao, Yulu; Jiang, Ting; Hao, Cao; Gao, Zongshuai; Sun, Yongning
2015-01-01
Aplastic anemia therapy remains difficult, due to lack of effective treatment regimens. In recent years, Huangqi injection for the adjunctive therapy of aplastic anemia has been reported in many clinical trials. Considering that Huangqi injection may be a novel approach to aplastic anemia treatment, we conducted a meta-analysis of clinical controlled trials to assess the clinical value of Huangqi injection in the treatment of aplastic anemia. We searched the Chinese Biomedical Literature Database (CBM), China National Knowledge Infrastructure (CNKI), Chinese Scientific Journals Full-text Database (VIP), Wanfang Database, PubMed and EMBASE database to collect the data about the trials of Huangqi injection combined with androgens for treating aplastic anemia. A total of ten studies involving 720 patients with aplastic anemia were included in this study. The meta-analysis showed significant increases in the pool effectiveness rate, white blood cells (WBC), haematoglobin (Hb), platelets (PLT), and reticulocytes (Ret) between the experimental group versus the control group. No severe side effects were found in this study. However, the lower Jadad scores and asymmetric funnel plot degrades the validity of the meta-analysis as the clinical evidence. Therefore, Huangqi injection may significantly enhance the efficacy of androgens for aplastic anemia, suggesting that the novel approach of Chinese traditional medicine combined with Western medicine is promising. The exact outcome required confirmation with rigorously well-designed multi-center trials. PMID:26379817
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Smartphone home monitoring of ECG
NASA Astrophysics Data System (ADS)
Szu, Harold; Hsu, Charles; Moon, Gyu; Landa, Joseph; Nakajima, Hiroshi; Hata, Yutaka
2012-06-01
A system of ambulatory, halter, electrocardiography (ECG) monitoring system has already been commercially available for recording and transmitting heartbeats data by the Internet. However, it enjoys the confidence with a reservation and thus a limited market penetration, our system was targeting at aging global villagers having an increasingly biomedical wellness (BMW) homecare needs, not hospital related BMI (biomedical illness). It was designed within SWaP-C (Size, Weight, and Power, Cost) using 3 innovative modules: (i) Smart Electrode (lowpower mixed signal embedded with modern compressive sensing and nanotechnology to improve the electrodes' contact impedance); (ii) Learnable Database (in terms of adaptive wavelets transform QRST feature extraction, Sequential Query Relational database allowing home care monitoring retrievable Aided Target Recognition); (iii) Smartphone (touch screen interface, powerful computation capability, caretaker reporting with GPI, ID, and patient panic button for programmable emergence procedure). It can provide a supplementary home screening system for the post or the pre-diagnosis care at home with a build-in database searchable with the time, the place, and the degree of urgency happened, using in-situ screening.
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Argo: an integrative, interactive, text mining-based workbench supporting curation
Rak, Rafal; Rowley, Andrew; Black, William; Ananiadou, Sophia
2012-01-01
Curation of biomedical literature is often supported by the automatic analysis of textual content that generally involves a sequence of individual processing components. Text mining (TM) has been used to enhance the process of manual biocuration, but has been focused on specific databases and tasks rather than an environment integrating TM tools into the curation pipeline, catering for a variety of tasks, types of information and applications. Processing components usually come from different sources and often lack interoperability. The well established Unstructured Information Management Architecture is a framework that addresses interoperability by defining common data structures and interfaces. However, most of the efforts are targeted towards software developers and are not suitable for curators, or are otherwise inconvenient to use on a higher level of abstraction. To overcome these issues we introduce Argo, an interoperable, integrative, interactive and collaborative system for text analysis with a convenient graphic user interface to ease the development of processing workflows and boost productivity in labour-intensive manual curation. Robust, scalable text analytics follow a modular approach, adopting component modules for distinct levels of text analysis. The user interface is available entirely through a web browser that saves the user from going through often complicated and platform-dependent installation procedures. Argo comes with a predefined set of processing components commonly used in text analysis, while giving the users the ability to deposit their own components. The system accommodates various areas and levels of user expertise, from TM and computational linguistics to ontology-based curation. One of the key functionalities of Argo is its ability to seamlessly incorporate user-interactive components, such as manual annotation editors, into otherwise completely automatic pipelines. As a use case, we demonstrate the functionality of an in-built manual annotation editor that is well suited for in-text corpus annotation tasks. Database URL: http://www.nactem.ac.uk/Argo PMID:22434844
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Biomedical Requirements for High Productivity Computing Systems
2005-04-01
server at http://www.ncbi.nlm.nih.gov/BLAST/. There are many variants of BLAST, including: 1. BLASTN - Compares a DNA query to a DNA database. Searches ...database (3 reading frames from each strand of the DNA) searching . 13 4. TBLASTN - Compares a protein query to a DNA database, in the 6 possible...the molecular during this phase. After eliminating molecules that could not match the query , an atom-by-atom search for the molecules in conducted
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Biomedical journals in Republic of Macedonia: the current state.
Polenakovic, Momir; Danevska, Lenche
2014-01-01
Several biomedical journals in the Republic of Macedonia have succeeded in maintaining regular publication over the years, but only a few have a long-standing tradition. In this paper we present the basic characteristics of 18 biomedical journals that have been published without a break in the Republic of Macedonia. Of these, more details are given for 14 journals, a particular emphasis being on the journal Prilozi/Contributions of the Macedonian Academy of Sciences and Arts, Section of Medical Sciences as one of the journals with a long-term publishing tradition and one of the journals included in the Medline/PubMed database. A brief or broad description is given for the following journals: Macedonian Medical Review, Acta Morphologica, Physioacta, MJMS-Macedonian Journal of Medical Sciences, International Medical Journal Medicus, Archives of Public Health, Epilepsy, Macedonian Orthopaedics and Traumatology Journal, BANTAO Journal, Macedonian Dental Review, Macedonian Pharmaceutical Bulletin, Macedonian Veterinary Review, Journal of Special Education and Rehabilitation, Balkan Journal of Medical Genetics, Contributions of the Macedonian Scientific Society of Bitola, Vox Medici, Social Medicine: Professional Journal for Public Health, and Prilozi/Contributions of the Macedonian Academy of Sciences and Arts. Journals from Macedonia should aim to be published regularly, should comply with the Uniform requirements for manuscripts submitted to biomedical journals, and with the recommendations of reliable organizations working in the field of publishing and research. These are the key prerequisites which Macedonian journals have to accomplish in order to be included in renowned international bibliographic databases. Thus the results of biomedical science from the Republic of Macedonia will be presented to the international scientific arena.
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Integrating a Hypernymic Proposition Interpreter into a Semantic Processor for Biomedical Texts
Fiszman, Marcelo; Rindflesch, Thomas C.; Kilicoglu, Halil
2003-01-01
Semantic processing provides the potential for producing high quality results in natural language processing (NLP) applications in the biomedical domain. In this paper, we address a specific semantic phenomenon, the hypernymic proposition, and concentrate on integrating the interpretation of such predications into a more general semantic processor in order to improve overall accuracy. A preliminary evaluation assesses the contribution of hypernymic propositions in providing more specific semantic predications and thus improving effectiveness in retrieving treatment propositions in MEDLINE abstracts. Finally, we discuss the generalization of this methodology to additional semantic propositions as well as other types of biomedical texts. PMID:14728170
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Ponnaiah, Paulraj; Vnoothenei, Nagiah; Chandramohan, Muruganandham; Thevarkattil, Mohamed Javad Pazhayakath
2018-01-30
Polyhydroxyalkanoates are bio-based, biodegradable naturally occurring polymers produced by a wide range of organisms, from bacteria to higher mammals. The properties and biocompatibility of PHA make it possible for a wide spectrum of applications. In this context, we analyze the potential applications of PHA in biomedical science by exploring the global trend through the patent survey. The survey suggests that PHA is an attractive candidate in such a way that their applications are widely distributed in the medical industry, drug delivery system, dental material, tissue engineering, packaging material as well as other useful products. In our present study, we explored patents associated with various biomedical applications of polyhydroxyalkanoates. Patent databases of European Patent Office, United States Patent and Trademark Office and World Intellectual Property Organization were mined. We developed an intensive exploration approach to eliminate overlapping patents and sort out significant patents. We demarcated the keywords and search criterions and established search patterns for the database request. We retrieved documents within the recent 6 years, 2010 to 2016 and sort out the collected data stepwise to gather the most appropriate documents in patent families for further scrutiny. By this approach, we retrieved 23,368 patent documents from all the three databases and the patent titles were further analyzed for the relevance of polyhydroxyalkanoates in biomedical applications. This ensued in the documentation of approximately 226 significant patents associated with biomedical applications of polyhydroxyalkanoates and the information was classified into six major groups. Polyhydroxyalkanoates has been patented in such a way that their applications are widely distributed in the medical industry, drug delivery system, dental material, tissue engineering, packaging material as well as other useful products. There are many avenues through which PHA & PHB could be used. Our analysis shows patent information can be used to identify various applications of PHA and its representatives in the biomedical field. Upcoming studies can focus on the application of PHA in the different field to discover the related topics and associate to this study. We believe that this approach of analysis and findings can initiate new researchers to undertake similar kind of studies in their represented field to fill the gap between the patent articles and researchpublications. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.
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Learning the Structure of Biomedical Relationships from Unstructured Text
Percha, Bethany; Altman, Russ B.
2015-01-01
The published biomedical research literature encompasses most of our understanding of how drugs interact with gene products to produce physiological responses (phenotypes). Unfortunately, this information is distributed throughout the unstructured text of over 23 million articles. The creation of structured resources that catalog the relationships between drugs and genes would accelerate the translation of basic molecular knowledge into discoveries of genomic biomarkers for drug response and prediction of unexpected drug-drug interactions. Extracting these relationships from natural language sentences on such a large scale, however, requires text mining algorithms that can recognize when different-looking statements are expressing similar ideas. Here we describe a novel algorithm, Ensemble Biclustering for Classification (EBC), that learns the structure of biomedical relationships automatically from text, overcoming differences in word choice and sentence structure. We validate EBC's performance against manually-curated sets of (1) pharmacogenomic relationships from PharmGKB and (2) drug-target relationships from DrugBank, and use it to discover new drug-gene relationships for both knowledge bases. We then apply EBC to map the complete universe of drug-gene relationships based on their descriptions in Medline, revealing unexpected structure that challenges current notions about how these relationships are expressed in text. For instance, we learn that newer experimental findings are described in consistently different ways than established knowledge, and that seemingly pure classes of relationships can exhibit interesting chimeric structure. The EBC algorithm is flexible and adaptable to a wide range of problems in biomedical text mining. PMID:26219079
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Biomedical waste management in Ayurveda hospitals - current practices & future prospectives.
Rajan, Renju; Robin, Delvin T; M, Vandanarani
2018-03-16
Biomedical waste management is an integral part of traditional and contemporary system of health care. The paper focuses on the identification and classification of biomedical wastes in Ayurvedic hospitals, current practices of its management in Ayurveda hospitals and its future prospective. Databases like PubMed (1975-2017 Feb), Scopus (1960-2017), AYUSH Portal, DOAJ, DHARA and Google scholar were searched. We used the medical subject headings 'biomedical waste' and 'health care waste' for identification and classification. The terms 'biomedical waste management', 'health care waste management' alone and combined with 'Ayurveda' or 'Ayurvedic' for current practices and recent advances in the treatment of these wastes were used. We made a humble attempt to categorize the biomedical wastes from Ayurvedic hospitals as the available data about its grouping is very scarce. Proper biomedical waste management is the mainstay of hospital cleanliness, hospital hygiene and maintenance activities. Current disposal techniques adopted for Ayurveda biomedical wastes are - sewage/drains, incineration and land fill. But these methods are having some merits as well as demerits. Our review has identified a number of interesting areas for future research such as the logical application of bioremediation techniques in biomedical waste management and the usage of effective micro-organisms and solar energy in waste disposal. Copyright © 2017 Transdisciplinary University, Bangalore and World Ayurveda Foundation. Published by Elsevier B.V. All rights reserved.
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G-Bean: an ontology-graph based web tool for biomedical literature retrieval
2014-01-01
Background Currently, most people use NCBI's PubMed to search the MEDLINE database, an important bibliographical information source for life science and biomedical information. However, PubMed has some drawbacks that make it difficult to find relevant publications pertaining to users' individual intentions, especially for non-expert users. To ameliorate the disadvantages of PubMed, we developed G-Bean, a graph based biomedical search engine, to search biomedical articles in MEDLINE database more efficiently. Methods G-Bean addresses PubMed's limitations with three innovations: (1) Parallel document index creation: a multithreaded index creation strategy is employed to generate the document index for G-Bean in parallel; (2) Ontology-graph based query expansion: an ontology graph is constructed by merging four major UMLS (Version 2013AA) vocabularies, MeSH, SNOMEDCT, CSP and AOD, to cover all concepts in National Library of Medicine (NLM) database; a Personalized PageRank algorithm is used to compute concept relevance in this ontology graph and the Term Frequency - Inverse Document Frequency (TF-IDF) weighting scheme is used to re-rank the concepts. The top 500 ranked concepts are selected for expanding the initial query to retrieve more accurate and relevant information; (3) Retrieval and re-ranking of documents based on user's search intention: after the user selects any article from the existing search results, G-Bean analyzes user's selections to determine his/her true search intention and then uses more relevant and more specific terms to retrieve additional related articles. The new articles are presented to the user in the order of their relevance to the already selected articles. Results Performance evaluation with 106 OHSUMED benchmark queries shows that G-Bean returns more relevant results than PubMed does when using these queries to search the MEDLINE database. PubMed could not even return any search result for some OHSUMED queries because it failed to form the appropriate Boolean query statement automatically from the natural language query strings. G-Bean is available at http://bioinformatics.clemson.edu/G-Bean/index.php. Conclusions G-Bean addresses PubMed's limitations with ontology-graph based query expansion, automatic document indexing, and user search intention discovery. It shows significant advantages in finding relevant articles from the MEDLINE database to meet the information need of the user. PMID:25474588
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2013-01-01
Background Due to the growing number of biomedical entries in data repositories of the National Center for Biotechnology Information (NCBI), it is difficult to collect, manage and process all of these entries in one place by third-party software developers without significant investment in hardware and software infrastructure, its maintenance and administration. Web services allow development of software applications that integrate in one place the functionality and processing logic of distributed software components, without integrating the components themselves and without integrating the resources to which they have access. This is achieved by appropriate orchestration or choreography of available Web services and their shared functions. After the successful application of Web services in the business sector, this technology can now be used to build composite software tools that are oriented towards biomedical data processing. Results We have developed a new tool for efficient and dynamic data exploration in GenBank and other NCBI databases. A dedicated search GenBank system makes use of NCBI Web services and a package of Entrez Programming Utilities (eUtils) in order to provide extended searching capabilities in NCBI data repositories. In search GenBank users can use one of the three exploration paths: simple data searching based on the specified user’s query, advanced data searching based on the specified user’s query, and advanced data exploration with the use of macros. search GenBank orchestrates calls of particular tools available through the NCBI Web service providing requested functionality, while users interactively browse selected records in search GenBank and traverse between NCBI databases using available links. On the other hand, by building macros in the advanced data exploration mode, users create choreographies of eUtils calls, which can lead to the automatic discovery of related data in the specified databases. Conclusions search GenBank extends standard capabilities of the NCBI Entrez search engine in querying biomedical databases. The possibility of creating and saving macros in the search GenBank is a unique feature and has a great potential. The potential will further grow in the future with the increasing density of networks of relationships between data stored in particular databases. search GenBank is available for public use at http://sgb.biotools.pl/. PMID:23452691
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Mrozek, Dariusz; Małysiak-Mrozek, Bożena; Siążnik, Artur
2013-03-01
Due to the growing number of biomedical entries in data repositories of the National Center for Biotechnology Information (NCBI), it is difficult to collect, manage and process all of these entries in one place by third-party software developers without significant investment in hardware and software infrastructure, its maintenance and administration. Web services allow development of software applications that integrate in one place the functionality and processing logic of distributed software components, without integrating the components themselves and without integrating the resources to which they have access. This is achieved by appropriate orchestration or choreography of available Web services and their shared functions. After the successful application of Web services in the business sector, this technology can now be used to build composite software tools that are oriented towards biomedical data processing. We have developed a new tool for efficient and dynamic data exploration in GenBank and other NCBI databases. A dedicated search GenBank system makes use of NCBI Web services and a package of Entrez Programming Utilities (eUtils) in order to provide extended searching capabilities in NCBI data repositories. In search GenBank users can use one of the three exploration paths: simple data searching based on the specified user's query, advanced data searching based on the specified user's query, and advanced data exploration with the use of macros. search GenBank orchestrates calls of particular tools available through the NCBI Web service providing requested functionality, while users interactively browse selected records in search GenBank and traverse between NCBI databases using available links. On the other hand, by building macros in the advanced data exploration mode, users create choreographies of eUtils calls, which can lead to the automatic discovery of related data in the specified databases. search GenBank extends standard capabilities of the NCBI Entrez search engine in querying biomedical databases. The possibility of creating and saving macros in the search GenBank is a unique feature and has a great potential. The potential will further grow in the future with the increasing density of networks of relationships between data stored in particular databases. search GenBank is available for public use at http://sgb.biotools.pl/.
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G-Bean: an ontology-graph based web tool for biomedical literature retrieval.
Wang, James Z; Zhang, Yuanyuan; Dong, Liang; Li, Lin; Srimani, Pradip K; Yu, Philip S
2014-01-01
Currently, most people use NCBI's PubMed to search the MEDLINE database, an important bibliographical information source for life science and biomedical information. However, PubMed has some drawbacks that make it difficult to find relevant publications pertaining to users' individual intentions, especially for non-expert users. To ameliorate the disadvantages of PubMed, we developed G-Bean, a graph based biomedical search engine, to search biomedical articles in MEDLINE database more efficiently. G-Bean addresses PubMed's limitations with three innovations: (1) Parallel document index creation: a multithreaded index creation strategy is employed to generate the document index for G-Bean in parallel; (2) Ontology-graph based query expansion: an ontology graph is constructed by merging four major UMLS (Version 2013AA) vocabularies, MeSH, SNOMEDCT, CSP and AOD, to cover all concepts in National Library of Medicine (NLM) database; a Personalized PageRank algorithm is used to compute concept relevance in this ontology graph and the Term Frequency - Inverse Document Frequency (TF-IDF) weighting scheme is used to re-rank the concepts. The top 500 ranked concepts are selected for expanding the initial query to retrieve more accurate and relevant information; (3) Retrieval and re-ranking of documents based on user's search intention: after the user selects any article from the existing search results, G-Bean analyzes user's selections to determine his/her true search intention and then uses more relevant and more specific terms to retrieve additional related articles. The new articles are presented to the user in the order of their relevance to the already selected articles. Performance evaluation with 106 OHSUMED benchmark queries shows that G-Bean returns more relevant results than PubMed does when using these queries to search the MEDLINE database. PubMed could not even return any search result for some OHSUMED queries because it failed to form the appropriate Boolean query statement automatically from the natural language query strings. G-Bean is available at http://bioinformatics.clemson.edu/G-Bean/index.php. G-Bean addresses PubMed's limitations with ontology-graph based query expansion, automatic document indexing, and user search intention discovery. It shows significant advantages in finding relevant articles from the MEDLINE database to meet the information need of the user.
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Securely and Flexibly Sharing a Biomedical Data Management System
Wang, Fusheng; Hussels, Phillip; Liu, Peiya
2011-01-01
Biomedical database systems need not only to address the issues of managing complex data, but also to provide data security and access control to the system. These include not only system level security, but also instance level access control such as access of documents, schemas, or aggregation of information. The latter is becoming more important as multiple users can share a single scientific data management system to conduct their research, while data have to be protected before they are published or IP-protected. This problem is challenging as users’ needs for data security vary dramatically from one application to another, in terms of who to share with, what resources to be shared, and at what access level. We develop a comprehensive data access framework for a biomedical data management system SciPort. SciPort provides fine-grained multi-level space based access control of resources at not only object level (documents and schemas), but also space level (resources set aggregated in a hierarchy way). Furthermore, to simplify the management of users and privileges, customizable role-based user model is developed. The access control is implemented efficiently by integrating access privileges into the backend XML database, thus efficient queries are supported. The secure access approach we take makes it possible for multiple users to share the same biomedical data management system with flexible access management and high data security. PMID:21625285
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The BioIntelligence Framework: a new computational platform for biomedical knowledge computing
Farley, Toni; Kiefer, Jeff; Lee, Preston; Von Hoff, Daniel; Trent, Jeffrey M; Colbourn, Charles
2013-01-01
Breakthroughs in molecular profiling technologies are enabling a new data-intensive approach to biomedical research, with the potential to revolutionize how we study, manage, and treat complex diseases. The next great challenge for clinical applications of these innovations will be to create scalable computational solutions for intelligently linking complex biomedical patient data to clinically actionable knowledge. Traditional database management systems (DBMS) are not well suited to representing complex syntactic and semantic relationships in unstructured biomedical information, introducing barriers to realizing such solutions. We propose a scalable computational framework for addressing this need, which leverages a hypergraph-based data model and query language that may be better suited for representing complex multi-lateral, multi-scalar, and multi-dimensional relationships. We also discuss how this framework can be used to create rapid learning knowledge base systems to intelligently capture and relate complex patient data to biomedical knowledge in order to automate the recovery of clinically actionable information. PMID:22859646
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Disambiguating ambiguous biomedical terms in biomedical narrative text: an unsupervised method.
Liu, H; Lussier, Y A; Friedman, C
2001-08-01
With the growing use of Natural Language Processing (NLP) techniques for information extraction and concept indexing in the biomedical domain, a method that quickly and efficiently assigns the correct sense of an ambiguous biomedical term in a given context is needed concurrently. The current status of word sense disambiguation (WSD) in the biomedical domain is that handcrafted rules are used based on contextual material. The disadvantages of this approach are (i) generating WSD rules manually is a time-consuming and tedious task, (ii) maintenance of rule sets becomes increasingly difficult over time, and (iii) handcrafted rules are often incomplete and perform poorly in new domains comprised of specialized vocabularies and different genres of text. This paper presents a two-phase unsupervised method to build a WSD classifier for an ambiguous biomedical term W. The first phase automatically creates a sense-tagged corpus for W, and the second phase derives a classifier for W using the derived sense-tagged corpus as a training set. A formative experiment was performed, which demonstrated that classifiers trained on the derived sense-tagged corpora achieved an overall accuracy of about 97%, with greater than 90% accuracy for each individual ambiguous term.
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Should we search Chinese biomedical databases when performing systematic reviews?
Cohen, Jérémie F; Korevaar, Daniël A; Wang, Junfeng; Spijker, René; Bossuyt, Patrick M
2015-03-06
Chinese biomedical databases contain a large number of publications available to systematic reviewers, but it is unclear whether they are used for synthesizing the available evidence. We report a case of two systematic reviews on the accuracy of anti-cyclic citrullinated peptide for diagnosing rheumatoid arthritis. In one of these, the authors did not search Chinese databases; in the other, they did. We additionally assessed the extent to which Cochrane reviewers have searched Chinese databases in a systematic overview of the Cochrane Library (inception to 2014). The two diagnostic reviews included a total of 269 unique studies, but only 4 studies were included in both reviews. The first review included five studies published in the Chinese language (out of 151) while the second included 114 (out of 118). The summary accuracy estimates from the two reviews were comparable. Only 243 of the published 8,680 Cochrane reviews (less than 3%) searched one or more of the five major Chinese databases. These Chinese databases index about 2,500 journals, of which less than 6% are also indexed in MEDLINE. All 243 Cochrane reviews evaluated an intervention, 179 (74%) had at least one author with a Chinese affiliation; 118 (49%) addressed a topic in complementary or alternative medicine. Although searching Chinese databases may lead to the identification of a large amount of additional clinical evidence, Cochrane reviewers have rarely included them in their search strategy. We encourage future initiatives to evaluate more systematically the relevance of searching Chinese databases, as well as collaborative efforts to allow better incorporation of Chinese resources in systematic reviews.
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Amelogenin test: From forensics to quality control in clinical and biochemical genomics.
Francès, F; Portolés, O; González, J I; Coltell, O; Verdú, F; Castelló, A; Corella, D
2007-01-01
The increasing number of samples from the biomedical genetic studies and the number of centers participating in the same involves increasing risk of mistakes in the different sample handling stages. We have evaluated the usefulness of the amelogenin test for quality control in sample identification. Amelogenin test (frequently used in forensics) was undertaken on 1224 individuals participating in a biomedical study. Concordance between referred sex in the database and amelogenin test was estimated. Additional sex-error genetic detecting systems were developed. The overall concordance rate was 99.84% (1222/1224). Two samples showed a female amelogenin test outcome, being codified as males in the database. The first, after checking sex-specific biochemical and clinical profile data was found to be due to a codification error in the database. In the second, after checking the database, no apparent error was discovered because a correct male profile was found. False negatives in amelogenin male sex determination were discarded by additional tests, and feminine sex was confirmed. A sample labeling error was revealed after a new DNA extraction. The amelogenin test is a useful quality control tool for detecting sex-identification errors in large genomic studies, and can contribute to increase its validity.
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ANTIBACTERIAL EFFICACY OF INTRACANAL MEDICAMENTS ON BACTERIAL BIOFILM: A CRITICAL REVIEW
Estrela, Carlos; Sydney, Gilson Blitzkow; Figueiredo, José Antonio Poli; Estrela, Cyntia Rodrigues de Araújo
2009-01-01
The purpose of this paper is to discuss critically the antibacterial efficacy of intracanal medicaments on bacterial biofilm. Longitudinal studies were evaluated by a systematic review of English-language articles retrieved from electronic biomedical journal databases (MEDLINE, EMBASE, CENTRAL) and handsearching records, using different matches of keywords for root canal biofilm, between 1966 and August 1st, 2007. The selected articles were identified from titles, abstracts and full-text articles by two independent reviewers, considering the tabulated inclusion and exclusion criteria. Disagreements were resolved by consensus. The search retrieved 91 related articles, of which 8.8% referred to in vivo studies demonstrating the lack of efficacy of endodontic therapy on bacterial biofilm. Intracanal medicaments were found to have a limited action against bacterial biofilm. PMID:19148398
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[Systematic literature search in PubMed : A short introduction].
Blümle, A; Lagrèze, W A; Motschall, E
2018-03-01
In order to identify current (and relevant) evidence for a specific clinical question within the unmanageable amount of information available, solid skills in performing a systematic literature search are essential. An efficient approach is to search a biomedical database containing relevant literature citations of study reports. The best known database is MEDLINE, which is searchable for free via the PubMed interface. In this article, we explain step by step how to perform a systematic literature search via PubMed by means of an example research question in the field of ophthalmology. First, we demonstrate how to translate the clinical problem into a well-framed and searchable research question, how to identify relevant search terms and how to conduct a text word search and a search with keywords in medical subject headings (MeSH) terms. We then show how to limit the number of search results if the search yields too many irrelevant hits and how to increase the number in the case of too few citations. Finally, we summarize all essential principles that guide a literature search via PubMed.
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Wu, Si-Jia; Xiong, Xian-Ze; Lin, Yi-Xin; Cheng, Nan-Sheng
2013-02-01
Our purpose was to assess the prophylactic antiemetic effects of ondansetron versus granisetron for laparoscopic cholecystectomy. We searched Medline, Cochrane Central Register of Controlled Trials, PubMed, Embase, Science Citation Index Expanded, Foreign Medical Journal Full-Text Service, China National Knowledge Infrastructure Whole Article Database, Chinese Biomedical Database, and the Google Scholar. We calculated the risk ratio (RR) with 95% confidence interval (CI) for dichotomous data. The χ(2) test and I(2) value were used to assess heterogeneity. The merged early incidence of postoperative nausea and vomiting (PONV) in ondansetron group (42.9%) was higher than granisetron group (34.3%) (RR = 1.25, 95% CI, 0.82-1.92, P=0.31, I(2) = 48%). The merged total incidence of PONV in ondansetron group (38.7%) was higher than granisetron group (34.2%) (RR = 1.13, 95% CI, 0.82-1.56, P = 0.46, I(2) = 39%), although these differences were not statistically significant. Ondansetron is equivalent to granisetron for preventing early and total incidence of PONV after laparoscopic cholecystectomy.
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Vishnyakova, Dina; Pasche, Emilie; Ruch, Patrick
2012-01-01
We report on the original integration of an automatic text categorization pipeline, so-called ToxiCat (Toxicogenomic Categorizer), that we developed to perform biomedical documents classification and prioritization in order to speed up the curation of the Comparative Toxicogenomics Database (CTD). The task can be basically described as a binary classification task, where a scoring function is used to rank a selected set of articles. Then components of a question-answering system are used to extract CTD-specific annotations from the ranked list of articles. The ranking function is generated using a Support Vector Machine, which combines three main modules: an information retrieval engine for MEDLINE (EAGLi), a gene normalization service (NormaGene) developed for a previous BioCreative campaign and finally, a set of answering components and entity recognizer for diseases and chemicals. The main components of the pipeline are publicly available both as web application and web services. The specific integration performed for the BioCreative competition is available via a web user interface at http://pingu.unige.ch:8080/Toxicat.
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A client/server system for Internet access to biomedical text/image databanks.
Thoma, G R; Long, L R; Berman, L E
1996-01-01
Internet access to mixed text/image databanks is finding application in the medical world. An example is a database of medical X-rays and associated data consisting of demographic, socioeconomic, physician's exam, medical laboratory and other information collected as part of a nationwide health survey conducted by the government. Another example is a collection of digitized cryosection images, CT and MR taken of cadavers as part of the National Library of Medicine's Visible Human Project. In both cases, the challenge is to provide access to both the image and the associated text for a wide end user community to create atlases, conduct epidemiological studies, to develop image-specific algorithms for compression, enhancement and other types of image processing, among many other applications. The databanks mentioned above are being created in prototype form. This paper describes the prototype system developed for the archiving of the data and the client software to enable a broad range of end users to access the archive, retrieve text and image data, display the data and manipulate the images. System design considerations include; data organization in a relational database management system with object-oriented extensions; a hierarchical organization of the image data by different resolution levels for different user classes; client design based on common hardware and software platforms incorporating SQL search capability, X Window, Motif and TAE (a development environment supporting rapid prototyping and management of graphic-oriented user interfaces); potential to include ultra high resolution display monitors as a user option; intuitive user interface paradigm for building complex queries; and contrast enhancement, magnification and mensuration tools for better viewing by the user.
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Unsupervised discovery of information structure in biomedical documents.
Kiela, Douwe; Guo, Yufan; Stenius, Ulla; Korhonen, Anna
2015-04-01
Information structure (IS) analysis is a text mining technique, which classifies text in biomedical articles into categories that capture different types of information, such as objectives, methods, results and conclusions of research. It is a highly useful technique that can support a range of Biomedical Text Mining tasks and can help readers of biomedical literature find information of interest faster, accelerating the highly time-consuming process of literature review. Several approaches to IS analysis have been presented in the past, with promising results in real-world biomedical tasks. However, all existing approaches, even weakly supervised ones, require several hundreds of hand-annotated training sentences specific to the domain in question. Because biomedicine is subject to considerable domain variation, such annotations are expensive to obtain. This makes the application of IS analysis across biomedical domains difficult. In this article, we investigate an unsupervised approach to IS analysis and evaluate the performance of several unsupervised methods on a large corpus of biomedical abstracts collected from PubMed. Our best unsupervised algorithm (multilevel-weighted graph clustering algorithm) performs very well on the task, obtaining over 0.70 F scores for most IS categories when applied to well-known IS schemes. This level of performance is close to that of lightly supervised IS methods and has proven sufficient to aid a range of practical tasks. Thus, using an unsupervised approach, IS could be applied to support a wide range of tasks across sub-domains of biomedicine. We also demonstrate that unsupervised learning brings novel insights into IS of biomedical literature and discovers information categories that are not present in any of the existing IS schemes. The annotated corpus and software are available at http://www.cl.cam.ac.uk/∼dk427/bio14info.html. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
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ERIC Educational Resources Information Center
Biomedical Interdisciplinary Curriculum Project, Berkeley, CA.
This student text presents instructional materials for a unit of science within the Biomedical Interdisciplinary Curriculum Project (BICP), a two-year interdisciplinary precollege curriculum aimed at preparing high school students for entry into college and vocational programs leading to a career in the health field. Lessons concentrate on the…
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Ozyurt, Ibrahim Burak; Grethe, Jeffrey S; Martone, Maryann E; Bandrowski, Anita E
2016-01-01
The NIF Registry developed and maintained by the Neuroscience Information Framework is a cooperative project aimed at cataloging research resources, e.g., software tools, databases and tissue banks, funded largely by governments and available as tools to research scientists. Although originally conceived for neuroscience, the NIF Registry has over the years broadened in the scope to include research resources of general relevance to biomedical research. The current number of research resources listed by the Registry numbers over 13K. The broadening in scope to biomedical science led us to re-christen the NIF Registry platform as SciCrunch. The NIF/SciCrunch Registry has been cataloging the resource landscape since 2006; as such, it serves as a valuable dataset for tracking the breadth, fate and utilization of these resources. Our experience shows research resources like databases are dynamic objects, that can change location and scope over time. Although each record is entered manually and human-curated, the current size of the registry requires tools that can aid in curation efforts to keep content up to date, including when and where such resources are used. To address this challenge, we have developed an open source tool suite, collectively termed RDW: Resource Disambiguator for the (Web). RDW is designed to help in the upkeep and curation of the registry as well as in enhancing the content of the registry by automated extraction of resource candidates from the literature. The RDW toolkit includes a URL extractor from papers, resource candidate screen, resource URL change tracker, resource content change tracker. Curators access these tools via a web based user interface. Several strategies are used to optimize these tools, including supervised and unsupervised learning algorithms as well as statistical text analysis. The complete tool suite is used to enhance and maintain the resource registry as well as track the usage of individual resources through an innovative literature citation index honed for research resources. Here we present an overview of the Registry and show how the RDW tools are used in curation and usage tracking.
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Ozyurt, Ibrahim Burak; Grethe, Jeffrey S.; Martone, Maryann E.; Bandrowski, Anita E.
2016-01-01
The NIF Registry developed and maintained by the Neuroscience Information Framework is a cooperative project aimed at cataloging research resources, e.g., software tools, databases and tissue banks, funded largely by governments and available as tools to research scientists. Although originally conceived for neuroscience, the NIF Registry has over the years broadened in the scope to include research resources of general relevance to biomedical research. The current number of research resources listed by the Registry numbers over 13K. The broadening in scope to biomedical science led us to re-christen the NIF Registry platform as SciCrunch. The NIF/SciCrunch Registry has been cataloging the resource landscape since 2006; as such, it serves as a valuable dataset for tracking the breadth, fate and utilization of these resources. Our experience shows research resources like databases are dynamic objects, that can change location and scope over time. Although each record is entered manually and human-curated, the current size of the registry requires tools that can aid in curation efforts to keep content up to date, including when and where such resources are used. To address this challenge, we have developed an open source tool suite, collectively termed RDW: Resource Disambiguator for the (Web). RDW is designed to help in the upkeep and curation of the registry as well as in enhancing the content of the registry by automated extraction of resource candidates from the literature. The RDW toolkit includes a URL extractor from papers, resource candidate screen, resource URL change tracker, resource content change tracker. Curators access these tools via a web based user interface. Several strategies are used to optimize these tools, including supervised and unsupervised learning algorithms as well as statistical text analysis. The complete tool suite is used to enhance and maintain the resource registry as well as track the usage of individual resources through an innovative literature citation index honed for research resources. Here we present an overview of the Registry and show how the RDW tools are used in curation and usage tracking. PMID:26730820
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FigSum: automatically generating structured text summaries for figures in biomedical literature.
Agarwal, Shashank; Yu, Hong
2009-11-14
Figures are frequently used in biomedical articles to support research findings; however, they are often difficult to comprehend based on their legends alone and information from the full-text articles is required to fully understand them. Previously, we found that the information associated with a single figure is distributed throughout the full-text article the figure appears in. Here, we develop and evaluate a figure summarization system - FigSum, which aggregates this scattered information to improve figure comprehension. For each figure in an article, FigSum generates a structured text summary comprising one sentence from each of the four rhetorical categories - Introduction, Methods, Results and Discussion (IMRaD). The IMRaD category of sentences is predicted by an automated machine learning classifier. Our evaluation shows that FigSum captures 53% of the sentences in the gold standard summaries annotated by biomedical scientists and achieves an average ROUGE-1 score of 0.70, which is higher than a baseline system.
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FigSum: Automatically Generating Structured Text Summaries for Figures in Biomedical Literature
Agarwal, Shashank; Yu, Hong
2009-01-01
Figures are frequently used in biomedical articles to support research findings; however, they are often difficult to comprehend based on their legends alone and information from the full-text articles is required to fully understand them. Previously, we found that the information associated with a single figure is distributed throughout the full-text article the figure appears in. Here, we develop and evaluate a figure summarization system – FigSum, which aggregates this scattered information to improve figure comprehension. For each figure in an article, FigSum generates a structured text summary comprising one sentence from each of the four rhetorical categories – Introduction, Methods, Results and Discussion (IMRaD). The IMRaD category of sentences is predicted by an automated machine learning classifier. Our evaluation shows that FigSum captures 53% of the sentences in the gold standard summaries annotated by biomedical scientists and achieves an average ROUGE-1 score of 0.70, which is higher than a baseline system. PMID:20351812
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Vuckovic-Dekic, Ljiljana; Gavrilovic, Dusica
2016-01-01
To investigate the dynamics of indexing the Journal of the Balkan Union of Oncology (JBUON) in important biomedical databases, the effects on the quantity and type of published articles, and also the countries of the (co)authors of these papers. The process of the JBUON indexing started with EMBASE/Excerpta Medica, followed in 2006 (PUBMED/MEDLINE) and continued every second year in other important biomedical databases, until 2012 when JBUON became Open Access Journal (for even more information please visit www.jbuon.com). Including the next two years for monitoring the effect of the last indexing, we analyzed 9 volumes consisting of 36 issues that were published from January 2006 to December 2014, with regard to the number and category of articles, the contribution of authors from Balkan and non-Balkan countries, and the (co)authorship in the published articles. In the period 2006-2014, 1165 articles of different categories were published in J BUON. The indexing progress of JBUON immediately increased the submission rate, and enlarged the number of publications, original papers in particular, in every volume of JBUON. Authors from Balkan countries contributed in 80.7% of all articles. The average number of coauthors per original article grew slowly and was higher at the end of the investigated period than at the start (6.6 and 5.8, respectively). The progressing covering of JBUON in important biomedical databases and its visibility on international level attracted the attention of a large readership, and submission rate and the number of published articles grew significantly, particularly the number of original papers. This is the most important consequence of the editorial policy which will hopefully lead to even more progress of JBUON in the near future.
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Karabulut, Nevzat
2017-03-01
The aim of this study is to investigate the frequency of incorrect citations and its effects on the impact factor of a specific biomedical journal: the American Journal of Roentgenology. The Cited Reference Search function of Thomson Reuters' Web of Science database (formerly the Institute for Scientific Information's Web of Knowledge database) was used to identify erroneous citations. This was done by entering the journal name into the Cited Work field and entering "2011-2012" into the Cited Year(s) field. The errors in any part of the inaccurately cited references (e.g., author names, title, year, volume, issue, and page numbers) were recorded, and the types of errors (i.e., absent, deficient, or mistyped) were analyzed. Erroneous citations were corrected using the Suggest a Correction function of the Web of Science database. The effect of inaccurate citations on the impact factor of the AJR was calculated. Overall, 183 of 1055 citable articles published in 2011-2012 were inaccurately cited 423 times (mean [± SD], 2.31 ± 4.67 times; range, 1-44 times). Of these 183 articles, 110 (60.1%) were web-only articles and 44 (24.0%) were print articles. The most commonly identified errors were page number errors (44.8%) and misspelling of an author's name (20.2%). Incorrect citations adversely affected the impact factor of the AJR by 0.065 in 2012 and by 0.123 in 2013. Inaccurate citations are not infrequent in biomedical journals, yet they can be detected and corrected using the Web of Science database. Although the accuracy of references is primarily the responsibility of authors, the journal editorial office should also define a periodic inaccurate citation check task and correct erroneous citations to reclaim unnecessarily lost credit.
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LipidPedia: a comprehensive lipid knowledgebase.
Kuo, Tien-Chueh; Tseng, Yufeng Jane
2018-04-10
Lipids are divided into fatty acyls, glycerolipids, glycerophospholipids, sphingolipids, saccharolipids, sterols, prenol lipids and polyketides. Fatty acyls and glycerolipids are commonly used as energy storage, whereas glycerophospholipids, sphingolipids, sterols and saccharolipids are common used as components of cell membranes. Lipids in fatty acyls, glycerophospholipids, sphingolipids and sterols classes play important roles in signaling. Although more than 36 million lipids can be identified or computationally generated, no single lipid database provides comprehensive information on lipids. Furthermore, the complex systematic or common names of lipids make the discovery of related information challenging. Here, we present LipidPedia, a comprehensive lipid knowledgebase. The content of this database is derived from integrating annotation data with full-text mining of 3,923 lipids and more than 400,000 annotations of associated diseases, pathways, functions, and locations that are essential for interpreting lipid functions and mechanisms from over 1,400,000 scientific publications. Each lipid in LipidPedia also has its own entry containing a text summary curated from the most frequently cited diseases, pathways, genes, locations, functions, lipids and experimental models in the biomedical literature. LipidPedia aims to provide an overall synopsis of lipids to summarize lipid annotations and provide a detailed listing of references for understanding complex lipid functions and mechanisms. LipidPedia is available at http://lipidpedia.cmdm.tw. yjtseng@csie.ntu.edu.tw. Supplementary data are available at Bioinformatics online.
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Semantic biomedical resource discovery: a Natural Language Processing framework.
Sfakianaki, Pepi; Koumakis, Lefteris; Sfakianakis, Stelios; Iatraki, Galatia; Zacharioudakis, Giorgos; Graf, Norbert; Marias, Kostas; Tsiknakis, Manolis
2015-09-30
A plethora of publicly available biomedical resources do currently exist and are constantly increasing at a fast rate. In parallel, specialized repositories are been developed, indexing numerous clinical and biomedical tools. The main drawback of such repositories is the difficulty in locating appropriate resources for a clinical or biomedical decision task, especially for non-Information Technology expert users. In parallel, although NLP research in the clinical domain has been active since the 1960s, progress in the development of NLP applications has been slow and lags behind progress in the general NLP domain. The aim of the present study is to investigate the use of semantics for biomedical resources annotation with domain specific ontologies and exploit Natural Language Processing methods in empowering the non-Information Technology expert users to efficiently search for biomedical resources using natural language. A Natural Language Processing engine which can "translate" free text into targeted queries, automatically transforming a clinical research question into a request description that contains only terms of ontologies, has been implemented. The implementation is based on information extraction techniques for text in natural language, guided by integrated ontologies. Furthermore, knowledge from robust text mining methods has been incorporated to map descriptions into suitable domain ontologies in order to ensure that the biomedical resources descriptions are domain oriented and enhance the accuracy of services discovery. The framework is freely available as a web application at ( http://calchas.ics.forth.gr/ ). For our experiments, a range of clinical questions were established based on descriptions of clinical trials from the ClinicalTrials.gov registry as well as recommendations from clinicians. Domain experts manually identified the available tools in a tools repository which are suitable for addressing the clinical questions at hand, either individually or as a set of tools forming a computational pipeline. The results were compared with those obtained from an automated discovery of candidate biomedical tools. For the evaluation of the results, precision and recall measurements were used. Our results indicate that the proposed framework has a high precision and low recall, implying that the system returns essentially more relevant results than irrelevant. There are adequate biomedical ontologies already available, sufficiency of existing NLP tools and quality of biomedical annotation systems for the implementation of a biomedical resources discovery framework, based on the semantic annotation of resources and the use on NLP techniques. The results of the present study demonstrate the clinical utility of the application of the proposed framework which aims to bridge the gap between clinical question in natural language and efficient dynamic biomedical resources discovery.
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NCBO Ontology Recommender 2.0: an enhanced approach for biomedical ontology recommendation.
Martínez-Romero, Marcos; Jonquet, Clement; O'Connor, Martin J; Graybeal, John; Pazos, Alejandro; Musen, Mark A
2017-06-07
Ontologies and controlled terminologies have become increasingly important in biomedical research. Researchers use ontologies to annotate their data with ontology terms, enabling better data integration and interoperability across disparate datasets. However, the number, variety and complexity of current biomedical ontologies make it cumbersome for researchers to determine which ones to reuse for their specific needs. To overcome this problem, in 2010 the National Center for Biomedical Ontology (NCBO) released the Ontology Recommender, which is a service that receives a biomedical text corpus or a list of keywords and suggests ontologies appropriate for referencing the indicated terms. We developed a new version of the NCBO Ontology Recommender. Called Ontology Recommender 2.0, it uses a novel recommendation approach that evaluates the relevance of an ontology to biomedical text data according to four different criteria: (1) the extent to which the ontology covers the input data; (2) the acceptance of the ontology in the biomedical community; (3) the level of detail of the ontology classes that cover the input data; and (4) the specialization of the ontology to the domain of the input data. Our evaluation shows that the enhanced recommender provides higher quality suggestions than the original approach, providing better coverage of the input data, more detailed information about their concepts, increased specialization for the domain of the input data, and greater acceptance and use in the community. In addition, it provides users with more explanatory information, along with suggestions of not only individual ontologies but also groups of ontologies to use together. It also can be customized to fit the needs of different ontology recommendation scenarios. Ontology Recommender 2.0 suggests relevant ontologies for annotating biomedical text data. It combines the strengths of its predecessor with a range of adjustments and new features that improve its reliability and usefulness. Ontology Recommender 2.0 recommends over 500 biomedical ontologies from the NCBO BioPortal platform, where it is openly available (both via the user interface at http://bioportal.bioontology.org/recommender , and via a Web service API).
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Multiple kernels learning-based biological entity relationship extraction method.
Dongliang, Xu; Jingchang, Pan; Bailing, Wang
2017-09-20
Automatic extracting protein entity interaction information from biomedical literature can help to build protein relation network and design new drugs. There are more than 20 million literature abstracts included in MEDLINE, which is the most authoritative textual database in the field of biomedicine, and follow an exponential growth over time. This frantic expansion of the biomedical literature can often be difficult to absorb or manually analyze. Thus efficient and automated search engines are necessary to efficiently explore the biomedical literature using text mining techniques. The P, R, and F value of tag graph method in Aimed corpus are 50.82, 69.76, and 58.61%, respectively. The P, R, and F value of tag graph kernel method in other four evaluation corpuses are 2-5% higher than that of all-paths graph kernel. And The P, R and F value of feature kernel and tag graph kernel fuse methods is 53.43, 71.62 and 61.30%, respectively. The P, R and F value of feature kernel and tag graph kernel fuse methods is 55.47, 70.29 and 60.37%, respectively. It indicated that the performance of the two kinds of kernel fusion methods is better than that of simple kernel. In comparison with the all-paths graph kernel method, the tag graph kernel method is superior in terms of overall performance. Experiments show that the performance of the multi-kernels method is better than that of the three separate single-kernel method and the dual-mutually fused kernel method used hereof in five corpus sets.
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Offerhaus, L
1989-06-01
The problems of the direct composition of a biomedical manuscript on a personal computer are discussed. Most word processing software is unsuitable because literature references, once stored, cannot be rearranged if major changes are necessary. These obstacles have been overcome in Manuscript Manager, a combination of word processing and database software. As it follows Council of Biology Editors and Vancouver rules, the printouts should be technically acceptable to most leading biomedical journals.
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Abstracting data warehousing issues in scientific research.
Tews, Cody; Bracio, Boris R
2002-01-01
This paper presents the design and implementation of the Idaho Biomedical Data Management System (IBDMS). This system preprocesses biomedical data from the IMPROVE (Improving Control of Patient Status in Critical Care) library via an Open Database Connectivity (ODBC) connection. The ODBC connection allows for local and remote simulations to access filtered, joined, and sorted data using the Structured Query Language (SQL). The tool is capable of providing an overview of available data in addition to user defined data subset for verification of models of the human respiratory system.
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Pressing needs of biomedical text mining in biocuration and beyond: opportunities and challenges
Singhal, Ayush; Leaman, Robert; Catlett, Natalie; Lemberger, Thomas; McEntyre, Johanna; Polson, Shawn; Xenarios, Ioannis; Arighi, Cecilia; Lu, Zhiyong
2016-01-01
Text mining in the biomedical sciences is rapidly transitioning from small-scale evaluation to large-scale application. In this article, we argue that text-mining technologies have become essential tools in real-world biomedical research. We describe four large scale applications of text mining, as showcased during a recent panel discussion at the BioCreative V Challenge Workshop. We draw on these applications as case studies to characterize common requirements for successfully applying text-mining techniques to practical biocuration needs. We note that system ‘accuracy’ remains a challenge and identify several additional common difficulties and potential research directions including (i) the ‘scalability’ issue due to the increasing need of mining information from millions of full-text articles, (ii) the ‘interoperability’ issue of integrating various text-mining systems into existing curation workflows and (iii) the ‘reusability’ issue on the difficulty of applying trained systems to text genres that are not seen previously during development. We then describe related efforts within the text-mining community, with a special focus on the BioCreative series of challenge workshops. We believe that focusing on the near-term challenges identified in this work will amplify the opportunities afforded by the continued adoption of text-mining tools. Finally, in order to sustain the curation ecosystem and have text-mining systems adopted for practical benefits, we call for increased collaboration between text-mining researchers and various stakeholders, including researchers, publishers and biocurators. PMID:28025348
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Pressing needs of biomedical text mining in biocuration and beyond: opportunities and challenges
Singhal, Ayush; Leaman, Robert; Catlett, Natalie; ...
2016-12-26
Text mining in the biomedical sciences is rapidly transitioning from small-scale evaluation to large-scale application. In this article, we argue that text-mining technologies have become essential tools in real-world biomedical research. We describe four large scale applications of text mining, as showcased during a recent panel discussion at the BioCreative V Challenge Workshop. We draw on these applications as case studies to characterize common requirements for successfully applying text-mining techniques to practical biocuration needs. We note that system ‘accuracy’ remains a challenge and identify several additional common difficulties and potential research directions including (i) the ‘scalability’ issue due to themore » increasing need of mining information from millions of full-text articles, (ii) the ‘interoperability’ issue of integrating various text-mining systems into existing curation workflows and (iii) the ‘reusability’ issue on the difficulty of applying trained systems to text genres that are not seen previously during development. We then describe related efforts within the text-mining community, with a special focus on the BioCreative series of challenge workshops. We believe that focusing on the near-term challenges identified in this work will amplify the opportunities afforded by the continued adoption of text-mining tools. In conclusion, in order to sustain the curation ecosystem and have text-mining systems adopted for practical benefits, we call for increased collaboration between text-mining researchers and various stakeholders, including researchers, publishers and biocurators.« less
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Pressing needs of biomedical text mining in biocuration and beyond: opportunities and challenges
DOE Office of Scientific and Technical Information (OSTI.GOV)
Singhal, Ayush; Leaman, Robert; Catlett, Natalie
Text mining in the biomedical sciences is rapidly transitioning from small-scale evaluation to large-scale application. In this article, we argue that text-mining technologies have become essential tools in real-world biomedical research. We describe four large scale applications of text mining, as showcased during a recent panel discussion at the BioCreative V Challenge Workshop. We draw on these applications as case studies to characterize common requirements for successfully applying text-mining techniques to practical biocuration needs. We note that system ‘accuracy’ remains a challenge and identify several additional common difficulties and potential research directions including (i) the ‘scalability’ issue due to themore » increasing need of mining information from millions of full-text articles, (ii) the ‘interoperability’ issue of integrating various text-mining systems into existing curation workflows and (iii) the ‘reusability’ issue on the difficulty of applying trained systems to text genres that are not seen previously during development. We then describe related efforts within the text-mining community, with a special focus on the BioCreative series of challenge workshops. We believe that focusing on the near-term challenges identified in this work will amplify the opportunities afforded by the continued adoption of text-mining tools. In conclusion, in order to sustain the curation ecosystem and have text-mining systems adopted for practical benefits, we call for increased collaboration between text-mining researchers and various stakeholders, including researchers, publishers and biocurators.« less
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Gimli: open source and high-performance biomedical name recognition
2013-01-01
Background Automatic recognition of biomedical names is an essential task in biomedical information extraction, presenting several complex and unsolved challenges. In recent years, various solutions have been implemented to tackle this problem. However, limitations regarding system characteristics, customization and usability still hinder their wider application outside text mining research. Results We present Gimli, an open-source, state-of-the-art tool for automatic recognition of biomedical names. Gimli includes an extended set of implemented and user-selectable features, such as orthographic, morphological, linguistic-based, conjunctions and dictionary-based. A simple and fast method to combine different trained models is also provided. Gimli achieves an F-measure of 87.17% on GENETAG and 72.23% on JNLPBA corpus, significantly outperforming existing open-source solutions. Conclusions Gimli is an off-the-shelf, ready to use tool for named-entity recognition, providing trained and optimized models for recognition of biomedical entities from scientific text. It can be used as a command line tool, offering full functionality, including training of new models and customization of the feature set and model parameters through a configuration file. Advanced users can integrate Gimli in their text mining workflows through the provided library, and extend or adapt its functionalities. Based on the underlying system characteristics and functionality, both for final users and developers, and on the reported performance results, we believe that Gimli is a state-of-the-art solution for biomedical NER, contributing to faster and better research in the field. Gimli is freely available at http://bioinformatics.ua.pt/gimli. PMID:23413997
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BioC: a minimalist approach to interoperability for biomedical text processing
Comeau, Donald C.; Islamaj Doğan, Rezarta; Ciccarese, Paolo; Cohen, Kevin Bretonnel; Krallinger, Martin; Leitner, Florian; Lu, Zhiyong; Peng, Yifan; Rinaldi, Fabio; Torii, Manabu; Valencia, Alfonso; Verspoor, Karin; Wiegers, Thomas C.; Wu, Cathy H.; Wilbur, W. John
2013-01-01
A vast amount of scientific information is encoded in natural language text, and the quantity of such text has become so great that it is no longer economically feasible to have a human as the first step in the search process. Natural language processing and text mining tools have become essential to facilitate the search for and extraction of information from text. This has led to vigorous research efforts to create useful tools and to create humanly labeled text corpora, which can be used to improve such tools. To encourage combining these efforts into larger, more powerful and more capable systems, a common interchange format to represent, store and exchange the data in a simple manner between different language processing systems and text mining tools is highly desirable. Here we propose a simple extensible mark-up language format to share text documents and annotations. The proposed annotation approach allows a large number of different annotations to be represented including sentences, tokens, parts of speech, named entities such as genes or diseases and relationships between named entities. In addition, we provide simple code to hold this data, read it from and write it back to extensible mark-up language files and perform some sample processing. We also describe completed as well as ongoing work to apply the approach in several directions. Code and data are available at http://bioc.sourceforge.net/. Database URL: http://bioc.sourceforge.net/ PMID:24048470
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Perera, Gayan; Broadbent, Matthew; Callard, Felicity; Chang, Chin-Kuo; Downs, Johnny; Dutta, Rina; Fernandes, Andrea; Hayes, Richard D; Henderson, Max; Jackson, Richard; Jewell, Amelia; Kadra, Giouliana; Little, Ryan; Pritchard, Megan; Shetty, Hitesh; Tulloch, Alex; Stewart, Robert
2016-03-01
The South London and Maudsley National Health Service (NHS) Foundation Trust Biomedical Research Centre (SLaM BRC) Case Register and its Clinical Record Interactive Search (CRIS) application were developed in 2008, generating a research repository of real-time, anonymised, structured and open-text data derived from the electronic health record system used by SLaM, a large mental healthcare provider in southeast London. In this paper, we update this register's descriptive data, and describe the substantial expansion and extension of the data resource since its original development. Descriptive data were generated from the SLaM BRC Case Register on 31 December 2014. Currently, there are over 250,000 patient records accessed through CRIS. Since 2008, the most significant developments in the SLaM BRC Case Register have been the introduction of natural language processing to extract structured data from open-text fields, linkages to external sources of data, and the addition of a parallel relational database (Structured Query Language) output. Natural language processing applications to date have brought in new and hitherto inaccessible data on cognitive function, education, social care receipt, smoking, diagnostic statements and pharmacotherapy. In addition, through external data linkages, large volumes of supplementary information have been accessed on mortality, hospital attendances and cancer registrations. Coupled with robust data security and governance structures, electronic health records provide potentially transformative information on mental disorders and outcomes in routine clinical care. The SLaM BRC Case Register continues to grow as a database, with approximately 20,000 new cases added each year, in addition to extension of follow-up for existing cases. Data linkages and natural language processing present important opportunities to enhance this type of research resource further, achieving both volume and depth of data. However, research projects still need to be carefully tailored, so that they take into account the nature and quality of the source information. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/
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Mapping the literature of nursing: 1996–2000
Allen, Margaret (Peg); Jacobs, Susan Kaplan; Levy, June R.
2006-01-01
Introduction: This project is a collaborative effort of the Task Force on Mapping the Nursing Literature of the Nursing and Allied Health Resources Section of the Medical Library Association. This overview summarizes eighteen studies covering general nursing and sixteen specialties. Method: Following a common protocol, citations from source journals were analyzed for a three-year period within the years 1996 to 2000. Analysis included cited formats, age, and ranking of the frequency of cited journal titles. Highly cited journals were analyzed for coverage in twelve health sciences and academic databases. Results: Journals were the most frequently cited format, followed by books. More than 60% of the cited resources were published in the previous seven years. Bradford's law was validated, with a small core of cited journals accounting for a third of the citations. Medical and science databases provided the most comprehensive access for biomedical titles, while CINAHL and PubMed provided the best access for nursing journals. Discussion: Beyond a heavily cited core, nursing journal citations are widely dispersed among a variety of sources and disciplines, with corresponding access via a variety of bibliographic tools. Results underscore the interdisciplinary nature of the nursing profession. Conclusion: For comprehensive searches, nurses need to search multiple databases. Libraries need to provide access to databases beyond PubMed, including CINAHL and academic databases. Database vendors should improve their coverage of nursing, biomedical, and psychosocial titles identified in these studies. Additional research is needed to update these studies and analyze nursing specialties not covered. PMID:16636714
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Mapping the literature of nursing: 1996-2000.
Allen, Margaret Peg; Jacobs, Susan Kaplan; Levy, June R
2006-04-01
This project is a collaborative effort of the Task Force on Mapping the Nursing Literature of the Nursing and Allied Health Resources Section of the Medical Library Association. This overview summarizes eighteen studies covering general nursing and sixteen specialties. Following a common protocol, citations from source journals were analyzed for a three-year period within the years 1996 to 2000. Analysis included cited formats, age, and ranking of the frequency of cited journal titles. Highly cited journals were analyzed for coverage in twelve health sciences and academic databases. Journals were the most frequently cited format, followed by books. More than 60% of the cited resources were published in the previous seven years. Bradford's law was validated, with a small core of cited journals accounting for a third of the citations. Medical and science databases provided the most comprehensive access for biomedical titles, while CINAHL and PubMed provided the best access for nursing journals. Beyond a heavily cited core, nursing journal citations are widely dispersed among a variety of sources and disciplines, with corresponding access via a variety of bibliographic tools. Results underscore the interdisciplinary nature of the nursing profession. For comprehensive searches, nurses need to search multiple databases. Libraries need to provide access to databases beyond PubMed, including CINAHL and academic databases. Database vendors should improve their coverage of nursing, biomedical, and psychosocial titles identified in these studies. Additional research is needed to update these studies and analyze nursing specialties not covered.
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Categorizing biomedicine images using novel image features and sparse coding representation
2013-01-01
Background Images embedded in biomedical publications carry rich information that often concisely summarize key hypotheses adopted, methods employed, or results obtained in a published study. Therefore, they offer valuable clues for understanding main content in a biomedical publication. Prior studies have pointed out the potential of mining images embedded in biomedical publications for automatically understanding and retrieving such images' associated source documents. Within the broad area of biomedical image processing, categorizing biomedical images is a fundamental step for building many advanced image analysis, retrieval, and mining applications. Similar to any automatic categorization effort, discriminative image features can provide the most crucial aid in the process. Method We observe that many images embedded in biomedical publications carry versatile annotation text. Based on the locations of and the spatial relationships between these text elements in an image, we thus propose some novel image features for image categorization purpose, which quantitatively characterize the spatial positions and distributions of text elements inside a biomedical image. We further adopt a sparse coding representation (SCR) based technique to categorize images embedded in biomedical publications by leveraging our newly proposed image features. Results we randomly selected 990 images of the JPG format for use in our experiments where 310 images were used as training samples and the rest were used as the testing cases. We first segmented 310 sample images following the our proposed procedure. This step produced a total of 1035 sub-images. We then manually labeled all these sub-images according to the two-level hierarchical image taxonomy proposed by [1]. Among our annotation results, 316 are microscopy images, 126 are gel electrophoresis images, 135 are line charts, 156 are bar charts, 52 are spot charts, 25 are tables, 70 are flow charts, and the remaining 155 images are of the type "others". A serial of experimental results are obtained. Firstly, each image categorizing results is presented, and next image categorizing performance indexes such as precision, recall, F-score, are all listed. Different features which include conventional image features and our proposed novel features indicate different categorizing performance, and the results are demonstrated. Thirdly, we conduct an accuracy comparison between support vector machine classification method and our proposed sparse representation classification method. At last, our proposed approach is compared with three peer classification method and experimental results verify our impressively improved performance. Conclusions Compared with conventional image features that do not exploit characteristics regarding text positions and distributions inside images embedded in biomedical publications, our proposed image features coupled with the SR based representation model exhibit superior performance for classifying biomedical images as demonstrated in our comparative benchmark study. PMID:24565470
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Natural Language Processing in aid of FlyBase curators
Karamanis, Nikiforos; Seal, Ruth; Lewin, Ian; McQuilton, Peter; Vlachos, Andreas; Gasperin, Caroline; Drysdale, Rachel; Briscoe, Ted
2008-01-01
Background Despite increasing interest in applying Natural Language Processing (NLP) to biomedical text, whether this technology can facilitate tasks such as database curation remains unclear. Results PaperBrowser is the first NLP-powered interface that was developed under a user-centered approach to improve the way in which FlyBase curators navigate an article. In this paper, we first discuss how observing curators at work informed the design and evaluation of PaperBrowser. Then, we present how we appraise PaperBrowser's navigational functionalities in a user-based study using a text highlighting task and evaluation criteria of Human-Computer Interaction. Our results show that PaperBrowser reduces the amount of interactions between two highlighting events and therefore improves navigational efficiency by about 58% compared to the navigational mechanism that was previously available to the curators. Moreover, PaperBrowser is shown to provide curators with enhanced navigational utility by over 74% irrespective of the different ways in which they highlight text in the article. Conclusion We show that state-of-the-art performance in certain NLP tasks such as Named Entity Recognition and Anaphora Resolution can be combined with the navigational functionalities of PaperBrowser to support curation quite successfully. PMID:18410678
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The European Bioinformatics Institute's data resources: towards systems biology.
Brooksbank, Catherine; Cameron, Graham; Thornton, Janet
2005-01-01
Genomic and post-genomic biological research has provided fine-grain insights into the molecular processes of life, but also threatens to drown biomedical researchers in data. Moreover, as new high-throughput technologies are developed, the types of data that are gathered en masse are diversifying. The need to collect, store and curate all this information in ways that allow its efficient retrieval and exploitation is greater than ever. The European Bioinformatics Institute's (EBI's) databases and tools have evolved to meet the changing needs of molecular biologists: since we last wrote about our services in the 2003 issue of Nucleic Acids Research, we have launched new databases covering protein-protein interactions (IntAct), pathways (Reactome) and small molecules (ChEBI). Our existing core databases have continued to evolve to meet the changing needs of biomedical researchers, and we have developed new data-access tools that help biologists to move intuitively through the different data types, thereby helping them to put the parts together to understand biology at the systems level. The EBI's data resources are all available on our website at http://www.ebi.ac.uk.
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The European Bioinformatics Institute's data resources: towards systems biology
Brooksbank, Catherine; Cameron, Graham; Thornton, Janet
2005-01-01
Genomic and post-genomic biological research has provided fine-grain insights into the molecular processes of life, but also threatens to drown biomedical researchers in data. Moreover, as new high-throughput technologies are developed, the types of data that are gathered en masse are diversifying. The need to collect, store and curate all this information in ways that allow its efficient retrieval and exploitation is greater than ever. The European Bioinformatics Institute's (EBI's) databases and tools have evolved to meet the changing needs of molecular biologists: since we last wrote about our services in the 2003 issue of Nucleic Acids Research, we have launched new databases covering protein–protein interactions (IntAct), pathways (Reactome) and small molecules (ChEBI). Our existing core databases have continued to evolve to meet the changing needs of biomedical researchers, and we have developed new data-access tools that help biologists to move intuitively through the different data types, thereby helping them to put the parts together to understand biology at the systems level. The EBI's data resources are all available on our website at http://www.ebi.ac.uk. PMID:15608238
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[Design and establishment of modern literature database about acupuncture Deqi].
Guo, Zheng-rong; Qian, Gui-feng; Pan, Qiu-yin; Wang, Yang; Xin, Si-yuan; Li, Jing; Hao, Jie; Hu, Ni-juan; Zhu, Jiang; Ma, Liang-xiao
2015-02-01
A search on acupuncture Deqi was conducted using four Chinese-language biomedical databases (CNKI, Wan-Fang, VIP and CBM) and PubMed database and using keywords "Deqi" or "needle sensation" "needling feeling" "needle feel" "obtaining qi", etc. Then, a "Modern Literature Database for Acupuncture Deqi" was established by employing Microsoft SQL Server 2005 Express Edition, introducing the contents, data types, information structure and logic constraint of the system table fields. From this Database, detailed inquiries about general information of clinical trials, acupuncturists' experience, ancient medical works, comprehensive literature, etc. can be obtained. The present databank lays a foundation for subsequent evaluation of literature quality about Deqi and data mining of undetected Deqi knowledge.
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OrganismTagger: detection, normalization and grounding of organism entities in biomedical documents.
Naderi, Nona; Kappler, Thomas; Baker, Christopher J O; Witte, René
2011-10-01
Semantic tagging of organism mentions in full-text articles is an important part of literature mining and semantic enrichment solutions. Tagged organism mentions also play a pivotal role in disambiguating other entities in a text, such as proteins. A high-precision organism tagging system must be able to detect the numerous forms of organism mentions, including common names as well as the traditional taxonomic groups: genus, species and strains. In addition, such a system must resolve abbreviations and acronyms, assign the scientific name and if possible link the detected mention to the NCBI Taxonomy database for further semantic queries and literature navigation. We present the OrganismTagger, a hybrid rule-based/machine learning system to extract organism mentions from the literature. It includes tools for automatically generating lexical and ontological resources from a copy of the NCBI Taxonomy database, thereby facilitating system updates by end users. Its novel ontology-based resources can also be reused in other semantic mining and linked data tasks. Each detected organism mention is normalized to a canonical name through the resolution of acronyms and abbreviations and subsequently grounded with an NCBI Taxonomy database ID. In particular, our system combines a novel machine-learning approach with rule-based and lexical methods for detecting strain mentions in documents. On our manually annotated OT corpus, the OrganismTagger achieves a precision of 95%, a recall of 94% and a grounding accuracy of 97.5%. On the manually annotated corpus of Linnaeus-100, the results show a precision of 99%, recall of 97% and grounding accuracy of 97.4%. The OrganismTagger, including supporting tools, resources, training data and manual annotations, as well as end user and developer documentation, is freely available under an open-source license at http://www.semanticsoftware.info/organism-tagger. witte@semanticsoftware.info.
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ERIC Educational Resources Information Center
Biomedical Interdisciplinary Curriculum Project, Berkeley, CA.
This student text presents instructional materials for a unit of science within the Biomedical Interdisciplinary Curriculum Project (BICP), a two-year interdisciplinary precollege curriculum aimed at preparing high school students for entry into college and vocational programs leading to a career in the health field. Lessons concentrate on…
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Literature searches on Ayurveda: An update.
Aggithaya, Madhur G; Narahari, Saravu R
2015-01-01
The journals that publish on Ayurveda are increasingly indexed by popular medical databases in recent years. However, many Eastern journals are not indexed biomedical journal databases such as PubMed. Literature searches for Ayurveda continue to be challenging due to the nonavailability of active, unbiased dedicated databases for Ayurvedic literature. In 2010, authors identified 46 databases that can be used for systematic search of Ayurvedic papers and theses. This update reviewed our previous recommendation and identified current and relevant databases. To update on Ayurveda literature search and strategy to retrieve maximum publications. Author used psoriasis as an example to search previously listed databases and identify new. The population, intervention, control, and outcome table included keywords related to psoriasis and Ayurvedic terminologies for skin diseases. Current citation update status, search results, and search options of previous databases were assessed. Eight search strategies were developed. Hundred and five journals, both biomedical and Ayurveda, which publish on Ayurveda, were identified. Variability in databases was explored to identify bias in journal citation. Five among 46 databases are now relevant - AYUSH research portal, Annotated Bibliography of Indian Medicine, Digital Helpline for Ayurveda Research Articles (DHARA), PubMed, and Directory of Open Access Journals. Search options in these databases are not uniform, and only PubMed allows complex search strategy. "The Researches in Ayurveda" and "Ayurvedic Research Database" (ARD) are important grey resources for hand searching. About 44/105 (41.5%) journals publishing Ayurvedic studies are not indexed in any database. Only 11/105 (10.4%) exclusive Ayurveda journals are indexed in PubMed. AYUSH research portal and DHARA are two major portals after 2010. It is mandatory to search PubMed and four other databases because all five carry citations from different groups of journals. The hand searching is important to identify Ayurveda publications that are not indexed elsewhere. Availability information of citations in Ayurveda libraries from National Union Catalogue of Scientific Serials in India if regularly updated will improve the efficacy of hand searching. A grey database (ARD) contains unpublished PG/Ph.D. theses. The AYUSH portal, DHARA (funded by Ministry of AYUSH), and ARD should be merged to form single larger database to limit Ayurveda literature searches.
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Google Scholar versus PubMed in locating primary literature to answer drug-related questions.
Freeman, Maisha Kelly; Lauderdale, Stacy A; Kendrach, Michael G; Woolley, Thomas W
2009-03-01
Google Scholar linked more visitors to biomedical journal Web sites than did PubMed after the database's initial release; however, its usefulness in locating primary literature articles is unknown. To assess in both databases the availability of primary literature target articles; total number of citations; availability of free, full-text journal articles; and number of primary literature target articles retrieved by year within the first 100 citations of the search results. Drug information question reviews published in The Annals of Pharmacotherapy Drug Information Rounds column served as targets to determine the retrieval ability of Google Scholar and PubMed searches. Reviews printed in this column from January 2006 to June 2007 were eligible for study inclusion. Articles were chosen if at least 2 key words of the printed article were included in the PubMed Medical Subject Heading (MeSH) database, and these terms were searched in both databases. Twenty-two of 33 (67%) eligible Drug Information Rounds articles met the inclusion criteria. The median number of primary literature articles used in each of these articles was 6.5 (IQR 4.8, 8.3; mean +/- SD 8 +/- 5.4). No significant differences were found for the mean number of target primary literature articles located within the first 100 citations in Google Scholar and PubMed searches (5.1 +/- 3.9 vs 5.3 +/- 3.3; p = 0.868). Google Scholar searches located more total results than PubMed (2211.6 +/- 3999.5 vs 44.2 +/- 47.4; p = 0.019). The availability of free, full-text journal articles per Drug Information Rounds article was similar between the databases (1.8 +/- 1.7 vs 2.3 +/- 1.7; p = 0.325). More primary literature articles published prior to 2000 were located with Google Scholar searches compared with PubMed (62.8% vs 34.9%; p = 0.017); however, no statistically significant differences between the databases were observed for articles published after 2000 (66.4 vs 77.1; p = 0.074). No significant differences were identified in the number of target primary literature articles located between databases. PubMed searches yielded fewer total citations than Google Scholar results; however, PubMed appears to be more specific than Google Scholar for locating relevant primary literature articles.
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Cai, Liyan; Wu, Jie; Ma, Tingting; Yang, Lijie
2015-10-01
The acupoint selection was retrieved from the ancient and modern literature on the treatment of sub-healthy condition with acupuncture. The law of acupoint application was analyzed so as to provide a certain reference to the determination of acupoint prescription in clinical acupuncture. The ancient literature was retrieved from Chinese basic ancient literature database. The modern literature was retrieved from Cochrane Library, Medline, PubMed, Ovid evidence-based medicine database, Chinese biomedical literature database, China journal full-text database, VIP journal full-text database and Wanfang database. The database mining software was adopted to explore the law of acupoint application in treatment of sub-healthy conditions with ancient and modern acupuncture. The acupoint use frequency, compatibility association rule, law for meridian use and the use regularity of specific points were analyzed. In the ancient treatment for sub-healthy condition, the top five commonly used acupoints are Shenmen (HT 7), Zhaohai (KI 6), Taibai (SP 3), Daling (PC 7) and Taixi (KI 3). The most commonly combined points are Zhangmen (LR 13), Taibai (SP 3) and Zhaohai (KI 6). The most commonly used meridians are the bladder meridian of foot-taiyang, kidney meridian of foot-shaoyin and liver meridian of foot-jueyin. The most commonly used specific points are the five-shu points. The most commonly used acupoints are located in the lower limbs. In the modern treatment, the top five commonly used acupoints are Zusanli (ST 36), Sanyinjiao (SP 6), Baihui (GV 20), Shenshu (BL 23) and Guanyuan (CV 4). The most commonly supplemented points are Hegu (LI 4) and Taichong (LR 3). The most commonly used meridians are the bladder meridian of foot-taiyang, the conception vessel and the governor vessel. The most commonly used specific points are the back-shu points. The most commonly used acupoints are located in the lower limbs. After the systematic comprehension of the relevant ancient and modern literature, the most commonly used acupoints are selected along the bladder meridian of foot-taiyang, and the most commonly used specific points are the back-shu points, the five-shu points and the front-mu-points. the acupoints are mostly located in the lower limbs.
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BioLemmatizer: a lemmatization tool for morphological processing of biomedical text
2012-01-01
Background The wide variety of morphological variants of domain-specific technical terms contributes to the complexity of performing natural language processing of the scientific literature related to molecular biology. For morphological analysis of these texts, lemmatization has been actively applied in the recent biomedical research. Results In this work, we developed a domain-specific lemmatization tool, BioLemmatizer, for the morphological analysis of biomedical literature. The tool focuses on the inflectional morphology of English and is based on the general English lemmatization tool MorphAdorner. The BioLemmatizer is further tailored to the biological domain through incorporation of several published lexical resources. It retrieves lemmas based on the use of a word lexicon, and defines a set of rules that transform a word to a lemma if it is not encountered in the lexicon. An innovative aspect of the BioLemmatizer is the use of a hierarchical strategy for searching the lexicon, which enables the discovery of the correct lemma even if the input Part-of-Speech information is inaccurate. The BioLemmatizer achieves an accuracy of 97.5% in lemmatizing an evaluation set prepared from the CRAFT corpus, a collection of full-text biomedical articles, and an accuracy of 97.6% on the LLL05 corpus. The contribution of the BioLemmatizer to accuracy improvement of a practical information extraction task is further demonstrated when it is used as a component in a biomedical text mining system. Conclusions The BioLemmatizer outperforms other tools when compared with eight existing lemmatizers. The BioLemmatizer is released as an open source software and can be downloaded from http://biolemmatizer.sourceforge.net. PMID:22464129
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tmBioC: improving interoperability of text-mining tools with BioC.
Khare, Ritu; Wei, Chih-Hsuan; Mao, Yuqing; Leaman, Robert; Lu, Zhiyong
2014-01-01
The lack of interoperability among biomedical text-mining tools is a major bottleneck in creating more complex applications. Despite the availability of numerous methods and techniques for various text-mining tasks, combining different tools requires substantial efforts and time owing to heterogeneity and variety in data formats. In response, BioC is a recent proposal that offers a minimalistic approach to tool interoperability by stipulating minimal changes to existing tools and applications. BioC is a family of XML formats that define how to present text documents and annotations, and also provides easy-to-use functions to read/write documents in the BioC format. In this study, we introduce our text-mining toolkit, which is designed to perform several challenging and significant tasks in the biomedical domain, and repackage the toolkit into BioC to enhance its interoperability. Our toolkit consists of six state-of-the-art tools for named-entity recognition, normalization and annotation (PubTator) of genes (GenNorm), diseases (DNorm), mutations (tmVar), species (SR4GN) and chemicals (tmChem). Although developed within the same group, each tool is designed to process input articles and output annotations in a different format. We modify these tools and enable them to read/write data in the proposed BioC format. We find that, using the BioC family of formats and functions, only minimal changes were required to build the newer versions of the tools. The resulting BioC wrapped toolkit, which we have named tmBioC, consists of our tools in BioC, an annotated full-text corpus in BioC, and a format detection and conversion tool. Furthermore, through participation in the 2013 BioCreative IV Interoperability Track, we empirically demonstrate that the tools in tmBioC can be more efficiently integrated with each other as well as with external tools: Our experimental results show that using BioC reduces >60% in lines of code for text-mining tool integration. The tmBioC toolkit is publicly available at http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/. Database URL: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/. Published by Oxford University Press 2014. This work is written by US Government employees and is in the public domain in the US.
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ERIC Educational Resources Information Center
Santana Arroyo, Sonia; del Carmen Gonzalez Rivero, Maria
2012-01-01
The National Medical Library of Cuba is currently developing an information literacy program to train users in the use of biomedical databases. This paper describes the experience with the course "Cochrane Library: Evidence-Based Medicine," which aims to teach users how to make the best use of this database, as well as the evidence-based…
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Mapping selected general literature of international nursing.
Shams, Marie-Lise Antoun; Dixon, Lana S
2007-01-01
This study, part of a wider project to map the literature of nursing, identifies core journals cited in non-US nursing journals and determines the extent of their coverage by indexing services. Four general English-language journals were analyzed for format types and publication dates. Core titles were identified and nine bibliographic databases were scanned for indexing coverage. Findings show that 57.5% (13,391/23,271) of the cited references from the 4 core journals were to journal articles, 27.8% (6,471/23,271) to books, 9.5% (2,208/23,271) to government documents, 4.9% (1,131/23,271) to miscellaneous sources, and less than 1% (70/23,271) to Internet resources. Eleven journals produced one-third of the citations; the next third included 146 journals, followed by a dispersion of 1,622 titles. PubMed received the best database coverage scores, followed by CINAHL and Science Citation Index. None of the databases provided complete coverage of all 11 core titles. The four source journals contain a diverse group of cited references. The currency of citations to government documents makes these journals a good source for regulatory and legislative awareness. Nurses consult nursing and biomedical journals and must search both nursing and biomedical databases to cover the literature.
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Pressing needs of biomedical text mining in biocuration and beyond: opportunities and challenges.
Singhal, Ayush; Leaman, Robert; Catlett, Natalie; Lemberger, Thomas; McEntyre, Johanna; Polson, Shawn; Xenarios, Ioannis; Arighi, Cecilia; Lu, Zhiyong
2016-01-01
Text mining in the biomedical sciences is rapidly transitioning from small-scale evaluation to large-scale application. In this article, we argue that text-mining technologies have become essential tools in real-world biomedical research. We describe four large scale applications of text mining, as showcased during a recent panel discussion at the BioCreative V Challenge Workshop. We draw on these applications as case studies to characterize common requirements for successfully applying text-mining techniques to practical biocuration needs. We note that system 'accuracy' remains a challenge and identify several additional common difficulties and potential research directions including (i) the 'scalability' issue due to the increasing need of mining information from millions of full-text articles, (ii) the 'interoperability' issue of integrating various text-mining systems into existing curation workflows and (iii) the 'reusability' issue on the difficulty of applying trained systems to text genres that are not seen previously during development. We then describe related efforts within the text-mining community, with a special focus on the BioCreative series of challenge workshops. We believe that focusing on the near-term challenges identified in this work will amplify the opportunities afforded by the continued adoption of text-mining tools. Finally, in order to sustain the curation ecosystem and have text-mining systems adopted for practical benefits, we call for increased collaboration between text-mining researchers and various stakeholders, including researchers, publishers and biocurators. Published by Oxford University Press 2016. This work is written by US Government employees and is in the public domain in the US.
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Enhanced functionalities for annotating and indexing clinical text with the NCBO Annotator.
Tchechmedjiev, Andon; Abdaoui, Amine; Emonet, Vincent; Melzi, Soumia; Jonnagaddala, Jitendra; Jonquet, Clement
2018-06-01
Second use of clinical data commonly involves annotating biomedical text with terminologies and ontologies. The National Center for Biomedical Ontology Annotator is a frequently used annotation service, originally designed for biomedical data, but not very suitable for clinical text annotation. In order to add new functionalities to the NCBO Annotator without hosting or modifying the original Web service, we have designed a proxy architecture that enables seamless extensions by pre-processing of the input text and parameters, and post processing of the annotations. We have then implemented enhanced functionalities for annotating and indexing free text such as: scoring, detection of context (negation, experiencer, temporality), new output formats and coarse-grained concept recognition (with UMLS Semantic Groups). In this paper, we present the NCBO Annotator+, a Web service which incorporates these new functionalities as well as a small set of evaluation results for concept recognition and clinical context detection on two standard evaluation tasks (Clef eHealth 2017, SemEval 2014). The Annotator+ has been successfully integrated into the SIFR BioPortal platform-an implementation of NCBO BioPortal for French biomedical terminologies and ontologies-to annotate English text. A Web user interface is available for testing and ontology selection (http://bioportal.lirmm.fr/ncbo_annotatorplus); however the Annotator+ is meant to be used through the Web service application programming interface (http://services.bioportal.lirmm.fr/ncbo_annotatorplus). The code is openly available, and we also provide a Docker packaging to enable easy local deployment to process sensitive (e.g. clinical) data in-house (https://github.com/sifrproject). andon.tchechmedjiev@lirmm.fr. Supplementary data are available at Bioinformatics online.
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NASA Astrophysics Data System (ADS)
You, Daekeun; Simpson, Matthew; Antani, Sameer; Demner-Fushman, Dina; Thoma, George R.
2013-01-01
Pointers (arrows and symbols) are frequently used in biomedical images to highlight specific image regions of interest (ROIs) that are mentioned in figure captions and/or text discussion. Detection of pointers is the first step toward extracting relevant visual features from ROIs and combining them with textual descriptions for a multimodal (text and image) biomedical article retrieval system. Recently we developed a pointer recognition algorithm based on an edge-based pointer segmentation method, and subsequently reported improvements made on our initial approach involving the use of Active Shape Models (ASM) for pointer recognition and region growing-based method for pointer segmentation. These methods contributed to improving the recall of pointer recognition but not much to the precision. The method discussed in this article is our recent effort to improve the precision rate. Evaluation performed on two datasets and compared with other pointer segmentation methods show significantly improved precision and the highest F1 score.
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Automated labeling of bibliographic data extracted from biomedical online journals
NASA Astrophysics Data System (ADS)
Kim, Jongwoo; Le, Daniel X.; Thoma, George R.
2003-01-01
A prototype system has been designed to automate the extraction of bibliographic data (e.g., article title, authors, abstract, affiliation and others) from online biomedical journals to populate the National Library of Medicine"s MEDLINE database. This paper describes a key module in this system: the labeling module that employs statistics and fuzzy rule-based algorithms to identify segmented zones in an article"s HTML pages as specific bibliographic data. Results from experiments conducted with 1,149 medical articles from forty-seven journal issues are presented.
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Image BOSS: a biomedical object storage system
NASA Astrophysics Data System (ADS)
Stacy, Mahlon C.; Augustine, Kurt E.; Robb, Richard A.
1997-05-01
Researchers using biomedical images have data management needs which are oriented perpendicular to clinical PACS. The image BOSS system is designed to permit researchers to organize and select images based on research topic, image metadata, and a thumbnail of the image. Image information is captured from existing images in a Unix based filesystem, stored in an object oriented database, and presented to the user in a familiar laboratory notebook metaphor. In addition, the ImageBOSS is designed to provide an extensible infrastructure for future content-based queries directly on the images.
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pubmed.mineR: an R package with text-mining algorithms to analyse PubMed abstracts.
Rani, Jyoti; Shah, A B Rauf; Ramachandran, Srinivasan
2015-10-01
The PubMed literature database is a valuable source of information for scientific research. It is rich in biomedical literature with more than 24 million citations. Data-mining of voluminous literature is a challenging task. Although several text-mining algorithms have been developed in recent years with focus on data visualization, they have limitations such as speed, are rigid and are not available in the open source. We have developed an R package, pubmed.mineR, wherein we have combined the advantages of existing algorithms, overcome their limitations, and offer user flexibility and link with other packages in Bioconductor and the Comprehensive R Network (CRAN) in order to expand the user capabilities for executing multifaceted approaches. Three case studies are presented, namely, 'Evolving role of diabetes educators', 'Cancer risk assessment' and 'Dynamic concepts on disease and comorbidity' to illustrate the use of pubmed.mineR. The package generally runs fast with small elapsed times in regular workstations even on large corpus sizes and with compute intensive functions. The pubmed.mineR is available at http://cran.rproject. org/web/packages/pubmed.mineR.
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SQLGEN: a framework for rapid client-server database application development.
Nadkarni, P M; Cheung, K H
1995-12-01
SQLGEN is a framework for rapid client-server relational database application development. It relies on an active data dictionary on the client machine that stores metadata on one or more database servers to which the client may be connected. The dictionary generates dynamic Structured Query Language (SQL) to perform common database operations; it also stores information about the access rights of the user at log-in time, which is used to partially self-configure the behavior of the client to disable inappropriate user actions. SQLGEN uses a microcomputer database as the client to store metadata in relational form, to transiently capture server data in tables, and to allow rapid application prototyping followed by porting to client-server mode with modest effort. SQLGEN is currently used in several production biomedical databases.
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BIOSSES: a semantic sentence similarity estimation system for the biomedical domain.
Sogancioglu, Gizem; Öztürk, Hakime; Özgür, Arzucan
2017-07-15
The amount of information available in textual format is rapidly increasing in the biomedical domain. Therefore, natural language processing (NLP) applications are becoming increasingly important to facilitate the retrieval and analysis of these data. Computing the semantic similarity between sentences is an important component in many NLP tasks including text retrieval and summarization. A number of approaches have been proposed for semantic sentence similarity estimation for generic English. However, our experiments showed that such approaches do not effectively cover biomedical knowledge and produce poor results for biomedical text. We propose several approaches for sentence-level semantic similarity computation in the biomedical domain, including string similarity measures and measures based on the distributed vector representations of sentences learned in an unsupervised manner from a large biomedical corpus. In addition, ontology-based approaches are presented that utilize general and domain-specific ontologies. Finally, a supervised regression based model is developed that effectively combines the different similarity computation metrics. A benchmark data set consisting of 100 sentence pairs from the biomedical literature is manually annotated by five human experts and used for evaluating the proposed methods. The experiments showed that the supervised semantic sentence similarity computation approach obtained the best performance (0.836 correlation with gold standard human annotations) and improved over the state-of-the-art domain-independent systems up to 42.6% in terms of the Pearson correlation metric. A web-based system for biomedical semantic sentence similarity computation, the source code, and the annotated benchmark data set are available at: http://tabilab.cmpe.boun.edu.tr/BIOSSES/ . gizemsogancioglu@gmail.com or arzucan.ozgur@boun.edu.tr. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com
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Recent Advances and Emerging Applications in Text and Data Mining for Biomedical Discovery.
Gonzalez, Graciela H; Tahsin, Tasnia; Goodale, Britton C; Greene, Anna C; Greene, Casey S
2016-01-01
Precision medicine will revolutionize the way we treat and prevent disease. A major barrier to the implementation of precision medicine that clinicians and translational scientists face is understanding the underlying mechanisms of disease. We are starting to address this challenge through automatic approaches for information extraction, representation and analysis. Recent advances in text and data mining have been applied to a broad spectrum of key biomedical questions in genomics, pharmacogenomics and other fields. We present an overview of the fundamental methods for text and data mining, as well as recent advances and emerging applications toward precision medicine. © The Author 2015. Published by Oxford University Press.
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Recent Advances and Emerging Applications in Text and Data Mining for Biomedical Discovery
Gonzalez, Graciela H.; Tahsin, Tasnia; Goodale, Britton C.; Greene, Anna C.
2016-01-01
Precision medicine will revolutionize the way we treat and prevent disease. A major barrier to the implementation of precision medicine that clinicians and translational scientists face is understanding the underlying mechanisms of disease. We are starting to address this challenge through automatic approaches for information extraction, representation and analysis. Recent advances in text and data mining have been applied to a broad spectrum of key biomedical questions in genomics, pharmacogenomics and other fields. We present an overview of the fundamental methods for text and data mining, as well as recent advances and emerging applications toward precision medicine. PMID:26420781
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An automated procedure to identify biomedical articles that contain cancer-associated gene variants.
McDonald, Ryan; Scott Winters, R; Ankuda, Claire K; Murphy, Joan A; Rogers, Amy E; Pereira, Fernando; Greenblatt, Marc S; White, Peter S
2006-09-01
The proliferation of biomedical literature makes it increasingly difficult for researchers to find and manage relevant information. However, identifying research articles containing mutation data, a requisite first step in integrating large and complex mutation data sets, is currently tedious, time-consuming and imprecise. More effective mechanisms for identifying articles containing mutation information would be beneficial both for the curation of mutation databases and for individual researchers. We developed an automated method that uses information extraction, classifier, and relevance ranking techniques to determine the likelihood of MEDLINE abstracts containing information regarding genomic variation data suitable for inclusion in mutation databases. We targeted the CDKN2A (p16) gene and the procedure for document identification currently used by CDKN2A Database curators as a measure of feasibility. A set of abstracts was manually identified from a MEDLINE search as potentially containing specific CDKN2A mutation events. A subset of these abstracts was used as a training set for a maximum entropy classifier to identify text features distinguishing "relevant" from "not relevant" abstracts. Each document was represented as a set of indicative word, word pair, and entity tagger-derived genomic variation features. When applied to a test set of 200 candidate abstracts, the classifier predicted 88 articles as being relevant; of these, 29 of 32 manuscripts in which manual curation found CDKN2A sequence variants were positively predicted. Thus, the set of potentially useful articles that a manual curator would have to review was reduced by 56%, maintaining 91% recall (sensitivity) and more than doubling precision (positive predictive value). Subsequent expansion of the training set to 494 articles yielded similar precision and recall rates, and comparison of the original and expanded trials demonstrated that the average precision improved with the larger data set. Our results show that automated systems can effectively identify article subsets relevant to a given task and may prove to be powerful tools for the broader research community. This procedure can be readily adapted to any or all genes, organisms, or sets of documents. Published 2006 Wiley-Liss, Inc.
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SORTA: a system for ontology-based re-coding and technical annotation of biomedical phenotype data.
Pang, Chao; Sollie, Annet; Sijtsma, Anna; Hendriksen, Dennis; Charbon, Bart; de Haan, Mark; de Boer, Tommy; Kelpin, Fleur; Jetten, Jonathan; van der Velde, Joeri K; Smidt, Nynke; Sijmons, Rolf; Hillege, Hans; Swertz, Morris A
2015-01-01
There is an urgent need to standardize the semantics of biomedical data values, such as phenotypes, to enable comparative and integrative analyses. However, it is unlikely that all studies will use the same data collection protocols. As a result, retrospective standardization is often required, which involves matching of original (unstructured or locally coded) data to widely used coding or ontology systems such as SNOMED CT (clinical terms), ICD-10 (International Classification of Disease) and HPO (Human Phenotype Ontology). This data curation process is usually a time-consuming process performed by a human expert. To help mechanize this process, we have developed SORTA, a computer-aided system for rapidly encoding free text or locally coded values to a formal coding system or ontology. SORTA matches original data values (uploaded in semicolon delimited format) to a target coding system (uploaded in Excel spreadsheet, OWL ontology web language or OBO open biomedical ontologies format). It then semi- automatically shortlists candidate codes for each data value using Lucene and n-gram based matching algorithms, and can also learn from matches chosen by human experts. We evaluated SORTA's applicability in two use cases. For the LifeLines biobank, we used SORTA to recode 90 000 free text values (including 5211 unique values) about physical exercise to MET (Metabolic Equivalent of Task) codes. For the CINEAS clinical symptom coding system, we used SORTA to map to HPO, enriching HPO when necessary (315 terms matched so far). Out of the shortlists at rank 1, we found a precision/recall of 0.97/0.98 in LifeLines and of 0.58/0.45 in CINEAS. More importantly, users found the tool both a major time saver and a quality improvement because SORTA reduced the chances of human mistakes. Thus, SORTA can dramatically ease data (re)coding tasks and we believe it will prove useful for many more projects. Database URL: http://molgenis.org/sorta or as an open source download from http://www.molgenis.org/wiki/SORTA. © The Author(s) 2015. Published by Oxford University Press.
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SORTA: a system for ontology-based re-coding and technical annotation of biomedical phenotype data
Pang, Chao; Sollie, Annet; Sijtsma, Anna; Hendriksen, Dennis; Charbon, Bart; de Haan, Mark; de Boer, Tommy; Kelpin, Fleur; Jetten, Jonathan; van der Velde, Joeri K.; Smidt, Nynke; Sijmons, Rolf; Hillege, Hans; Swertz, Morris A.
2015-01-01
There is an urgent need to standardize the semantics of biomedical data values, such as phenotypes, to enable comparative and integrative analyses. However, it is unlikely that all studies will use the same data collection protocols. As a result, retrospective standardization is often required, which involves matching of original (unstructured or locally coded) data to widely used coding or ontology systems such as SNOMED CT (clinical terms), ICD-10 (International Classification of Disease) and HPO (Human Phenotype Ontology). This data curation process is usually a time-consuming process performed by a human expert. To help mechanize this process, we have developed SORTA, a computer-aided system for rapidly encoding free text or locally coded values to a formal coding system or ontology. SORTA matches original data values (uploaded in semicolon delimited format) to a target coding system (uploaded in Excel spreadsheet, OWL ontology web language or OBO open biomedical ontologies format). It then semi- automatically shortlists candidate codes for each data value using Lucene and n-gram based matching algorithms, and can also learn from matches chosen by human experts. We evaluated SORTA’s applicability in two use cases. For the LifeLines biobank, we used SORTA to recode 90 000 free text values (including 5211 unique values) about physical exercise to MET (Metabolic Equivalent of Task) codes. For the CINEAS clinical symptom coding system, we used SORTA to map to HPO, enriching HPO when necessary (315 terms matched so far). Out of the shortlists at rank 1, we found a precision/recall of 0.97/0.98 in LifeLines and of 0.58/0.45 in CINEAS. More importantly, users found the tool both a major time saver and a quality improvement because SORTA reduced the chances of human mistakes. Thus, SORTA can dramatically ease data (re)coding tasks and we believe it will prove useful for many more projects. Database URL: http://molgenis.org/sorta or as an open source download from http://www.molgenis.org/wiki/SORTA PMID:26385205
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Knowledge acquisition, semantic text mining, and security risks in health and biomedical informatics
Huang, Jingshan; Dou, Dejing; Dang, Jiangbo; Pardue, J Harold; Qin, Xiao; Huan, Jun; Gerthoffer, William T; Tan, Ming
2012-01-01
Computational techniques have been adopted in medical and biological systems for a long time. There is no doubt that the development and application of computational methods will render great help in better understanding biomedical and biological functions. Large amounts of datasets have been produced by biomedical and biological experiments and simulations. In order for researchers to gain knowledge from original data, nontrivial transformation is necessary, which is regarded as a critical link in the chain of knowledge acquisition, sharing, and reuse. Challenges that have been encountered include: how to efficiently and effectively represent human knowledge in formal computing models, how to take advantage of semantic text mining techniques rather than traditional syntactic text mining, and how to handle security issues during the knowledge sharing and reuse. This paper summarizes the state-of-the-art in these research directions. We aim to provide readers with an introduction of major computing themes to be applied to the medical and biological research. PMID:22371823
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MedlinePlus FAQ: MedlinePlus and MEDLINE/PubMed
... What is the difference between MedlinePlus and MEDLINE/PubMed? To use the sharing features on this page, ... latest health professional articles on your topic. MEDLINE/PubMed: Is a database of professional biomedical literature Is ...
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Technological Innovations from NASA
NASA Technical Reports Server (NTRS)
Pellis, Neal R.
2006-01-01
The challenge of human space exploration places demands on technology that push concepts and development to the leading edge. In biotechnology and biomedical equipment development, NASA science has been the seed for numerous innovations, many of which are in the commercial arena. The biotechnology effort has led to rational drug design, analytical equipment, and cell culture and tissue engineering strategies. Biomedical research and development has resulted in medical devices that enable diagnosis and treatment advances. NASA Biomedical developments are exemplified in the new laser light scattering analysis for cataracts, the axial flow left ventricular-assist device, non contact electrocardiography, and the guidance system for LASIK surgery. Many more developments are in progress. NASA will continue to advance technologies, incorporating new approaches from basic and applied research, nanotechnology, computational modeling, and database analyses.
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Stephens, Susie M; Chen, Jake Y; Davidson, Marcel G; Thomas, Shiby; Trute, Barry M
2005-01-01
As database management systems expand their array of analytical functionality, they become powerful research engines for biomedical data analysis and drug discovery. Databases can hold most of the data types commonly required in life sciences and consequently can be used as flexible platforms for the implementation of knowledgebases. Performing data analysis in the database simplifies data management by minimizing the movement of data from disks to memory, allowing pre-filtering and post-processing of datasets, and enabling data to remain in a secure, highly available environment. This article describes the Oracle Database 10g implementation of BLAST and Regular Expression Searches and provides case studies of their usage in bioinformatics. http://www.oracle.com/technology/software/index.html.
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BIOSMILE web search: a web application for annotating biomedical entities and relations.
Dai, Hong-Jie; Huang, Chi-Hsin; Lin, Ryan T K; Tsai, Richard Tzong-Han; Hsu, Wen-Lian
2008-07-01
BIOSMILE web search (BWS), a web-based NCBI-PubMed search application, which can analyze articles for selected biomedical verbs and give users relational information, such as subject, object, location, manner, time, etc. After receiving keyword query input, BWS retrieves matching PubMed abstracts and lists them along with snippets by order of relevancy to protein-protein interaction. Users can then select articles for further analysis, and BWS will find and mark up biomedical relations in the text. The analysis results can be viewed in the abstract text or in table form. To date, BWS has been field tested by over 30 biologists and questionnaires have shown that subjects are highly satisfied with its capabilities and usability. BWS is accessible free of charge at http://bioservices.cse.yzu.edu.tw/BWS.
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A corpus for plant-chemical relationships in the biomedical domain.
Choi, Wonjun; Kim, Baeksoo; Cho, Hyejin; Lee, Doheon; Lee, Hyunju
2016-09-20
Plants are natural products that humans consume in various ways including food and medicine. They have a long empirical history of treating diseases with relatively few side effects. Based on these strengths, many studies have been performed to verify the effectiveness of plants in treating diseases. It is crucial to understand the chemicals contained in plants because these chemicals can regulate activities of proteins that are key factors in causing diseases. With the accumulation of a large volume of biomedical literature in various databases such as PubMed, it is possible to automatically extract relationships between plants and chemicals in a large-scale way if we apply a text mining approach. A cornerstone of achieving this task is a corpus of relationships between plants and chemicals. In this study, we first constructed a corpus for plant and chemical entities and for the relationships between them. The corpus contains 267 plant entities, 475 chemical entities, and 1,007 plant-chemical relationships (550 and 457 positive and negative relationships, respectively), which are drawn from 377 sentences in 245 PubMed abstracts. Inter-annotator agreement scores for the corpus among three annotators were measured. The simple percent agreement scores for entities and trigger words for the relationships were 99.6 and 94.8 %, respectively, and the overall kappa score for the classification of positive and negative relationships was 79.8 %. We also developed a rule-based model to automatically extract such plant-chemical relationships. When we evaluated the rule-based model using the corpus and randomly selected biomedical articles, overall F-scores of 68.0 and 61.8 % were achieved, respectively. We expect that the corpus for plant-chemical relationships will be a useful resource for enhancing plant research. The corpus is available at http://combio.gist.ac.kr/plantchemicalcorpus .
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Rinaldi, Fabio; Schneider, Gerold; Kaljurand, Kaarel; Hess, Michael; Andronis, Christos; Konstandi, Ourania; Persidis, Andreas
2007-02-01
The amount of new discoveries (as published in the scientific literature) in the biomedical area is growing at an exponential rate. This growth makes it very difficult to filter the most relevant results, and thus the extraction of the core information becomes very expensive. Therefore, there is a growing interest in text processing approaches that can deliver selected information from scientific publications, which can limit the amount of human intervention normally needed to gather those results. This paper presents and evaluates an approach aimed at automating the process of extracting functional relations (e.g. interactions between genes and proteins) from scientific literature in the biomedical domain. The approach, using a novel dependency-based parser, is based on a complete syntactic analysis of the corpus. We have implemented a state-of-the-art text mining system for biomedical literature, based on a deep-linguistic, full-parsing approach. The results are validated on two different corpora: the manually annotated genomics information access (GENIA) corpus and the automatically annotated arabidopsis thaliana circadian rhythms (ATCR) corpus. We show how a deep-linguistic approach (contrary to common belief) can be used in a real world text mining application, offering high-precision relation extraction, while at the same time retaining a sufficient recall.
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Compound image segmentation of published biomedical figures.
Li, Pengyuan; Jiang, Xiangying; Kambhamettu, Chandra; Shatkay, Hagit
2018-04-01
Images convey essential information in biomedical publications. As such, there is a growing interest within the bio-curation and the bio-databases communities, to store images within publications as evidence for biomedical processes and for experimental results. However, many of the images in biomedical publications are compound images consisting of multiple panels, where each individual panel potentially conveys a different type of information. Segmenting such images into constituent panels is an essential first step toward utilizing images. In this article, we develop a new compound image segmentation system, FigSplit, which is based on Connected Component Analysis. To overcome shortcomings typically manifested by existing methods, we develop a quality assessment step for evaluating and modifying segmentations. Two methods are proposed to re-segment the images if the initial segmentation is inaccurate. Experimental results show the effectiveness of our method compared with other methods. The system is publicly available for use at: https://www.eecis.udel.edu/~compbio/FigSplit. The code is available upon request. shatkay@udel.edu. Supplementary data are available online at Bioinformatics.
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CellFinder: a cell data repository
Stachelscheid, Harald; Seltmann, Stefanie; Lekschas, Fritz; Fontaine, Jean-Fred; Mah, Nancy; Neves, Mariana; Andrade-Navarro, Miguel A.; Leser, Ulf; Kurtz, Andreas
2014-01-01
CellFinder (http://www.cellfinder.org) is a comprehensive one-stop resource for molecular data characterizing mammalian cells in different tissues and in different development stages. It is built from carefully selected data sets stemming from other curated databases and the biomedical literature. To date, CellFinder describes 3394 cell types and 50 951 cell lines. The database currently contains 3055 microscopic and anatomical images, 205 whole-genome expression profiles of 194 cell/tissue types from RNA-seq and microarrays and 553 905 protein expressions for 535 cells/tissues. Text mining of a corpus of >2000 publications followed by manual curation confirmed expression information on ∼900 proteins and genes. CellFinder’s data model is capable to seamlessly represent entities from single cells to the organ level, to incorporate mappings between homologous entities in different species and to describe processes of cell development and differentiation. Its ontological backbone currently consists of 204 741 ontology terms incorporated from 10 different ontologies unified under the novel CELDA ontology. CellFinder’s web portal allows searching, browsing and comparing the stored data, interactive construction of developmental trees and navigating the partonomic hierarchy of cells and tissues through a unique body browser designed for life scientists and clinicians. PMID:24304896
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Managing biological networks by using text mining and computer-aided curation
NASA Astrophysics Data System (ADS)
Yu, Seok Jong; Cho, Yongseong; Lee, Min-Ho; Lim, Jongtae; Yoo, Jaesoo
2015-11-01
In order to understand a biological mechanism in a cell, a researcher should collect a huge number of protein interactions with experimental data from experiments and the literature. Text mining systems that extract biological interactions from papers have been used to construct biological networks for a few decades. Even though the text mining of literature is necessary to construct a biological network, few systems with a text mining tool are available for biologists who want to construct their own biological networks. We have developed a biological network construction system called BioKnowledge Viewer that can generate a biological interaction network by using a text mining tool and biological taggers. It also Boolean simulation software to provide a biological modeling system to simulate the model that is made with the text mining tool. A user can download PubMed articles and construct a biological network by using the Multi-level Knowledge Emergence Model (KMEM), MetaMap, and A Biomedical Named Entity Recognizer (ABNER) as a text mining tool. To evaluate the system, we constructed an aging-related biological network that consist 9,415 nodes (genes) by using manual curation. With network analysis, we found that several genes, including JNK, AP-1, and BCL-2, were highly related in aging biological network. We provide a semi-automatic curation environment so that users can obtain a graph database for managing text mining results that are generated in the server system and can navigate the network with BioKnowledge Viewer, which is freely available at http://bioknowledgeviewer.kisti.re.kr.
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Kibbe, Warren A.; Arze, Cesar; Felix, Victor; Mitraka, Elvira; Bolton, Evan; Fu, Gang; Mungall, Christopher J.; Binder, Janos X.; Malone, James; Vasant, Drashtti; Parkinson, Helen; Schriml, Lynn M.
2015-01-01
The current version of the Human Disease Ontology (DO) (http://www.disease-ontology.org) database expands the utility of the ontology for the examination and comparison of genetic variation, phenotype, protein, drug and epitope data through the lens of human disease. DO is a biomedical resource of standardized common and rare disease concepts with stable identifiers organized by disease etiology. The content of DO has had 192 revisions since 2012, including the addition of 760 terms. Thirty-two percent of all terms now include definitions. DO has expanded the number and diversity of research communities and community members by 50+ during the past two years. These community members actively submit term requests, coordinate biomedical resource disease representation and provide expert curation guidance. Since the DO 2012 NAR paper, there have been hundreds of term requests and a steady increase in the number of DO listserv members, twitter followers and DO website usage. DO is moving to a multi-editor model utilizing Protégé to curate DO in web ontology language. This will enable closer collaboration with the Human Phenotype Ontology, EBI's Ontology Working Group, Mouse Genome Informatics and the Monarch Initiative among others, and enhance DO's current asserted view and multiple inferred views through reasoning. PMID:25348409
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Using Google Blogs and Discussions to Recommend Biomedical Resources: A Case Study
Reed, Robyn B.; Chattopadhyay, Ansuman; Iwema, Carrie L.
2013-01-01
This case study investigated whether data gathered from discussions within the social media provide a reliable basis for a biomedical resources recommendation system. Using a search query to mine text from Google Blogs and Discussions, a ranking of biomedical resources was determined based on those most frequently mentioned. To establish quality, these results were compared to rankings by subject experts. An overall agreement between the frequency of social media discussions and subject expert recommendations was observed when identifying key bioinformatics and consumer health resources. Testing the method in more than one biomedical area implies this procedure could be employed across different subjects. PMID:24180648
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Aubry, Marc; Monnier, Annabelle; Chicault, Celine; de Tayrac, Marie; Galibert, Marie-Dominique; Burgun, Anita; Mosser, Jean
2006-01-01
Background Large-scale genomic studies based on transcriptome technologies provide clusters of genes that need to be functionally annotated. The Gene Ontology (GO) implements a controlled vocabulary organised into three hierarchies: cellular components, molecular functions and biological processes. This terminology allows a coherent and consistent description of the knowledge about gene functions. The GO terms related to genes come primarily from semi-automatic annotations made by trained biologists (annotation based on evidence) or text-mining of the published scientific literature (literature profiling). Results We report an original functional annotation method based on a combination of evidence and literature that overcomes the weaknesses and the limitations of each approach. It relies on the Gene Ontology Annotation database (GOA Human) and the PubGene biomedical literature index. We support these annotations with statistically associated GO terms and retrieve associative relations across the three GO hierarchies to emphasise the major pathways involved by a gene cluster. Both annotation methods and associative relations were quantitatively evaluated with a reference set of 7397 genes and a multi-cluster study of 14 clusters. We also validated the biological appropriateness of our hybrid method with the annotation of a single gene (cdc2) and that of a down-regulated cluster of 37 genes identified by a transcriptome study of an in vitro enterocyte differentiation model (CaCo-2 cells). Conclusion The combination of both approaches is more informative than either separate approach: literature mining can enrich an annotation based only on evidence. Text-mining of the literature can also find valuable associated MEDLINE references that confirm the relevance of the annotation. Eventually, GO terms networks can be built with associative relations in order to highlight cooperative and competitive pathways and their connected molecular functions. PMID:16674810
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Chen, Qian; Ai, Ni; Liao, Jie; Shao, Xin; Liu, Yufeng; Fan, Xiaohui
2017-01-01
Valuable scientific results on biomedicine are very rich, but they are widely scattered in the literature. Topic modeling enables researchers to discover themes from an unstructured collection of documents without any prior annotations or labels. In this paper, taking ginseng as an example, biological dynamic topic model (Bio-DTM) was proposed to conduct a retrospective study and interpret the temporal evolution of the research of ginseng. The system of Bio-DTM mainly includes four components, documents pre-processing, bio-dictionary construction, dynamic topic models, topics analysis and visualization. Scientific articles pertaining to ginseng were retrieved through text mining from PubMed. The bio-dictionary integrates MedTerms medical dictionary, the second edition of side effect resource, a dictionary of biology and HGNC database of human gene names (HGNC). A dynamic topic model, a text mining technique, was used to emphasize on capturing the development trends of topics in a sequentially collected documents. Besides the contents of topics taken on, the evolution of topics was visualized over time using ThemeRiver. From the topic 9, ginseng was used in dietary supplements and complementary and integrative health practices, and became very popular since the early twentieth century. Topic 6 reminded that the planting of ginseng is a major area of research and symbiosis and allelopathy of ginseng became a research hotspot in 2007. In addition, the Bio-DTM model gave an insight into the main pharmacologic effects of ginseng, such as anti-metabolic disorder effect, cardioprotective effect, anti-cancer effect, hepatoprotective effect, anti-thrombotic effect and neuroprotective effect. The Bio-DTM model not only discovers what ginseng's research involving in but also displays how these topics evolving over time. This approach can be applied to the biomedical field to conduct a retrospective study and guide future studies.
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NaKnowBaseTM: The EPA Nanomaterials Research Database
The ability to predict the environmental and health implications of engineered nanomaterials is an important research priority due to the exponential rate at which nanotechnology is being incorporated into consumer, industrial and biomedical applications. To address this need and...
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The BioMedical Evidence Graph (BMEG) | Informatics Technology for Cancer Research (ITCR)
The BMEG is a Cancer Data integration Platform that utilizes methods collected from DREAM challenges and applied to large datasets, such as the TCGA, and makes them avalible for analysis using a high performance graph database
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Lin, Jimmy
2008-01-01
Background Graph analysis algorithms such as PageRank and HITS have been successful in Web environments because they are able to extract important inter-document relationships from manually-created hyperlinks. We consider the application of these techniques to biomedical text retrieval. In the current PubMed® search interface, a MEDLINE® citation is connected to a number of related citations, which are in turn connected to other citations. Thus, a MEDLINE record represents a node in a vast content-similarity network. This article explores the hypothesis that these networks can be exploited for text retrieval, in the same manner as hyperlink graphs on the Web. Results We conducted a number of reranking experiments using the TREC 2005 genomics track test collection in which scores extracted from PageRank and HITS analysis were combined with scores returned by an off-the-shelf retrieval engine. Experiments demonstrate that incorporating PageRank scores yields significant improvements in terms of standard ranked-retrieval metrics. Conclusion The link structure of content-similarity networks can be exploited to improve the effectiveness of information retrieval systems. These results generalize the applicability of graph analysis algorithms to text retrieval in the biomedical domain. PMID:18538027
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A hybrid model based on neural networks for biomedical relation extraction.
Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian; Zhang, Shaowu; Sun, Yuanyuan; Yang, Liang
2018-05-01
Biomedical relation extraction can automatically extract high-quality biomedical relations from biomedical texts, which is a vital step for the mining of biomedical knowledge hidden in the literature. Recurrent neural networks (RNNs) and convolutional neural networks (CNNs) are two major neural network models for biomedical relation extraction. Neural network-based methods for biomedical relation extraction typically focus on the sentence sequence and employ RNNs or CNNs to learn the latent features from sentence sequences separately. However, RNNs and CNNs have their own advantages for biomedical relation extraction. Combining RNNs and CNNs may improve biomedical relation extraction. In this paper, we present a hybrid model for the extraction of biomedical relations that combines RNNs and CNNs. First, the shortest dependency path (SDP) is generated based on the dependency graph of the candidate sentence. To make full use of the SDP, we divide the SDP into a dependency word sequence and a relation sequence. Then, RNNs and CNNs are employed to automatically learn the features from the sentence sequence and the dependency sequences, respectively. Finally, the output features of the RNNs and CNNs are combined to detect and extract biomedical relations. We evaluate our hybrid model using five public (protein-protein interaction) PPI corpora and a (drug-drug interaction) DDI corpus. The experimental results suggest that the advantages of RNNs and CNNs in biomedical relation extraction are complementary. Combining RNNs and CNNs can effectively boost biomedical relation extraction performance. Copyright © 2018 Elsevier Inc. All rights reserved.
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Automatic inference of indexing rules for MEDLINE
Névéol, Aurélie; Shooshan, Sonya E; Claveau, Vincent
2008-01-01
Background: Indexing is a crucial step in any information retrieval system. In MEDLINE, a widely used database of the biomedical literature, the indexing process involves the selection of Medical Subject Headings in order to describe the subject matter of articles. The need for automatic tools to assist MEDLINE indexers in this task is growing with the increasing number of publications being added to MEDLINE. Methods: In this paper, we describe the use and the customization of Inductive Logic Programming (ILP) to infer indexing rules that may be used to produce automatic indexing recommendations for MEDLINE indexers. Results: Our results show that this original ILP-based approach outperforms manual rules when they exist. In addition, the use of ILP rules also improves the overall performance of the Medical Text Indexer (MTI), a system producing automatic indexing recommendations for MEDLINE. Conclusion: We expect the sets of ILP rules obtained in this experiment to be integrated into MTI. PMID:19025687
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Automatic inference of indexing rules for MEDLINE.
Névéol, Aurélie; Shooshan, Sonya E; Claveau, Vincent
2008-11-19
Indexing is a crucial step in any information retrieval system. In MEDLINE, a widely used database of the biomedical literature, the indexing process involves the selection of Medical Subject Headings in order to describe the subject matter of articles. The need for automatic tools to assist MEDLINE indexers in this task is growing with the increasing number of publications being added to MEDLINE. In this paper, we describe the use and the customization of Inductive Logic Programming (ILP) to infer indexing rules that may be used to produce automatic indexing recommendations for MEDLINE indexers. Our results show that this original ILP-based approach outperforms manual rules when they exist. In addition, the use of ILP rules also improves the overall performance of the Medical Text Indexer (MTI), a system producing automatic indexing recommendations for MEDLINE. We expect the sets of ILP rules obtained in this experiment to be integrated into MTI.
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Semantic characteristics of NLP-extracted concepts in clinical notes vs. biomedical literature.
Wu, Stephen; Liu, Hongfang
2011-01-01
Natural language processing (NLP) has become crucial in unlocking information stored in free text, from both clinical notes and biomedical literature. Clinical notes convey clinical information related to individual patient health care, while biomedical literature communicates scientific findings. This work focuses on semantic characterization of texts at an enterprise scale, comparing and contrasting the two domains and their NLP approaches. We analyzed the empirical distributional characteristics of NLP-discovered named entities in Mayo Clinic clinical notes from 2001-2010, and in the 2011 MetaMapped Medline Baseline. We give qualitative and quantitative measures of domain similarity and point to the feasibility of transferring resources and techniques. An important by-product for this study is the development of a weighted ontology for each domain, which gives distributional semantic information that may be used to improve NLP applications.
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Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
ERIC Educational Resources Information Center
Imbens, Guido W.; Rubin, Donald B.
2015-01-01
Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions. This book starts with the notion of potential outcomes, each corresponding…
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Read-across predictions require high quality measured data for source analogues. These data are typically retrieved from structured databases, but biomedical literature data are often untapped because current literature mining approaches are resource intensive. Our high-throughpu...
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Chembank | Office of Cancer Genomics
Funded in large part by the Initiative for Chemical Genetics (ICG), Chembank is an interactive database for small molecules. It contains data from hundreds of biomedically relevant small molecule screens that involved hundreds-of-thousands of compounds. Chembank also provides analysis tools to facilitate data mining.
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Stephens, Susie M.; Chen, Jake Y.; Davidson, Marcel G.; Thomas, Shiby; Trute, Barry M.
2005-01-01
As database management systems expand their array of analytical functionality, they become powerful research engines for biomedical data analysis and drug discovery. Databases can hold most of the data types commonly required in life sciences and consequently can be used as flexible platforms for the implementation of knowledgebases. Performing data analysis in the database simplifies data management by minimizing the movement of data from disks to memory, allowing pre-filtering and post-processing of datasets, and enabling data to remain in a secure, highly available environment. This article describes the Oracle Database 10g implementation of BLAST and Regular Expression Searches and provides case studies of their usage in bioinformatics. http://www.oracle.com/technology/software/index.html PMID:15608287
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Lowe, H J; Lomax, E C; Polonkey, S E
1996-01-01
The Internet is rapidly evolving from a resource used primarily by the research community to a true global information network offering a wide range of databases and services. This evolution presents many opportunities for improved access to biomedical information, but Internet-based resources have often been difficult for the non-expert to develop and use. The World Wide Web (WWW) supports an inexpensive, easy-to-use, cross-platform, graphic interface to the Internet that may radically alter the way we retrieve and disseminate medical data. This paper summarizes the Internet and hypertext origins of the WWW, reviews WWW-specific technologies, and describes current and future applications of this technology in medicine and medical informatics. The paper also includes an appendix of useful biomedical WWW servers. PMID:8750386
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Kim, Sun; Chatr-aryamontri, Andrew; Chang, Christie S.; Oughtred, Rose; Rust, Jennifer; Wilbur, W. John; Comeau, Donald C.; Dolinski, Kara; Tyers, Mike
2017-01-01
A great deal of information on the molecular genetics and biochemistry of model organisms has been reported in the scientific literature. However, this data is typically described in free text form and is not readily amenable to computational analyses. To this end, the BioGRID database systematically curates the biomedical literature for genetic and protein interaction data. This data is provided in a standardized computationally tractable format and includes structured annotation of experimental evidence. BioGRID curation necessarily involves substantial human effort by expert curators who must read each publication to extract the relevant information. Computational text-mining methods offer the potential to augment and accelerate manual curation. To facilitate the development of practical text-mining strategies, a new challenge was organized in BioCreative V for the BioC task, the collaborative Biocurator Assistant Task. This was a non-competitive, cooperative task in which the participants worked together to build BioC-compatible modules into an integrated pipeline to assist BioGRID curators. As an integral part of this task, a test collection of full text articles was developed that contained both biological entity annotations (gene/protein and organism/species) and molecular interaction annotations (protein–protein and genetic interactions (PPIs and GIs)). This collection, which we call the BioC-BioGRID corpus, was annotated by four BioGRID curators over three rounds of annotation and contains 120 full text articles curated in a dataset representing two major model organisms, namely budding yeast and human. The BioC-BioGRID corpus contains annotations for 6409 mentions of genes and their Entrez Gene IDs, 186 mentions of organism names and their NCBI Taxonomy IDs, 1867 mentions of PPIs and 701 annotations of PPI experimental evidence statements, 856 mentions of GIs and 399 annotations of GI evidence statements. The purpose, characteristics and possible future uses of the BioC-BioGRID corpus are detailed in this report. Database URL: http://bioc.sourceforge.net/BioC-BioGRID.html PMID:28077563
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Challenges for automatically extracting molecular interactions from full-text articles.
McIntosh, Tara; Curran, James R
2009-09-24
The increasing availability of full-text biomedical articles will allow more biomedical knowledge to be extracted automatically with greater reliability. However, most Information Retrieval (IR) and Extraction (IE) tools currently process only abstracts. The lack of corpora has limited the development of tools that are capable of exploiting the knowledge in full-text articles. As a result, there has been little investigation into the advantages of full-text document structure, and the challenges developers will face in processing full-text articles. We manually annotated passages from full-text articles that describe interactions summarised in a Molecular Interaction Map (MIM). Our corpus tracks the process of identifying facts to form the MIM summaries and captures any factual dependencies that must be resolved to extract the fact completely. For example, a fact in the results section may require a synonym defined in the introduction. The passages are also annotated with negated and coreference expressions that must be resolved.We describe the guidelines for identifying relevant passages and possible dependencies. The corpus includes 2162 sentences from 78 full-text articles. Our corpus analysis demonstrates the necessity of full-text processing; identifies the article sections where interactions are most commonly stated; and quantifies the proportion of interaction statements requiring coherent dependencies. Further, it allows us to report on the relative importance of identifying synonyms and resolving negated expressions. We also experiment with an oracle sentence retrieval system using the corpus as a gold-standard evaluation set. We introduce the MIM corpus, a unique resource that maps interaction facts in a MIM to annotated passages within full-text articles. It is an invaluable case study providing guidance to developers of biomedical IR and IE systems, and can be used as a gold-standard evaluation set for full-text IR tasks.
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Extracting microRNA-gene relations from biomedical literature using distant supervision
Clarke, Luka A.; Couto, Francisco M.
2017-01-01
Many biomedical relation extraction approaches are based on supervised machine learning, requiring an annotated corpus. Distant supervision aims at training a classifier by combining a knowledge base with a corpus, reducing the amount of manual effort necessary. This is particularly useful for biomedicine because many databases and ontologies have been made available for many biological processes, while the availability of annotated corpora is still limited. We studied the extraction of microRNA-gene relations from text. MicroRNA regulation is an important biological process due to its close association with human diseases. The proposed method, IBRel, is based on distantly supervised multi-instance learning. We evaluated IBRel on three datasets, and the results were compared with a co-occurrence approach as well as a supervised machine learning algorithm. While supervised learning outperformed on two of those datasets, IBRel obtained an F-score 28.3 percentage points higher on the dataset for which there was no training set developed specifically. To demonstrate the applicability of IBRel, we used it to extract 27 miRNA-gene relations from recently published papers about cystic fibrosis. Our results demonstrate that our method can be successfully used to extract relations from literature about a biological process without an annotated corpus. The source code and data used in this study are available at https://github.com/AndreLamurias/IBRel. PMID:28263989
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Extracting microRNA-gene relations from biomedical literature using distant supervision.
Lamurias, Andre; Clarke, Luka A; Couto, Francisco M
2017-01-01
Many biomedical relation extraction approaches are based on supervised machine learning, requiring an annotated corpus. Distant supervision aims at training a classifier by combining a knowledge base with a corpus, reducing the amount of manual effort necessary. This is particularly useful for biomedicine because many databases and ontologies have been made available for many biological processes, while the availability of annotated corpora is still limited. We studied the extraction of microRNA-gene relations from text. MicroRNA regulation is an important biological process due to its close association with human diseases. The proposed method, IBRel, is based on distantly supervised multi-instance learning. We evaluated IBRel on three datasets, and the results were compared with a co-occurrence approach as well as a supervised machine learning algorithm. While supervised learning outperformed on two of those datasets, IBRel obtained an F-score 28.3 percentage points higher on the dataset for which there was no training set developed specifically. To demonstrate the applicability of IBRel, we used it to extract 27 miRNA-gene relations from recently published papers about cystic fibrosis. Our results demonstrate that our method can be successfully used to extract relations from literature about a biological process without an annotated corpus. The source code and data used in this study are available at https://github.com/AndreLamurias/IBRel.
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Heterogeneous database integration in biomedicine.
Sujansky, W
2001-08-01
The rapid expansion of biomedical knowledge, reduction in computing costs, and spread of internet access have created an ocean of electronic data. The decentralized nature of our scientific community and healthcare system, however, has resulted in a patchwork of diverse, or heterogeneous, database implementations, making access to and aggregation of data across databases very difficult. The database heterogeneity problem applies equally to clinical data describing individual patients and biological data characterizing our genome. Specifically, databases are highly heterogeneous with respect to the data models they employ, the data schemas they specify, the query languages they support, and the terminologies they recognize. Heterogeneous database systems attempt to unify disparate databases by providing uniform conceptual schemas that resolve representational heterogeneities, and by providing querying capabilities that aggregate and integrate distributed data. Research in this area has applied a variety of database and knowledge-based techniques, including semantic data modeling, ontology definition, query translation, query optimization, and terminology mapping. Existing systems have addressed heterogeneous database integration in the realms of molecular biology, hospital information systems, and application portability.
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Liu, Qin; Tian, Li-Guang; Xiao, Shu-Hua; Qi, Zhen; Steinmann, Peter; Mak, Tippi K; Utzinger, Jürg; Zhou, Xiao-Nong
2008-01-01
The economy of China continues to boom and so have its biomedical research and related publishing activities. Several so-called neglected tropical diseases that are most common in the developing world are still rampant or even emerging in some parts of China. The purpose of this article is to document the significant research potential from the Chinese biomedical bibliographic databases. The research contributions from China in the epidemiology and control of schistosomiasis provide an excellent illustration. We searched two widely used databases, namely China National Knowledge Infrastructure (CNKI) and VIP Information (VIP). Employing the keyword "Schistosoma" () and covering the period 1990–2006, we obtained 10,244 hits in the CNKI database and 5,975 in VIP. We examined 10 Chinese biomedical journals that published the highest number of original research articles on schistosomiasis for issues including languages and open access. Although most of the journals are published in Chinese, English abstracts are usually available. Open access to full articles was available in China Tropical Medicine in 2005/2006 and is granted by the Chinese Journal of Parasitology and Parasitic Diseases since 2003; none of the other journals examined offered open access. We reviewed (i) the discovery and development of antischistosomal drugs, (ii) the progress made with molluscicides and (iii) environmental management for schistosomiasis control in China over the past 20 years. In conclusion, significant research is published in the Chinese literature, which is relevant for local control measures and global scientific knowledge. Open access should be encouraged and language barriers removed so the wealth of Chinese research can be more fully appreciated by the scientific community. PMID:18826598
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Literature searches on Ayurveda: An update
Aggithaya, Madhur G.; Narahari, Saravu R.
2015-01-01
Introduction: The journals that publish on Ayurveda are increasingly indexed by popular medical databases in recent years. However, many Eastern journals are not indexed biomedical journal databases such as PubMed. Literature searches for Ayurveda continue to be challenging due to the nonavailability of active, unbiased dedicated databases for Ayurvedic literature. In 2010, authors identified 46 databases that can be used for systematic search of Ayurvedic papers and theses. This update reviewed our previous recommendation and identified current and relevant databases. Aims: To update on Ayurveda literature search and strategy to retrieve maximum publications. Methods: Author used psoriasis as an example to search previously listed databases and identify new. The population, intervention, control, and outcome table included keywords related to psoriasis and Ayurvedic terminologies for skin diseases. Current citation update status, search results, and search options of previous databases were assessed. Eight search strategies were developed. Hundred and five journals, both biomedical and Ayurveda, which publish on Ayurveda, were identified. Variability in databases was explored to identify bias in journal citation. Results: Five among 46 databases are now relevant – AYUSH research portal, Annotated Bibliography of Indian Medicine, Digital Helpline for Ayurveda Research Articles (DHARA), PubMed, and Directory of Open Access Journals. Search options in these databases are not uniform, and only PubMed allows complex search strategy. “The Researches in Ayurveda” and “Ayurvedic Research Database” (ARD) are important grey resources for hand searching. About 44/105 (41.5%) journals publishing Ayurvedic studies are not indexed in any database. Only 11/105 (10.4%) exclusive Ayurveda journals are indexed in PubMed. Conclusion: AYUSH research portal and DHARA are two major portals after 2010. It is mandatory to search PubMed and four other databases because all five carry citations from different groups of journals. The hand searching is important to identify Ayurveda publications that are not indexed elsewhere. Availability information of citations in Ayurveda libraries from National Union Catalogue of Scientific Serials in India if regularly updated will improve the efficacy of hand searching. A grey database (ARD) contains unpublished PG/Ph.D. theses. The AYUSH portal, DHARA (funded by Ministry of AYUSH), and ARD should be merged to form single larger database to limit Ayurveda literature searches. PMID:27313409
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[The Chilean Association of Biomedical Journal Editors].
Reyes, H
2001-01-01
On September 29th, 2000, The Chilean Association of Biomedical Journal Editors was founded, sponsored by the "Comisión Nacional de Investigación Científica y Tecnológica (CONICYT)" (the Governmental Agency promoting and funding scientific research and technological development in Chile) and the "Sociedad Médica de Santiago" (Chilean Society of Internal Medicine). The Association adopted the goals of the World Association of Medical Editors (WAME) and therefore it will foster "cooperation and communication among Editors of Chilean biomedical journals; to improve editorial standards, to promote professionalism in medical editing through education, self-criticism and self-regulation; and to encourage research on the principles and practice of medical editing". Twenty nine journals covering a closely similar number of different biomedical sciences, medical specialties, veterinary, dentistry and nursing, became Founding Members of the Association. A Governing Board was elected: President: Humberto Reyes, M.D. (Editor, Revista Médica de Chile); Vice-President: Mariano del Sol, M.D. (Editor, Revista Chilena de Anatomía); Secretary: Anna María Prat (CONICYT); Councilors: Manuel Krauskopff, Ph.D. (Editor, Biological Research) and Maritza Rahal, M.D. (Editor, Revista de Otorrinolaringología y Cirugía de Cabeza y Cuello). The Association will organize a Symposium on Biomedical Journal Editing and will spread information stimulating Chilean biomedical journals to become indexed in international databases and in SciELO-Chile, the main Chilean scientific website (www.scielo.cl).
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Supervised Learning Based Hypothesis Generation from Biomedical Literature.
Sang, Shengtian; Yang, Zhihao; Li, Zongyao; Lin, Hongfei
2015-01-01
Nowadays, the amount of biomedical literatures is growing at an explosive speed, and there is much useful knowledge undiscovered in this literature. Researchers can form biomedical hypotheses through mining these works. In this paper, we propose a supervised learning based approach to generate hypotheses from biomedical literature. This approach splits the traditional processing of hypothesis generation with classic ABC model into AB model and BC model which are constructed with supervised learning method. Compared with the concept cooccurrence and grammar engineering-based approaches like SemRep, machine learning based models usually can achieve better performance in information extraction (IE) from texts. Then through combining the two models, the approach reconstructs the ABC model and generates biomedical hypotheses from literature. The experimental results on the three classic Swanson hypotheses show that our approach outperforms SemRep system.
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Full-Text Databases in Medicine.
ERIC Educational Resources Information Center
Sievert, MaryEllen C.; And Others
1995-01-01
Describes types of full-text databases in medicine; discusses features for searching full-text journal databases available through online vendors; reviews research on full-text databases in medicine; and describes the MEDLINE/Full-Text Research Project at the University of Missouri (Columbia) which investigated precision, recall, and relevancy.…
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DBMap: a TreeMap-based framework for data navigation and visualization of brain research registry
NASA Astrophysics Data System (ADS)
Zhang, Ming; Zhang, Hong; Tjandra, Donny; Wong, Stephen T. C.
2003-05-01
The purpose of this study is to investigate and apply a new, intuitive and space-conscious visualization framework to facilitate efficient data presentation and exploration of large-scale data warehouses. We have implemented the DBMap framework for the UCSF Brain Research Registry. Such a novel utility would facilitate medical specialists and clinical researchers in better exploring and evaluating a number of attributes organized in the brain research registry. The current UCSF Brain Research Registry consists of a federation of disease-oriented database modules, including Epilepsy, Brain Tumor, Intracerebral Hemorrphage, and CJD (Creuzfeld-Jacob disease). These database modules organize large volumes of imaging and non-imaging data to support Web-based clinical research. While the data warehouse supports general information retrieval and analysis, there lacks an effective way to visualize and present the voluminous and complex data stored. This study investigates whether the TreeMap algorithm can be adapted to display and navigate categorical biomedical data warehouse or registry. TreeMap is a space constrained graphical representation of large hierarchical data sets, mapped to a matrix of rectangles, whose size and color represent interested database fields. It allows the display of a large amount of numerical and categorical information in limited real estate of computer screen with an intuitive user interface. The paper will describe, DBMap, the proposed new data visualization framework for large biomedical databases. Built upon XML, Java and JDBC technologies, the prototype system includes a set of software modules that reside in the application server tier and provide interface to backend database tier and front-end Web tier of the brain registry.
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Comparison of PubMed, Scopus, Web of Science, and Google Scholar: strengths and weaknesses.
Falagas, Matthew E; Pitsouni, Eleni I; Malietzis, George A; Pappas, Georgios
2008-02-01
The evolution of the electronic age has led to the development of numerous medical databases on the World Wide Web, offering search facilities on a particular subject and the ability to perform citation analysis. We compared the content coverage and practical utility of PubMed, Scopus, Web of Science, and Google Scholar. The official Web pages of the databases were used to extract information on the range of journals covered, search facilities and restrictions, and update frequency. We used the example of a keyword search to evaluate the usefulness of these databases in biomedical information retrieval and a specific published article to evaluate their utility in performing citation analysis. All databases were practical in use and offered numerous search facilities. PubMed and Google Scholar are accessed for free. The keyword search with PubMed offers optimal update frequency and includes online early articles; other databases can rate articles by number of citations, as an index of importance. For citation analysis, Scopus offers about 20% more coverage than Web of Science, whereas Google Scholar offers results of inconsistent accuracy. PubMed remains an optimal tool in biomedical electronic research. Scopus covers a wider journal range, of help both in keyword searching and citation analysis, but it is currently limited to recent articles (published after 1995) compared with Web of Science. Google Scholar, as for the Web in general, can help in the retrieval of even the most obscure information but its use is marred by inadequate, less often updated, citation information.
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Combining Crowd and Expert Labels using Decision Theoretic Active Learning
2015-10-11
meta-data such as titles, author information and keywords. Motivating Application: Biomedical Systematic Reviews Evidence - based medicine (EBM) aims to...individuals trained in evidence - based medicine ; usually MDs) reading the entire set of citations retrieved via database search to identify the small
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Schneeweiss, S; Eichler, H-G; Garcia-Altes, A; Chinn, C; Eggimann, A-V; Garner, S; Goettsch, W; Lim, R; Löbker, W; Martin, D; Müller, T; Park, B J; Platt, R; Priddy, S; Ruhl, M; Spooner, A; Vannieuwenhuyse, B; Willke, R J
2016-12-01
Analyses of healthcare databases (claims, electronic health records [EHRs]) are useful supplements to clinical trials for generating evidence on the effectiveness, harm, use, and value of medical products in routine care. A constant stream of data from the routine operation of modern healthcare systems, which can be analyzed in rapid cycles, enables incremental evidence development to support accelerated and appropriate access to innovative medicines. Evidentiary needs by regulators, Health Technology Assessment, payers, clinicians, and patients after marketing authorization comprise (1) monitoring of medication performance in routine care, including the materialized effectiveness, harm, and value; (2) identifying new patient strata with added value or unacceptable harms; and (3) monitoring targeted utilization. Adaptive biomedical innovation (ABI) with rapid cycle database analytics is successfully enabled if evidence is meaningful, valid, expedited, and transparent. These principles will bring rigor and credibility to current efforts to increase research efficiency while upholding evidentiary standards required for effective decision-making in healthcare. © 2016 American Society for Clinical Pharmacology and Therapeutics.
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Genomics Community Resources | Informatics Technology for Cancer Research (ITCR)
To facilitate genomic research and the dissemination of its products, National Human Genome Research Institute (NHGRI) supports genomic resources that are crucial for basic research, disease studies, model organism studies, and other biomedical research. Awards under this FOA will support the development and distribution of genomic resources that will be valuable for the broad research community, using cost-effective approaches. Such resources include (but are not limited to) databases and informatics resources (such as human and model organism databases, ontologies, and analysi
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Reinforcement learning interfaces for biomedical database systems.
Rudowsky, I; Kulyba, O; Kunin, M; Parsons, S; Raphan, T
2006-01-01
Studies of neural function that are carried out in different laboratories and that address different questions use a wide range of descriptors for data storage, depending on the laboratory and the individuals that input the data. A common approach to describe non-textual data that are referenced through a relational database is to use metadata descriptors. We have recently designed such a prototype system, but to maintain efficiency and a manageable metadata table, free formatted fields were designed as table entries. The database interface application utilizes an intelligent agent to improve integrity of operation. The purpose of this study was to investigate how reinforcement learning algorithms can assist the user in interacting with the database interface application that has been developed to improve the performance of the system.
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Usié, Anabel; Cruz, Joaquim; Comas, Jorge; Solsona, Francesc; Alves, Rui
2015-01-01
Small chemical molecules regulate biological processes at the molecular level. Those molecules are often involved in causing or treating pathological states. Automatically identifying such molecules in biomedical text is difficult due to both, the diverse morphology of chemical names and the alternative types of nomenclature that are simultaneously used to describe them. To address these issues, the last BioCreAtIvE challenge proposed a CHEMDNER task, which is a Named Entity Recognition (NER) challenge that aims at labelling different types of chemical names in biomedical text. To address this challenge we tested various approaches to recognizing chemical entities in biomedical documents. These approaches range from linear Conditional Random Fields (CRFs) to a combination of CRFs with regular expression and dictionary matching, followed by a post-processing step to tag those chemical names in a corpus of Medline abstracts. We named our best performing systems CheNER. We evaluate the performance of the various approaches using the F-score statistics. Higher F-scores indicate better performance. The highest F-score we obtain in identifying unique chemical entities is 72.88%. The highest F-score we obtain in identifying all chemical entities is 73.07%. We also evaluate the F-Score of combining our system with ChemSpot, and find an increase from 72.88% to 73.83%. CheNER presents a valid alternative for automated annotation of chemical entities in biomedical documents. In addition, CheNER may be used to derive new features to train newer methods for tagging chemical entities. CheNER can be downloaded from http://metres.udl.cat and included in text annotation pipelines.
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Mouriño García, Marcos Antonio; Pérez Rodríguez, Roberto; Anido Rifón, Luis E
2015-01-01
Automatic classification of text documents into a set of categories has a lot of applications. Among those applications, the automatic classification of biomedical literature stands out as an important application for automatic document classification strategies. Biomedical staff and researchers have to deal with a lot of literature in their daily activities, so it would be useful a system that allows for accessing to documents of interest in a simple and effective way; thus, it is necessary that these documents are sorted based on some criteria-that is to say, they have to be classified. Documents to classify are usually represented following the bag-of-words (BoW) paradigm. Features are words in the text-thus suffering from synonymy and polysemy-and their weights are just based on their frequency of occurrence. This paper presents an empirical study of the efficiency of a classifier that leverages encyclopedic background knowledge-concretely Wikipedia-in order to create bag-of-concepts (BoC) representations of documents, understanding concept as "unit of meaning", and thus tackling synonymy and polysemy. Besides, the weighting of concepts is based on their semantic relevance in the text. For the evaluation of the proposal, empirical experiments have been conducted with one of the commonly used corpora for evaluating classification and retrieval of biomedical information, OHSUMED, and also with a purpose-built corpus of MEDLINE biomedical abstracts, UVigoMED. Results obtained show that the Wikipedia-based bag-of-concepts representation outperforms the classical bag-of-words representation up to 157% in the single-label classification problem and up to 100% in the multi-label problem for OHSUMED corpus, and up to 122% in the single-label classification problem and up to 155% in the multi-label problem for UVigoMED corpus.
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Albin, Aaron; Ji, Xiaonan; Borlawsky, Tara B; Ye, Zhan; Lin, Simon; Payne, Philip Ro; Huang, Kun; Xiang, Yang
2014-10-07
The Unified Medical Language System (UMLS) contains many important ontologies in which terms are connected by semantic relations. For many studies on the relationships between biomedical concepts, the use of transitively associated information from ontologies and the UMLS has been shown to be effective. Although there are a few tools and methods available for extracting transitive relationships from the UMLS, they usually have major restrictions on the length of transitive relations or on the number of data sources. Our goal was to design an efficient online platform that enables efficient studies on the conceptual relationships between any medical terms. To overcome the restrictions of available methods and to facilitate studies on the conceptual relationships between medical terms, we developed a Web platform, onGrid, that supports efficient transitive queries and conceptual relationship studies using the UMLS. This framework uses the latest technique in converting natural language queries into UMLS concepts, performs efficient transitive queries, and visualizes the result paths. It also dynamically builds a relationship matrix for two sets of input biomedical terms. We are thus able to perform effective studies on conceptual relationships between medical terms based on their relationship matrix. The advantage of onGrid is that it can be applied to study any two sets of biomedical concept relations and the relations within one set of biomedical concepts. We use onGrid to study the disease-disease relationships in the Online Mendelian Inheritance in Man (OMIM). By crossvalidating our results with an external database, the Comparative Toxicogenomics Database (CTD), we demonstrated that onGrid is effective for the study of conceptual relationships between medical terms. onGrid is an efficient tool for querying the UMLS for transitive relations, studying the relationship between medical terms, and generating hypotheses.
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Identifying interactions between chemical entities in biomedical text.
Lamurias, Andre; Ferreira, João D; Couto, Francisco M
2014-10-23
Interactions between chemical compounds described in biomedical text can be of great importance to drug discovery and design, as well as pharmacovigilance. We developed a novel system, \\"Identifying Interactions between Chemical Entities\\" (IICE), to identify chemical interactions described in text. Kernel-based Support Vector Machines first identify the interactions and then an ensemble classifier validates and classifies the type of each interaction. This relation extraction module was evaluated with the corpus released for the DDI Extraction task of SemEval 2013, obtaining results comparable to state-of-the-art methods for this type of task. We integrated this module with our chemical named entity recognition module and made the whole system available as a web tool at www.lasige.di.fc.ul.pt/webtools/iice.
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Identifying interactions between chemical entities in biomedical text.
Lamurias, Andre; Ferreira, João D; Couto, Francisco M
2014-12-01
Interactions between chemical compounds described in biomedical text can be of great importance to drug discovery and design, as well as pharmacovigilance. We developed a novel system, "Identifying Interactions between Chemical Entities" (IICE), to identify chemical interactions described in text. Kernel-based Support Vector Machines first identify the interactions and then an ensemble classifier validates and classifies the type of each interaction. This relation extraction module was evaluated with the corpus released for the DDI Extraction task of SemEval 2013, obtaining results comparable to stateof- the-art methods for this type of task. We integrated this module with our chemical named entity recognition module and made the whole system available as a web tool at www.lasige.di.fc.ul.pt/webtools/iice.
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Twitter K-H networks in action: Advancing biomedical literature for drug search.
Hamed, Ahmed Abdeen; Wu, Xindong; Erickson, Robert; Fandy, Tamer
2015-08-01
The importance of searching biomedical literature for drug interaction and side-effects is apparent. Current digital libraries (e.g., PubMed) suffer infrequent tagging and metadata annotation updates. Such limitations cause absence of linking literature to new scientific evidence. This demonstrates a great deal of challenges that stand in the way of scientists when searching biomedical repositories. In this paper, we present a network mining approach that provides a bridge for linking and searching drug-related literature. Our contributions here are two fold: (1) an efficient algorithm called HashPairMiner to address the run-time complexity issues demonstrated in its predecessor algorithm: HashnetMiner, and (2) a database of discoveries hosted on the web to facilitate literature search using the results produced by HashPairMiner. Though the K-H network model and the HashPairMiner algorithm are fairly young, their outcome is evidence of the considerable promise they offer to the biomedical science community in general and the drug research community in particular. Copyright © 2015 Elsevier Inc. All rights reserved.
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Uniform resolution of compact identifiers for biomedical data
Wimalaratne, Sarala M.; Juty, Nick; Kunze, John; Janée, Greg; McMurry, Julie A.; Beard, Niall; Jimenez, Rafael; Grethe, Jeffrey S.; Hermjakob, Henning; Martone, Maryann E.; Clark, Tim
2018-01-01
Most biomedical data repositories issue locally-unique accessions numbers, but do not provide globally unique, machine-resolvable, persistent identifiers for their datasets, as required by publishers wishing to implement data citation in accordance with widely accepted principles. Local accessions may however be prefixed with a namespace identifier, providing global uniqueness. Such “compact identifiers” have been widely used in biomedical informatics to support global resource identification with local identifier assignment. We report here on our project to provide robust support for machine-resolvable, persistent compact identifiers in biomedical data citation, by harmonizing the Identifiers.org and N2T.net (Name-To-Thing) meta-resolvers and extending their capabilities. Identifiers.org services hosted at the European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI), and N2T.net services hosted at the California Digital Library (CDL), can now resolve any given identifier from over 600 source databases to its original source on the Web, using a common registry of prefix-based redirection rules. We believe these services will be of significant help to publishers and others implementing persistent, machine-resolvable citation of research data. PMID:29737976
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Mapping annotations with textual evidence using an scLDA model.
Jin, Bo; Chen, Vicky; Chen, Lujia; Lu, Xinghua
2011-01-01
Most of the knowledge regarding genes and proteins is stored in biomedical literature as free text. Extracting information from complex biomedical texts demands techniques capable of inferring biological concepts from local text regions and mapping them to controlled vocabularies. To this end, we present a sentence-based correspondence latent Dirichlet allocation (scLDA) model which, when trained with a corpus of PubMed documents with known GO annotations, performs the following tasks: 1) learning major biological concepts from the corpus, 2) inferring the biological concepts existing within text regions (sentences), and 3) identifying the text regions in a document that provides evidence for the observed annotations. When applied to new gene-related documents, a trained scLDA model is capable of predicting GO annotations and identifying text regions as textual evidence supporting the predicted annotations. This study uses GO annotation data as a testbed; the approach can be generalized to other annotated data, such as MeSH and MEDLINE documents.
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Kibbe, Warren A; Arze, Cesar; Felix, Victor; Mitraka, Elvira; Bolton, Evan; Fu, Gang; Mungall, Christopher J; Binder, Janos X; Malone, James; Vasant, Drashtti; Parkinson, Helen; Schriml, Lynn M
2015-01-01
The current version of the Human Disease Ontology (DO) (http://www.disease-ontology.org) database expands the utility of the ontology for the examination and comparison of genetic variation, phenotype, protein, drug and epitope data through the lens of human disease. DO is a biomedical resource of standardized common and rare disease concepts with stable identifiers organized by disease etiology. The content of DO has had 192 revisions since 2012, including the addition of 760 terms. Thirty-two percent of all terms now include definitions. DO has expanded the number and diversity of research communities and community members by 50+ during the past two years. These community members actively submit term requests, coordinate biomedical resource disease representation and provide expert curation guidance. Since the DO 2012 NAR paper, there have been hundreds of term requests and a steady increase in the number of DO listserv members, twitter followers and DO website usage. DO is moving to a multi-editor model utilizing Protégé to curate DO in web ontology language. This will enable closer collaboration with the Human Phenotype Ontology, EBI's Ontology Working Group, Mouse Genome Informatics and the Monarch Initiative among others, and enhance DO's current asserted view and multiple inferred views through reasoning. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.
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Kibbe, Warren A.; Arze, Cesar; Felix, Victor; ...
2014-10-27
The current version of the Human Disease Ontology (DO) (http://www.disease-ontology.org) database expands the utility of the ontology for the examination and comparison of genetic variation, phenotype, protein, drug and epitope data through the lens of human disease. DO is a biomedical resource of standardized common and rare disease concepts with stable identifiers organized by disease etiology. The content of DO has had 192 revisions since 2012, including the addition of 760 terms. Thirty-two percent of all terms now include definitions. DO has expanded the number and diversity of research communities and community members by 50+ during the past two years.more » These community members actively submit term requests, coordinate biomedical resource disease representation and provide expert curation guidance. Since the DO 2012 NAR paper, there have been hundreds of term requests and a steady increase in the number of DO listserv members, twitter followers and DO website usage. DO is moving to a multi-editor model utilizing Protégé to curate DO in web ontology language. In conclusion, this will enable closer collaboration with the Human Phenotype Ontology, EBI's Ontology Working Group, Mouse Genome Informatics and the Monarch Initiative among others, and enhance DO's current asserted view and multiple inferred views through reasoning.« less
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Kibbe, Warren A.; Arze, Cesar; Felix, Victor
The current version of the Human Disease Ontology (DO) (http://www.disease-ontology.org) database expands the utility of the ontology for the examination and comparison of genetic variation, phenotype, protein, drug and epitope data through the lens of human disease. DO is a biomedical resource of standardized common and rare disease concepts with stable identifiers organized by disease etiology. The content of DO has had 192 revisions since 2012, including the addition of 760 terms. Thirty-two percent of all terms now include definitions. DO has expanded the number and diversity of research communities and community members by 50+ during the past two years.more » These community members actively submit term requests, coordinate biomedical resource disease representation and provide expert curation guidance. Since the DO 2012 NAR paper, there have been hundreds of term requests and a steady increase in the number of DO listserv members, twitter followers and DO website usage. DO is moving to a multi-editor model utilizing Protégé to curate DO in web ontology language. In conclusion, this will enable closer collaboration with the Human Phenotype Ontology, EBI's Ontology Working Group, Mouse Genome Informatics and the Monarch Initiative among others, and enhance DO's current asserted view and multiple inferred views through reasoning.« less
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GOVERNING GENETIC DATABASES: COLLECTION, STORAGE AND USE
Gibbons, Susan M.C.; Kaye, Jane
2008-01-01
This paper provides an introduction to a collection of five papers, published as a special symposium journal issue, under the title: “Governing Genetic Databases: Collection, Storage and Use”. It begins by setting the scene, to provide a backdrop and context for the papers. It describes the evolving scientific landscape around genetic databases and genomic research, particularly within the biomedical and criminal forensic investigation fields. It notes the lack of any clear, coherent or coordinated legal governance regime, either at the national or international level. It then identifies and reflects on key cross-cutting issues and themes that emerge from the five papers, in particular: terminology and definitions; consent; special concerns around population genetic databases (biobanks) and forensic databases; international harmonisation; data protection; data access; boundary-setting; governance; and issues around balancing individual interests against public good values. PMID:18841252
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Grethe, Jeffrey S; Ross, Edward; Little, David; Sanders, Brian; Gupta, Amarnath; Astakhov, Vadim
2009-01-01
This paper presents current progress in the development of semantic data integration environment which is a part of the Biomedical Informatics Research Network (BIRN; http://www.nbirn.net) project. BIRN is sponsored by the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH). A goal is the development of a cyberinfrastructure for biomedical research that supports advance data acquisition, data storage, data management, data integration, data mining, data visualization, and other computing and information processing services over the Internet. Each participating institution maintains storage of their experimental or computationally derived data. Mediator-based data integration system performs semantic integration over the databases to enable researchers to perform analyses based on larger and broader datasets than would be available from any single institution's data. This paper describes recent revision of the system architecture, implementation, and capabilities of the semantically based data integration environment for BIRN.
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A multilingual gold-standard corpus for biomedical concept recognition: the Mantra GSC
Clematide, Simon; Akhondi, Saber A; van Mulligen, Erik M; Rebholz-Schuhmann, Dietrich
2015-01-01
Objective To create a multilingual gold-standard corpus for biomedical concept recognition. Materials and methods We selected text units from different parallel corpora (Medline abstract titles, drug labels, biomedical patent claims) in English, French, German, Spanish, and Dutch. Three annotators per language independently annotated the biomedical concepts, based on a subset of the Unified Medical Language System and covering a wide range of semantic groups. To reduce the annotation workload, automatically generated preannotations were provided. Individual annotations were automatically harmonized and then adjudicated, and cross-language consistency checks were carried out to arrive at the final annotations. Results The number of final annotations was 5530. Inter-annotator agreement scores indicate good agreement (median F-score 0.79), and are similar to those between individual annotators and the gold standard. The automatically generated harmonized annotation set for each language performed equally well as the best annotator for that language. Discussion The use of automatic preannotations, harmonized annotations, and parallel corpora helped to keep the manual annotation efforts manageable. The inter-annotator agreement scores provide a reference standard for gauging the performance of automatic annotation techniques. Conclusion To our knowledge, this is the first gold-standard corpus for biomedical concept recognition in languages other than English. Other distinguishing features are the wide variety of semantic groups that are being covered, and the diversity of text genres that were annotated. PMID:25948699
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A review on computational systems biology of pathogen–host interactions
Durmuş, Saliha; Çakır, Tunahan; Özgür, Arzucan; Guthke, Reinhard
2015-01-01
Pathogens manipulate the cellular mechanisms of host organisms via pathogen–host interactions (PHIs) in order to take advantage of the capabilities of host cells, leading to infections. The crucial role of these interspecies molecular interactions in initiating and sustaining infections necessitates a thorough understanding of the corresponding mechanisms. Unlike the traditional approach of considering the host or pathogen separately, a systems-level approach, considering the PHI system as a whole is indispensable to elucidate the mechanisms of infection. Following the technological advances in the post-genomic era, PHI data have been produced in large-scale within the last decade. Systems biology-based methods for the inference and analysis of PHI regulatory, metabolic, and protein–protein networks to shed light on infection mechanisms are gaining increasing demand thanks to the availability of omics data. The knowledge derived from the PHIs may largely contribute to the identification of new and more efficient therapeutics to prevent or cure infections. There are recent efforts for the detailed documentation of these experimentally verified PHI data through Web-based databases. Despite these advances in data archiving, there are still large amounts of PHI data in the biomedical literature yet to be discovered, and novel text mining methods are in development to unearth such hidden data. Here, we review a collection of recent studies on computational systems biology of PHIs with a special focus on the methods for the inference and analysis of PHI networks, covering also the Web-based databases and text-mining efforts to unravel the data hidden in the literature. PMID:25914674
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Predicting structured metadata from unstructured metadata
Posch, Lisa; Panahiazar, Maryam; Dumontier, Michel; Gevaert, Olivier
2016-01-01
Enormous amounts of biomedical data have been and are being produced by investigators all over the world. However, one crucial and limiting factor in data reuse is accurate, structured and complete description of the data or data about the data—defined as metadata. We propose a framework to predict structured metadata terms from unstructured metadata for improving quality and quantity of metadata, using the Gene Expression Omnibus (GEO) microarray database. Our framework consists of classifiers trained using term frequency-inverse document frequency (TF-IDF) features and a second approach based on topics modeled using a Latent Dirichlet Allocation model (LDA) to reduce the dimensionality of the unstructured data. Our results on the GEO database show that structured metadata terms can be the most accurately predicted using the TF-IDF approach followed by LDA both outperforming the majority vote baseline. While some accuracy is lost by the dimensionality reduction of LDA, the difference is small for elements with few possible values, and there is a large improvement over the majority classifier baseline. Overall this is a promising approach for metadata prediction that is likely to be applicable to other datasets and has implications for researchers interested in biomedical metadata curation and metadata prediction. Database URL: http://www.yeastgenome.org/ PMID:28637268
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Ensemble Deep Learning for Biomedical Time Series Classification
2016-01-01
Ensemble learning has been proved to improve the generalization ability effectively in both theory and practice. In this paper, we briefly outline the current status of research on it first. Then, a new deep neural network-based ensemble method that integrates filtering views, local views, distorted views, explicit training, implicit training, subview prediction, and Simple Average is proposed for biomedical time series classification. Finally, we validate its effectiveness on the Chinese Cardiovascular Disease Database containing a large number of electrocardiogram recordings. The experimental results show that the proposed method has certain advantages compared to some well-known ensemble methods, such as Bagging and AdaBoost. PMID:27725828
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Trapp, Jamie
2016-12-01
There are often differences in a publication's citation count, depending on the database accessed. Here, aspects of citation counts for medical physics and biomedical engineering papers are studied using papers published in the journal Australasian physical and engineering sciences in medicine. Comparison is made between the Web of Science, Scopus, and Google Scholar. Papers are categorised into subject matter, and citation trends are examined. It is shown that review papers as a group tend to receive more citations on average; however the highest cited individual papers are more likely to be research papers.
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Open-source tools for data mining.
Zupan, Blaz; Demsar, Janez
2008-03-01
With a growing volume of biomedical databases and repositories, the need to develop a set of tools to address their analysis and support knowledge discovery is becoming acute. The data mining community has developed a substantial set of techniques for computational treatment of these data. In this article, we discuss the evolution of open-source toolboxes that data mining researchers and enthusiasts have developed over the span of a few decades and review several currently available open-source data mining suites. The approaches we review are diverse in data mining methods and user interfaces and also demonstrate that the field and its tools are ready to be fully exploited in biomedical research.
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Development of an information retrieval tool for biomedical patents.
Alves, Tiago; Rodrigues, Rúben; Costa, Hugo; Rocha, Miguel
2018-06-01
The volume of biomedical literature has been increasing in the last years. Patent documents have also followed this trend, being important sources of biomedical knowledge, technical details and curated data, which are put together along the granting process. The field of Biomedical text mining (BioTM) has been creating solutions for the problems posed by the unstructured nature of natural language, which makes the search of information a challenging task. Several BioTM techniques can be applied to patents. From those, Information Retrieval (IR) includes processes where relevant data are obtained from collections of documents. In this work, the main goal was to build a patent pipeline addressing IR tasks over patent repositories to make these documents amenable to BioTM tasks. The pipeline was developed within @Note2, an open-source computational framework for BioTM, adding a number of modules to the core libraries, including patent metadata and full text retrieval, PDF to text conversion and optical character recognition. Also, user interfaces were developed for the main operations materialized in a new @Note2 plug-in. The integration of these tools in @Note2 opens opportunities to run BioTM tools over patent texts, including tasks from Information Extraction, such as Named Entity Recognition or Relation Extraction. We demonstrated the pipeline's main functions with a case study, using an available benchmark dataset from BioCreative challenges. Also, we show the use of the plug-in with a user query related to the production of vanillin. This work makes available all the relevant content from patents to the scientific community, decreasing drastically the time required for this task, and provides graphical interfaces to ease the use of these tools. Copyright © 2018 Elsevier B.V. All rights reserved.
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Hassanpour, Saeed; O'Connor, Martin J; Das, Amar K
2013-08-12
A variety of informatics approaches have been developed that use information retrieval, NLP and text-mining techniques to identify biomedical concepts and relations within scientific publications or their sentences. These approaches have not typically addressed the challenge of extracting more complex knowledge such as biomedical definitions. In our efforts to facilitate knowledge acquisition of rule-based definitions of autism phenotypes, we have developed a novel semantic-based text-mining approach that can automatically identify such definitions within text. Using an existing knowledge base of 156 autism phenotype definitions and an annotated corpus of 26 source articles containing such definitions, we evaluated and compared the average rank of correctly identified rule definition or corresponding rule template using both our semantic-based approach and a standard term-based approach. We examined three separate scenarios: (1) the snippet of text contained a definition already in the knowledge base; (2) the snippet contained an alternative definition for a concept in the knowledge base; and (3) the snippet contained a definition not in the knowledge base. Our semantic-based approach had a higher average rank than the term-based approach for each of the three scenarios (scenario 1: 3.8 vs. 5.0; scenario 2: 2.8 vs. 4.9; and scenario 3: 4.5 vs. 6.2), with each comparison significant at the p-value of 0.05 using the Wilcoxon signed-rank test. Our work shows that leveraging existing domain knowledge in the information extraction of biomedical definitions significantly improves the correct identification of such knowledge within sentences. Our method can thus help researchers rapidly acquire knowledge about biomedical definitions that are specified and evolving within an ever-growing corpus of scientific publications.
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Bramer, Wichor M; Giustini, Dean; Kramer, Bianca Mr; Anderson, Pf
2013-12-23
The usefulness of Google Scholar (GS) as a bibliographic database for biomedical systematic review (SR) searching is a subject of current interest and debate in research circles. Recent research has suggested GS might even be used alone in SR searching. This assertion is challenged here by testing whether GS can locate all studies included in 21 previously published SRs. Second, it examines the recall of GS, taking into account the maximum number of items that can be viewed, and tests whether more complete searches created by an information specialist will improve recall compared to the searches used in the 21 published SRs. The authors identified 21 biomedical SRs that had used GS and PubMed as information sources and reported their use of identical, reproducible search strategies in both databases. These search strategies were rerun in GS and PubMed, and analyzed as to their coverage and recall. Efforts were made to improve searches that underperformed in each database. GS' overall coverage was higher than PubMed (98% versus 91%) and overall recall is higher in GS: 80% of the references included in the 21 SRs were returned by the original searches in GS versus 68% in PubMed. Only 72% of the included references could be used as they were listed among the first 1,000 hits (the maximum number shown). Practical precision (the number of included references retrieved in the first 1,000, divided by 1,000) was on average 1.9%, which is only slightly lower than in other published SRs. Improving searches with the lowest recall resulted in an increase in recall from 48% to 66% in GS and, in PubMed, from 60% to 85%. Although its coverage and precision are acceptable, GS, because of its incomplete recall, should not be used as a single source in SR searching. A specialized, curated medical database such as PubMed provides experienced searchers with tools and functionality that help improve recall, and numerous options in order to optimize precision. Searches for SRs should be performed by experienced searchers creating searches that maximize recall for as many databases as deemed necessary by the search expert.
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2013-01-01
Background The usefulness of Google Scholar (GS) as a bibliographic database for biomedical systematic review (SR) searching is a subject of current interest and debate in research circles. Recent research has suggested GS might even be used alone in SR searching. This assertion is challenged here by testing whether GS can locate all studies included in 21 previously published SRs. Second, it examines the recall of GS, taking into account the maximum number of items that can be viewed, and tests whether more complete searches created by an information specialist will improve recall compared to the searches used in the 21 published SRs. Methods The authors identified 21 biomedical SRs that had used GS and PubMed as information sources and reported their use of identical, reproducible search strategies in both databases. These search strategies were rerun in GS and PubMed, and analyzed as to their coverage and recall. Efforts were made to improve searches that underperformed in each database. Results GS’ overall coverage was higher than PubMed (98% versus 91%) and overall recall is higher in GS: 80% of the references included in the 21 SRs were returned by the original searches in GS versus 68% in PubMed. Only 72% of the included references could be used as they were listed among the first 1,000 hits (the maximum number shown). Practical precision (the number of included references retrieved in the first 1,000, divided by 1,000) was on average 1.9%, which is only slightly lower than in other published SRs. Improving searches with the lowest recall resulted in an increase in recall from 48% to 66% in GS and, in PubMed, from 60% to 85%. Conclusions Although its coverage and precision are acceptable, GS, because of its incomplete recall, should not be used as a single source in SR searching. A specialized, curated medical database such as PubMed provides experienced searchers with tools and functionality that help improve recall, and numerous options in order to optimize precision. Searches for SRs should be performed by experienced searchers creating searches that maximize recall for as many databases as deemed necessary by the search expert. PMID:24360284
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Airoldi, Edoardo M.; Bai, Xue; Malin, Bradley A.
2011-01-01
We live in an increasingly mobile world, which leads to the duplication of information across domains. Though organizations attempt to obscure the identities of their constituents when sharing information for worthwhile purposes, such as basic research, the uncoordinated nature of such environment can lead to privacy vulnerabilities. For instance, disparate healthcare providers can collect information on the same patient. Federal policy requires that such providers share “de-identified” sensitive data, such as biomedical (e.g., clinical and genomic) records. But at the same time, such providers can share identified information, devoid of sensitive biomedical data, for administrative functions. On a provider-by-provider basis, the biomedical and identified records appear unrelated, however, links can be established when multiple providers’ databases are studied jointly. The problem, known as trail disclosure, is a generalized phenomenon and occurs because an individual’s location access pattern can be matched across the shared databases. Due to technical and legal constraints, it is often difficult to coordinate between providers and thus it is critical to assess the disclosure risk in distributed environments, so that we can develop techniques to mitigate such risks. Research on privacy protection has so far focused on developing technologies to suppress or encrypt identifiers associated with sensitive information. There is growing body of work on the formal assessment of the disclosure risk of database entries in publicly shared databases, but a less attention has been paid to the distributed setting. In this research, we review the trail disclosure problem in several domains with known vulnerabilities and show that disclosure risk is influenced by the distribution of how people visit service providers. Based on empirical evidence, we propose an entropy metric for assessing such risk in shared databases prior to their release. This metric assesses risk by leveraging the statistical characteristics of a visit distribution, as opposed to person-level data. It is computationally efficient and superior to existing risk assessment methods, which rely on ad hoc assessment that are often computationally expensive and unreliable. We evaluate our approach on a range of location access patterns in simulated environments. Our results demonstrate the approach is effective at estimating trail disclosure risks and the amount of self-information contained in a distributed system is one of the main driving factors. PMID:21647242
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Building a glaucoma interaction network using a text mining approach.
Soliman, Maha; Nasraoui, Olfa; Cooper, Nigel G F
2016-01-01
The volume of biomedical literature and its underlying knowledge base is rapidly expanding, making it beyond the ability of a single human being to read through all the literature. Several automated methods have been developed to help make sense of this dilemma. The present study reports on the results of a text mining approach to extract gene interactions from the data warehouse of published experimental results which are then used to benchmark an interaction network associated with glaucoma. To the best of our knowledge, there is, as yet, no glaucoma interaction network derived solely from text mining approaches. The presence of such a network could provide a useful summative knowledge base to complement other forms of clinical information related to this disease. A glaucoma corpus was constructed from PubMed Central and a text mining approach was applied to extract genes and their relations from this corpus. The extracted relations between genes were checked using reference interaction databases and classified generally as known or new relations. The extracted genes and relations were then used to construct a glaucoma interaction network. Analysis of the resulting network indicated that it bears the characteristics of a small world interaction network. Our analysis showed the presence of seven glaucoma linked genes that defined the network modularity. A web-based system for browsing and visualizing the extracted glaucoma related interaction networks is made available at http://neurogene.spd.louisville.edu/GlaucomaINViewer/Form1.aspx. This study has reported the first version of a glaucoma interaction network using a text mining approach. The power of such an approach is in its ability to cover a wide range of glaucoma related studies published over many years. Hence, a bigger picture of the disease can be established. To the best of our knowledge, this is the first glaucoma interaction network to summarize the known literature. The major findings were a set of relations that could not be found in existing interaction databases and that were found to be new, in addition to a smaller subnetwork consisting of interconnected clusters of seven glaucoma genes. Future improvements can be applied towards obtaining a better version of this network.
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View-Based Searching Systems--Progress Towards Effective Disintermediation.
ERIC Educational Resources Information Center
Pollitt, A. Steven; Smith, Martin P.; Treglown, Mark; Braekevelt, Patrick
This paper presents the background and then reports progress made in the development of two view-based searching systems--HIBROWSE for EMBASE, searching Europe's most important biomedical bibliographic database, and HIBROWSE for EPOQUE, improving access to the European Parliament's Online Query System. The HIBROWSE approach to searching promises…
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An open-source framework for large-scale, flexible evaluation of biomedical text mining systems.
Baumgartner, William A; Cohen, K Bretonnel; Hunter, Lawrence
2008-01-29
Improved evaluation methodologies have been identified as a necessary prerequisite to the improvement of text mining theory and practice. This paper presents a publicly available framework that facilitates thorough, structured, and large-scale evaluations of text mining technologies. The extensibility of this framework and its ability to uncover system-wide characteristics by analyzing component parts as well as its usefulness for facilitating third-party application integration are demonstrated through examples in the biomedical domain. Our evaluation framework was assembled using the Unstructured Information Management Architecture. It was used to analyze a set of gene mention identification systems involving 225 combinations of system, evaluation corpus, and correctness measure. Interactions between all three were found to affect the relative rankings of the systems. A second experiment evaluated gene normalization system performance using as input 4,097 combinations of gene mention systems and gene mention system-combining strategies. Gene mention system recall is shown to affect gene normalization system performance much more than does gene mention system precision, and high gene normalization performance is shown to be achievable with remarkably low levels of gene mention system precision. The software presented in this paper demonstrates the potential for novel discovery resulting from the structured evaluation of biomedical language processing systems, as well as the usefulness of such an evaluation framework for promoting collaboration between developers of biomedical language processing technologies. The code base is available as part of the BioNLP UIMA Component Repository on SourceForge.net.
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An open-source framework for large-scale, flexible evaluation of biomedical text mining systems
Baumgartner, William A; Cohen, K Bretonnel; Hunter, Lawrence
2008-01-01
Background Improved evaluation methodologies have been identified as a necessary prerequisite to the improvement of text mining theory and practice. This paper presents a publicly available framework that facilitates thorough, structured, and large-scale evaluations of text mining technologies. The extensibility of this framework and its ability to uncover system-wide characteristics by analyzing component parts as well as its usefulness for facilitating third-party application integration are demonstrated through examples in the biomedical domain. Results Our evaluation framework was assembled using the Unstructured Information Management Architecture. It was used to analyze a set of gene mention identification systems involving 225 combinations of system, evaluation corpus, and correctness measure. Interactions between all three were found to affect the relative rankings of the systems. A second experiment evaluated gene normalization system performance using as input 4,097 combinations of gene mention systems and gene mention system-combining strategies. Gene mention system recall is shown to affect gene normalization system performance much more than does gene mention system precision, and high gene normalization performance is shown to be achievable with remarkably low levels of gene mention system precision. Conclusion The software presented in this paper demonstrates the potential for novel discovery resulting from the structured evaluation of biomedical language processing systems, as well as the usefulness of such an evaluation framework for promoting collaboration between developers of biomedical language processing technologies. The code base is available as part of the BioNLP UIMA Component Repository on SourceForge.net. PMID:18230184
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Application of text mining in the biomedical domain.
Fleuren, Wilco W M; Alkema, Wynand
2015-03-01
In recent years the amount of experimental data that is produced in biomedical research and the number of papers that are being published in this field have grown rapidly. In order to keep up to date with developments in their field of interest and to interpret the outcome of experiments in light of all available literature, researchers turn more and more to the use of automated literature mining. As a consequence, text mining tools have evolved considerably in number and quality and nowadays can be used to address a variety of research questions ranging from de novo drug target discovery to enhanced biological interpretation of the results from high throughput experiments. In this paper we introduce the most important techniques that are used for a text mining and give an overview of the text mining tools that are currently being used and the type of problems they are typically applied for. Copyright © 2015 Elsevier Inc. All rights reserved.
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Augmenting Oracle Text with the UMLS for enhanced searching of free-text medical reports.
Ding, Jing; Erdal, Selnur; Dhaval, Rakesh; Kamal, Jyoti
2007-10-11
The intrinsic complexity of free-text medical reports imposes great challenges for information retrieval systems. We have developed a prototype search engine for retrieving clinical reports that leverages the powerful indexing and querying capabilities of Oracle Text, and the rich biomedical domain knowledge and semantic structures that are captured in the UMLS Metathesaurus.
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Integrating systems biology models and biomedical ontologies
2011-01-01
Background Systems biology is an approach to biology that emphasizes the structure and dynamic behavior of biological systems and the interactions that occur within them. To succeed, systems biology crucially depends on the accessibility and integration of data across domains and levels of granularity. Biomedical ontologies were developed to facilitate such an integration of data and are often used to annotate biosimulation models in systems biology. Results We provide a framework to integrate representations of in silico systems biology with those of in vivo biology as described by biomedical ontologies and demonstrate this framework using the Systems Biology Markup Language. We developed the SBML Harvester software that automatically converts annotated SBML models into OWL and we apply our software to those biosimulation models that are contained in the BioModels Database. We utilize the resulting knowledge base for complex biological queries that can bridge levels of granularity, verify models based on the biological phenomenon they represent and provide a means to establish a basic qualitative layer on which to express the semantics of biosimulation models. Conclusions We establish an information flow between biomedical ontologies and biosimulation models and we demonstrate that the integration of annotated biosimulation models and biomedical ontologies enables the verification of models as well as expressive queries. Establishing a bi-directional information flow between systems biology and biomedical ontologies has the potential to enable large-scale analyses of biological systems that span levels of granularity from molecules to organisms. PMID:21835028
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Understanding PubMed user search behavior through log analysis.
Islamaj Dogan, Rezarta; Murray, G Craig; Névéol, Aurélie; Lu, Zhiyong
2009-01-01
This article reports on a detailed investigation of PubMed users' needs and behavior as a step toward improving biomedical information retrieval. PubMed is providing free service to researchers with access to more than 19 million citations for biomedical articles from MEDLINE and life science journals. It is accessed by millions of users each day. Efficient search tools are crucial for biomedical researchers to keep abreast of the biomedical literature relating to their own research. This study provides insight into PubMed users' needs and their behavior. This investigation was conducted through the analysis of one month of log data, consisting of more than 23 million user sessions and more than 58 million user queries. Multiple aspects of users' interactions with PubMed are characterized in detail with evidence from these logs. Despite having many features in common with general Web searches, biomedical information searches have unique characteristics that are made evident in this study. PubMed users are more persistent in seeking information and they reformulate queries often. The three most frequent types of search are search by author name, search by gene/protein, and search by disease. Use of abbreviation in queries is very frequent. Factors such as result set size influence users' decisions. Analysis of characteristics such as these plays a critical role in identifying users' information needs and their search habits. In turn, such an analysis also provides useful insight for improving biomedical information retrieval.Database URL:http://www.ncbi.nlm.nih.gov/PubMed.
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Dialynas, Emmanuel; Topalis, Pantelis; Vontas, John; Louis, Christos
2009-01-01
Background Monitoring of insect vector populations with respect to their susceptibility to one or more insecticides is a crucial element of the strategies used for the control of arthropod-borne diseases. This management task can nowadays be achieved more efficiently when assisted by IT (Information Technology) tools, ranging from modern integrated databases to GIS (Geographic Information System). Here we describe an application ontology that we developed de novo, and a specially designed database that, based on this ontology, can be used for the purpose of controlling mosquitoes and, thus, the diseases that they transmit. Methodology/Principal Findings The ontology, named MIRO for Mosquito Insecticide Resistance Ontology, developed using the OBO-Edit software, describes all pertinent aspects of insecticide resistance, including specific methodology and mode of action. MIRO, then, forms the basis for the design and development of a dedicated database, IRbase, constructed using open source software, which can be used to retrieve data on mosquito populations in a temporally and spatially separate way, as well as to map the output using a Google Earth interface. The dependency of the database on the MIRO allows for a rational and efficient hierarchical search possibility. Conclusions/Significance The fact that the MIRO complies with the rules set forward by the OBO (Open Biomedical Ontologies) Foundry introduces cross-referencing with other biomedical ontologies and, thus, both MIRO and IRbase are suitable as parts of future comprehensive surveillance tools and decision support systems that will be used for the control of vector-borne diseases. MIRO is downloadable from and IRbase is accessible at VectorBase, the NIAID-sponsored open access database for arthropod vectors of disease. PMID:19547750
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Carson, Andrea
2018-07-01
The birth control pill is one of the most popular forms of contraception in North America and has been a key player in women's rights activism for over 50 years. In this paper, I conduct a feminist deconstructive analysis of 12 biomedical texts on the birth control pill, published between 1965 and 2016. This study is situated amongst the feminist scholarship that challenges the representation of women's bodies in biomedicine. Findings suggest that clinical texts on the birth control pill continue to universalise women's lives and experiences, and essentialise them based on their reproductive capacities. One way the texts accomplish this is by making women absent or passive in the literature thereby losing concern for the diversity of their lives, interpretations and identities as more than reproductive beings. The consequence of such representations is that biomedical texts disseminate limited forms of knowledge, in particular concerning definitions of 'natural' and 'normal' behaviour, with important consequences for the embodied experiences of women.
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Empirical Distributional Semantics: Methods and Biomedical Applications
Cohen, Trevor; Widdows, Dominic
2009-01-01
Over the past fifteen years, a range of methods have been developed that are able to learn human-like estimates of the semantic relatedness between terms from the way in which these terms are distributed in a corpus of unannotated natural language text. These methods have also been evaluated in a number of applications in the cognitive science, computational linguistics and the information retrieval literatures. In this paper, we review the available methodologies for derivation of semantic relatedness from free text, as well as their evaluation in a variety of biomedical and other applications. Recent methodological developments, and their applicability to several existing applications are also discussed. PMID:19232399
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Chinese journals: a guide for epidemiologists
Fung, Isaac CH
2008-01-01
Chinese journals in epidemiology, preventive medicine and public health contain much that is of potential international interest. However, few non-Chinese speakers are acquainted with this literature. This article therefore provides an overview of the contemporary scene in Chinese biomedical journal publication, Chinese bibliographic databases and Chinese journals in epidemiology, preventive medicine and public health. The challenge of switching to English as the medium of publication, the development of publishing bibliometric data from Chinese databases, the prospect of an Open Access publication model in China, the issue of language bias in literature reviews and the quality of Chinese journals are discussed. Epidemiologists are encouraged to search the Chinese bibliographic databases for Chinese journal articles. PMID:18826604
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A crowdsourcing workflow for extracting chemical-induced disease relations from free text
Li, Tong Shu; Bravo, Àlex; Furlong, Laura I.; Good, Benjamin M.; Su, Andrew I.
2016-01-01
Relations between chemicals and diseases are one of the most queried biomedical interactions. Although expert manual curation is the standard method for extracting these relations from the literature, it is expensive and impractical to apply to large numbers of documents, and therefore alternative methods are required. We describe here a crowdsourcing workflow for extracting chemical-induced disease relations from free text as part of the BioCreative V Chemical Disease Relation challenge. Five non-expert workers on the CrowdFlower platform were shown each potential chemical-induced disease relation highlighted in the original source text and asked to make binary judgments about whether the text supported the relation. Worker responses were aggregated through voting, and relations receiving four or more votes were predicted as true. On the official evaluation dataset of 500 PubMed abstracts, the crowd attained a 0.505 F-score (0.475 precision, 0.540 recall), with a maximum theoretical recall of 0.751 due to errors with named entity recognition. The total crowdsourcing cost was $1290.67 ($2.58 per abstract) and took a total of 7 h. A qualitative error analysis revealed that 46.66% of sampled errors were due to task limitations and gold standard errors, indicating that performance can still be improved. All code and results are publicly available at https://github.com/SuLab/crowd_cid_relex Database URL: https://github.com/SuLab/crowd_cid_relex PMID:27087308
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Using uncertainty to link and rank evidence from biomedical literature for model curation
Zerva, Chrysoula; Batista-Navarro, Riza; Day, Philip; Ananiadou, Sophia
2017-01-01
Abstract Motivation In recent years, there has been great progress in the field of automated curation of biomedical networks and models, aided by text mining methods that provide evidence from literature. Such methods must not only extract snippets of text that relate to model interactions, but also be able to contextualize the evidence and provide additional confidence scores for the interaction in question. Although various approaches calculating confidence scores have focused primarily on the quality of the extracted information, there has been little work on exploring the textual uncertainty conveyed by the author. Despite textual uncertainty being acknowledged in biomedical text mining as an attribute of text mined interactions (events), it is significantly understudied as a means of providing a confidence measure for interactions in pathways or other biomedical models. In this work, we focus on improving identification of textual uncertainty for events and explore how it can be used as an additional measure of confidence for biomedical models. Results We present a novel method for extracting uncertainty from the literature using a hybrid approach that combines rule induction and machine learning. Variations of this hybrid approach are then discussed, alongside their advantages and disadvantages. We use subjective logic theory to combine multiple uncertainty values extracted from different sources for the same interaction. Our approach achieves F-scores of 0.76 and 0.88 based on the BioNLP-ST and Genia-MK corpora, respectively, making considerable improvements over previously published work. Moreover, we evaluate our proposed system on pathways related to two different areas, namely leukemia and melanoma cancer research. Availability and implementation The leukemia pathway model used is available in Pathway Studio while the Ras model is available via PathwayCommons. Online demonstration of the uncertainty extraction system is available for research purposes at http://argo.nactem.ac.uk/test. The related code is available on https://github.com/c-zrv/uncertainty_components.git. Details on the above are available in the Supplementary Material. Contact sophia.ananiadou@manchester.ac.uk Supplementary information Supplementary data are available at Bioinformatics online. PMID:29036627
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Using uncertainty to link and rank evidence from biomedical literature for model curation.
Zerva, Chrysoula; Batista-Navarro, Riza; Day, Philip; Ananiadou, Sophia
2017-12-01
In recent years, there has been great progress in the field of automated curation of biomedical networks and models, aided by text mining methods that provide evidence from literature. Such methods must not only extract snippets of text that relate to model interactions, but also be able to contextualize the evidence and provide additional confidence scores for the interaction in question. Although various approaches calculating confidence scores have focused primarily on the quality of the extracted information, there has been little work on exploring the textual uncertainty conveyed by the author. Despite textual uncertainty being acknowledged in biomedical text mining as an attribute of text mined interactions (events), it is significantly understudied as a means of providing a confidence measure for interactions in pathways or other biomedical models. In this work, we focus on improving identification of textual uncertainty for events and explore how it can be used as an additional measure of confidence for biomedical models. We present a novel method for extracting uncertainty from the literature using a hybrid approach that combines rule induction and machine learning. Variations of this hybrid approach are then discussed, alongside their advantages and disadvantages. We use subjective logic theory to combine multiple uncertainty values extracted from different sources for the same interaction. Our approach achieves F-scores of 0.76 and 0.88 based on the BioNLP-ST and Genia-MK corpora, respectively, making considerable improvements over previously published work. Moreover, we evaluate our proposed system on pathways related to two different areas, namely leukemia and melanoma cancer research. The leukemia pathway model used is available in Pathway Studio while the Ras model is available via PathwayCommons. Online demonstration of the uncertainty extraction system is available for research purposes at http://argo.nactem.ac.uk/test. The related code is available on https://github.com/c-zrv/uncertainty_components.git. Details on the above are available in the Supplementary Material. sophia.ananiadou@manchester.ac.uk. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press.
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Bruce, Rachel; Chauvin, Anthony; Trinquart, Ludovic; Ravaud, Philippe; Boutron, Isabelle
2016-06-10
The peer review process is a cornerstone of biomedical research. We aimed to evaluate the impact of interventions to improve the quality of peer review for biomedical publications. We performed a systematic review and meta-analysis. We searched CENTRAL, MEDLINE (PubMed), Embase, Cochrane Database of Systematic Reviews, and WHO ICTRP databases, for all randomized controlled trials (RCTs) evaluating the impact of interventions to improve the quality of peer review for biomedical publications. We selected 22 reports of randomized controlled trials, for 25 comparisons evaluating training interventions (n = 5), the addition of a statistical peer reviewer (n = 2), use of a checklist (n = 2), open peer review (i.e., peer reviewers informed that their identity would be revealed; n = 7), blinded peer review (i.e., peer reviewers blinded to author names and affiliation; n = 6) and other interventions to increase the speed of the peer review process (n = 3). Results from only seven RCTs were published since 2004. As compared with the standard peer review process, training did not improve the quality of the peer review report and use of a checklist did not improve the quality of the final manuscript. Adding a statistical peer review improved the quality of the final manuscript (standardized mean difference (SMD), 0.58; 95 % CI, 0.19 to 0.98). Open peer review improved the quality of the peer review report (SMD, 0.14; 95 % CI, 0.05 to 0.24), did not affect the time peer reviewers spent on the peer review (mean difference, 0.18; 95 % CI, -0.06 to 0.43), and decreased the rate of rejection (odds ratio, 0.56; 95 % CI, 0.33 to 0.94). Blinded peer review did not affect the quality of the peer review report or rejection rate. Interventions to increase the speed of the peer review process were too heterogeneous to allow for pooling the results. Despite the essential role of peer review, only a few interventions have been assessed in randomized controlled trials. Evidence-based peer review needs to be developed in biomedical journals.
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A multilingual gold-standard corpus for biomedical concept recognition: the Mantra GSC.
Kors, Jan A; Clematide, Simon; Akhondi, Saber A; van Mulligen, Erik M; Rebholz-Schuhmann, Dietrich
2015-09-01
To create a multilingual gold-standard corpus for biomedical concept recognition. We selected text units from different parallel corpora (Medline abstract titles, drug labels, biomedical patent claims) in English, French, German, Spanish, and Dutch. Three annotators per language independently annotated the biomedical concepts, based on a subset of the Unified Medical Language System and covering a wide range of semantic groups. To reduce the annotation workload, automatically generated preannotations were provided. Individual annotations were automatically harmonized and then adjudicated, and cross-language consistency checks were carried out to arrive at the final annotations. The number of final annotations was 5530. Inter-annotator agreement scores indicate good agreement (median F-score 0.79), and are similar to those between individual annotators and the gold standard. The automatically generated harmonized annotation set for each language performed equally well as the best annotator for that language. The use of automatic preannotations, harmonized annotations, and parallel corpora helped to keep the manual annotation efforts manageable. The inter-annotator agreement scores provide a reference standard for gauging the performance of automatic annotation techniques. To our knowledge, this is the first gold-standard corpus for biomedical concept recognition in languages other than English. Other distinguishing features are the wide variety of semantic groups that are being covered, and the diversity of text genres that were annotated. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.
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Beyond accuracy: creating interoperable and scalable text-mining web services.
Wei, Chih-Hsuan; Leaman, Robert; Lu, Zhiyong
2016-06-15
The biomedical literature is a knowledge-rich resource and an important foundation for future research. With over 24 million articles in PubMed and an increasing growth rate, research in automated text processing is becoming increasingly important. We report here our recently developed web-based text mining services for biomedical concept recognition and normalization. Unlike most text-mining software tools, our web services integrate several state-of-the-art entity tagging systems (DNorm, GNormPlus, SR4GN, tmChem and tmVar) and offer a batch-processing mode able to process arbitrary text input (e.g. scholarly publications, patents and medical records) in multiple formats (e.g. BioC). We support multiple standards to make our service interoperable and allow simpler integration with other text-processing pipelines. To maximize scalability, we have preprocessed all PubMed articles, and use a computer cluster for processing large requests of arbitrary text. Our text-mining web service is freely available at http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/#curl : Zhiyong.Lu@nih.gov. Published by Oxford University Press 2016. This work is written by US Government employees and is in the public domain in the US.
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A bioinformatics knowledge discovery in text application for grid computing
Castellano, Marcello; Mastronardi, Giuseppe; Bellotti, Roberto; Tarricone, Gianfranco
2009-01-01
Background A fundamental activity in biomedical research is Knowledge Discovery which has the ability to search through large amounts of biomedical information such as documents and data. High performance computational infrastructures, such as Grid technologies, are emerging as a possible infrastructure to tackle the intensive use of Information and Communication resources in life science. The goal of this work was to develop a software middleware solution in order to exploit the many knowledge discovery applications on scalable and distributed computing systems to achieve intensive use of ICT resources. Methods The development of a grid application for Knowledge Discovery in Text using a middleware solution based methodology is presented. The system must be able to: perform a user application model, process the jobs with the aim of creating many parallel jobs to distribute on the computational nodes. Finally, the system must be aware of the computational resources available, their status and must be able to monitor the execution of parallel jobs. These operative requirements lead to design a middleware to be specialized using user application modules. It included a graphical user interface in order to access to a node search system, a load balancing system and a transfer optimizer to reduce communication costs. Results A middleware solution prototype and the performance evaluation of it in terms of the speed-up factor is shown. It was written in JAVA on Globus Toolkit 4 to build the grid infrastructure based on GNU/Linux computer grid nodes. A test was carried out and the results are shown for the named entity recognition search of symptoms and pathologies. The search was applied to a collection of 5,000 scientific documents taken from PubMed. Conclusion In this paper we discuss the development of a grid application based on a middleware solution. It has been tested on a knowledge discovery in text process to extract new and useful information about symptoms and pathologies from a large collection of unstructured scientific documents. As an example a computation of Knowledge Discovery in Database was applied on the output produced by the KDT user module to extract new knowledge about symptom and pathology bio-entities. PMID:19534749
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A bioinformatics knowledge discovery in text application for grid computing.
Castellano, Marcello; Mastronardi, Giuseppe; Bellotti, Roberto; Tarricone, Gianfranco
2009-06-16
A fundamental activity in biomedical research is Knowledge Discovery which has the ability to search through large amounts of biomedical information such as documents and data. High performance computational infrastructures, such as Grid technologies, are emerging as a possible infrastructure to tackle the intensive use of Information and Communication resources in life science. The goal of this work was to develop a software middleware solution in order to exploit the many knowledge discovery applications on scalable and distributed computing systems to achieve intensive use of ICT resources. The development of a grid application for Knowledge Discovery in Text using a middleware solution based methodology is presented. The system must be able to: perform a user application model, process the jobs with the aim of creating many parallel jobs to distribute on the computational nodes. Finally, the system must be aware of the computational resources available, their status and must be able to monitor the execution of parallel jobs. These operative requirements lead to design a middleware to be specialized using user application modules. It included a graphical user interface in order to access to a node search system, a load balancing system and a transfer optimizer to reduce communication costs. A middleware solution prototype and the performance evaluation of it in terms of the speed-up factor is shown. It was written in JAVA on Globus Toolkit 4 to build the grid infrastructure based on GNU/Linux computer grid nodes. A test was carried out and the results are shown for the named entity recognition search of symptoms and pathologies. The search was applied to a collection of 5,000 scientific documents taken from PubMed. In this paper we discuss the development of a grid application based on a middleware solution. It has been tested on a knowledge discovery in text process to extract new and useful information about symptoms and pathologies from a large collection of unstructured scientific documents. As an example a computation of Knowledge Discovery in Database was applied on the output produced by the KDT user module to extract new knowledge about symptom and pathology bio-entities.
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AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images.
Albarqouni, Shadi; Baur, Christoph; Achilles, Felix; Belagiannis, Vasileios; Demirci, Stefanie; Navab, Nassir
2016-05-01
The lack of publicly available ground-truth data has been identified as the major challenge for transferring recent developments in deep learning to the biomedical imaging domain. Though crowdsourcing has enabled annotation of large scale databases for real world images, its application for biomedical purposes requires a deeper understanding and hence, more precise definition of the actual annotation task. The fact that expert tasks are being outsourced to non-expert users may lead to noisy annotations introducing disagreement between users. Despite being a valuable resource for learning annotation models from crowdsourcing, conventional machine-learning methods may have difficulties dealing with noisy annotations during training. In this manuscript, we present a new concept for learning from crowds that handle data aggregation directly as part of the learning process of the convolutional neural network (CNN) via additional crowdsourcing layer (AggNet). Besides, we present an experimental study on learning from crowds designed to answer the following questions. 1) Can deep CNN be trained with data collected from crowdsourcing? 2) How to adapt the CNN to train on multiple types of annotation datasets (ground truth and crowd-based)? 3) How does the choice of annotation and aggregation affect the accuracy? Our experimental setup involved Annot8, a self-implemented web-platform based on Crowdflower API realizing image annotation tasks for a publicly available biomedical image database. Our results give valuable insights into the functionality of deep CNN learning from crowd annotations and prove the necessity of data aggregation integration.
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Botsis, Taxiarchis; Foster, Matthew; Kreimeyer, Kory; Pandey, Abhishek; Forshee, Richard
2017-01-01
Literature review is critical but time-consuming in the post-market surveillance of medical products. We focused on the safety signal of intussusception after the vaccination of infants with the Rotashield Vaccine in 1999 and retrieved all PubMed abstracts for rotavirus vaccines published after January 1, 1998. We used the Event-based Text-mining of Health Electronic Records system, the MetaMap tool, and the National Center for Biomedical Ontologies Annotator to process the abstracts and generate coded terms stamped with the date of publication. Data were analyzed in the Pattern-based and Advanced Network Analyzer for Clinical Evaluation and Assessment to evaluate the intussusception-related findings before and after the release of the new rotavirus vaccines in 2006. The tight connection of intussusception with the historical signal in the first period and the absence of any safety concern for the new vaccines in the second period were verified. We demonstrated the feasibility for semi-automated solutions that may assist medical reviewers in monitoring biomedical literature.
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BANNER: an executable survey of advances in biomedical named entity recognition.
Leaman, Robert; Gonzalez, Graciela
2008-01-01
There has been an increasing amount of research on biomedical named entity recognition, the most basic text extraction problem, resulting in significant progress by different research teams around the world. This has created a need for a freely-available, open source system implementing the advances described in the literature. In this paper we present BANNER, an open-source, executable survey of advances in biomedical named entity recognition, intended to serve as a benchmark for the field. BANNER is implemented in Java as a machine-learning system based on conditional random fields and includes a wide survey of the best techniques recently described in the literature. It is designed to maximize domain independence by not employing brittle semantic features or rule-based processing steps, and achieves significantly better performance than existing baseline systems. It is therefore useful to developers as an extensible NER implementation, to researchers as a standard for comparing innovative techniques, and to biologists requiring the ability to find novel entities in large amounts of text.
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Community challenges in biomedical text mining over 10 years: success, failure and the future
Huang, Chung-Chi
2016-01-01
One effective way to improve the state of the art is through competitions. Following the success of the Critical Assessment of protein Structure Prediction (CASP) in bioinformatics research, a number of challenge evaluations have been organized by the text-mining research community to assess and advance natural language processing (NLP) research for biomedicine. In this article, we review the different community challenge evaluations held from 2002 to 2014 and their respective tasks. Furthermore, we examine these challenge tasks through their targeted problems in NLP research and biomedical applications, respectively. Next, we describe the general workflow of organizing a Biomedical NLP (BioNLP) challenge and involved stakeholders (task organizers, task data producers, task participants and end users). Finally, we summarize the impact and contributions by taking into account different BioNLP challenges as a whole, followed by a discussion of their limitations and difficulties. We conclude with future trends in BioNLP challenge evaluations. PMID:25935162
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Jácome, Alberto G; Fdez-Riverola, Florentino; Lourenço, Anália
2016-07-01
Text mining and semantic analysis approaches can be applied to the construction of biomedical domain-specific search engines and provide an attractive alternative to create personalized and enhanced search experiences. Therefore, this work introduces the new open-source BIOMedical Search Engine Framework for the fast and lightweight development of domain-specific search engines. The rationale behind this framework is to incorporate core features typically available in search engine frameworks with flexible and extensible technologies to retrieve biomedical documents, annotate meaningful domain concepts, and develop highly customized Web search interfaces. The BIOMedical Search Engine Framework integrates taggers for major biomedical concepts, such as diseases, drugs, genes, proteins, compounds and organisms, and enables the use of domain-specific controlled vocabulary. Technologies from the Typesafe Reactive Platform, the AngularJS JavaScript framework and the Bootstrap HTML/CSS framework support the customization of the domain-oriented search application. Moreover, the RESTful API of the BIOMedical Search Engine Framework allows the integration of the search engine into existing systems or a complete web interface personalization. The construction of the Smart Drug Search is described as proof-of-concept of the BIOMedical Search Engine Framework. This public search engine catalogs scientific literature about antimicrobial resistance, microbial virulence and topics alike. The keyword-based queries of the users are transformed into concepts and search results are presented and ranked accordingly. The semantic graph view portraits all the concepts found in the results, and the researcher may look into the relevance of different concepts, the strength of direct relations, and non-trivial, indirect relations. The number of occurrences of the concept shows its importance to the query, and the frequency of concept co-occurrence is indicative of biological relations meaningful to that particular scope of research. Conversely, indirect concept associations, i.e. concepts related by other intermediary concepts, can be useful to integrate information from different studies and look into non-trivial relations. The BIOMedical Search Engine Framework supports the development of domain-specific search engines. The key strengths of the framework are modularity and extensibilityin terms of software design, the use of open-source consolidated Web technologies, and the ability to integrate any number of biomedical text mining tools and information resources. Currently, the Smart Drug Search keeps over 1,186,000 documents, containing more than 11,854,000 annotations for 77,200 different concepts. The Smart Drug Search is publicly accessible at http://sing.ei.uvigo.es/sds/. The BIOMedical Search Engine Framework is freely available for non-commercial use at https://github.com/agjacome/biomsef. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
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Impact of Overt and Subclinical Hypothyroidism on Exercise Tolerance: A Systematic Review
ERIC Educational Resources Information Center
Lankhaar, Jeannette A. C.; de Vries, Wouter R.; Jansen, Jaap A. C. G.; Zelissen, Pierre M. J.; Backx, Frank J. G.
2014-01-01
Purpose: This systematic review describes the state of the art of the impact of hypothyroidism on exercise tolerance and physical performance capacity in untreated and treated patients with hypothyroidism. Method: A systematic computer-aided search was conducted using biomedical databases. Relevant studies in English, German, and Dutch, published…
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The Reorganization of Basic Science Departments in U.S. Medical Schools, 1980-1999.
ERIC Educational Resources Information Center
Mallon, William T.; Biebuyck, Julien F.; Jones, Robert F.
2003-01-01
Constructed a longitudinal database to examine how basic science departments have been reorganized at U.S. medical schools. Found that there were fewer basic science departments in the traditional disciplines of anatomy, biochemistry, microbiology, pharmacology, and physiology in 1999 than in 1980. But as biomedical science has developed in an…
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Director of Duke Institute Wants To Make Medicine More of a Science.
ERIC Educational Resources Information Center
Wheeler, David L.
1998-01-01
The director of the Duke Clinical Research Institute (North Carolina) is committed to making better use of patient information for medical research, and is building a database from the institute's clinical trials. His approach is to provide biomedical researchers with daily involvement in medicine rather than management. (MSE)
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Experiences in supporting the structured collection of cancer nanotechnology data using caNanoLab
Gaheen, Sharon; Lijowski, Michal; Heiskanen, Mervi; Klemm, Juli
2015-01-01
Summary The cancer Nanotechnology Laboratory (caNanoLab) data portal is an online nanomaterial database that allows users to submit and retrieve information on well-characterized nanomaterials, including composition, in vitro and in vivo experimental characterizations, experimental protocols, and related publications. Initiated in 2006, caNanoLab serves as an established resource with an infrastructure supporting the structured collection of nanotechnology data to address the needs of the cancer biomedical and nanotechnology communities. The portal contains over 1,000 curated nanomaterial data records that are publicly accessible for review, comparison, and re-use, with the ultimate goal of accelerating the translation of nanotechnology-based cancer therapeutics, diagnostics, and imaging agents to the clinic. In this paper, we will discuss challenges associated with developing a nanomaterial database and recognized needs for nanotechnology data curation and sharing in the biomedical research community. We will also describe the latest version of caNanoLab, caNanoLab 2.0, which includes enhancements and new features to improve usability such as personalized views of data and enhanced search and navigation. PMID:26425409
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CELLPEDIA: a repository for human cell information for cell studies and differentiation analyses.
Hatano, Akiko; Chiba, Hirokazu; Moesa, Harry Amri; Taniguchi, Takeaki; Nagaie, Satoshi; Yamanegi, Koji; Takai-Igarashi, Takako; Tanaka, Hiroshi; Fujibuchi, Wataru
2011-01-01
CELLPEDIA is a repository database for current knowledge about human cells. It contains various types of information, such as cell morphologies, gene expression and literature references. The major role of CELLPEDIA is to provide a digital dictionary of human cells for the biomedical field, including support for the characterization of artificially generated cells in regenerative medicine. CELLPEDIA features (i) its own cell classification scheme, in which whole human cells are classified by their physical locations in addition to conventional taxonomy; and (ii) cell differentiation pathways compiled from biomedical textbooks and journal papers. Currently, human differentiated cells and stem cells are classified into 2260 and 66 cell taxonomy keys, respectively, from which 934 parent-child relationships reported in cell differentiation or transdifferentiation pathways are retrievable. As far as we know, this is the first attempt to develop a digital cell bank to function as a public resource for the accumulation of current knowledge about human cells. The CELLPEDIA homepage is freely accessible except for the data submission pages that require authentication (please send a password request to cell-info@cbrc.jp). Database URL: http://cellpedia.cbrc.jp/
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Whetzel, Patricia L.; Grethe, Jeffrey S.; Banks, Davis E.; Martone, Maryann E.
2015-01-01
The NIDDK Information Network (dkNET; http://dknet.org) was launched to serve the needs of basic and clinical investigators in metabolic, digestive and kidney disease by facilitating access to research resources that advance the mission of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). By research resources, we mean the multitude of data, software tools, materials, services, projects and organizations available to researchers in the public domain. Most of these are accessed via web-accessible databases or web portals, each developed, designed and maintained by numerous different projects, organizations and individuals. While many of the large government funded databases, maintained by agencies such as European Bioinformatics Institute and the National Center for Biotechnology Information, are well known to researchers, many more that have been developed by and for the biomedical research community are unknown or underutilized. At least part of the problem is the nature of dynamic databases, which are considered part of the “hidden” web, that is, content that is not easily accessed by search engines. dkNET was created specifically to address the challenge of connecting researchers to research resources via these types of community databases and web portals. dkNET functions as a “search engine for data”, searching across millions of database records contained in hundreds of biomedical databases developed and maintained by independent projects around the world. A primary focus of dkNET are centers and projects specifically created to provide high quality data and resources to NIDDK researchers. Through the novel data ingest process used in dkNET, additional data sources can easily be incorporated, allowing it to scale with the growth of digital data and the needs of the dkNET community. Here, we provide an overview of the dkNET portal and its functions. We show how dkNET can be used to address a variety of use cases that involve searching for research resources. PMID:26393351
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Kwon, Yoojin; Powelson, Susan E; Wong, Holly; Ghali, William A; Conly, John M
2014-11-11
The purpose of our study is to determine the value and efficacy of searching biomedical databases beyond MEDLINE for systematic reviews. We analyzed the results from a systematic review conducted by the authors and others on ward closure as an infection control practice. Ovid MEDLINE including In-Process & Other Non-Indexed Citations, Ovid Embase, CINAHL Plus, LILACS, and IndMED were systematically searched for articles of any study type discussing ward closure, as were bibliographies of selected articles and recent infection control conference abstracts. Search results were tracked, recorded, and analyzed using a relative recall method. The sensitivity of searching in each database was calculated. Two thousand ninety-five unique citations were identified and screened for inclusion in the systematic review: 2,060 from database searching and 35 from hand searching and other sources. Ninety-seven citations were included in the final review. MEDLINE and Embase searches each retrieved 80 of the 97 articles included, only 4 articles from each database were unique. The CINAHL search retrieved 35 included articles, and 4 were unique. The IndMED and LILACS searches did not retrieve any included articles, although 75 of the included articles were indexed in LILACS. The true value of using regional databases, particularly LILACS, may lie with the ability to search in the language spoken in the region. Eight articles were found only through hand searching. Identifying studies for a systematic review where the research is observational is complex. The value each individual study contributes to the review cannot be accurately measured. Consequently, we could not determine the value of results found from searching beyond MEDLINE, Embase, and CINAHL with accuracy. However, hand searching for serendipitous retrieval remains an important aspect due to indexing and keyword challenges inherent in this literature.
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Research in Biomaterials and Tissue Engineering: Achievements and perspectives.
Ventre, Maurizio; Causa, Filippo; Netti, Paolo A; Pietrabissa, Riccardo
2015-01-01
Research on biomaterials and related subjects has been active in Italy. Starting from the very first examples of biomaterials and biomedical devices, Italian researchers have always provided valuable scientific contributions. This trend has steadily increased. To provide a rough estimate of this, it is sufficient to search PubMed, a free search engine accessing primarily the MEDLINE database of references and abstracts on life sciences and biomedical topics, with the keywords "biomaterials" or "tissue engineering" and sort the results by affiliation. Again, even though this is a crude estimate, the results speak for themselves, as Italy is the third European country, in terms of publications, with an astonishing 3,700 products in the last decade.
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A pilot biomedical engineering course in rapid prototyping for mobile health.
Stokes, Todd H; Venugopalan, Janani; Hubbard, Elena N; Wang, May D
2013-01-01
Rapid prototyping of medically assistive mobile devices promises to fuel innovation and provides opportunity for hands-on engineering training in biomedical engineering curricula. This paper presents the design and outcomes of a course offered during a 16-week semester in Fall 2011 with 11 students enrolled. The syllabus covered a mobile health design process from end-to-end, including storyboarding, non-functional prototypes, integrated circuit programming, 3D modeling, 3D printing, cloud computing database programming, and developing patient engagement through animated videos describing the benefits of a new device. Most technologies presented in this class are open source and thus provide unlimited "hackability". They are also cost-effective and easily transferrable to other departments.
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BioTextQuest(+): a knowledge integration platform for literature mining and concept discovery.
Papanikolaou, Nikolas; Pavlopoulos, Georgios A; Pafilis, Evangelos; Theodosiou, Theodosios; Schneider, Reinhard; Satagopam, Venkata P; Ouzounis, Christos A; Eliopoulos, Aristides G; Promponas, Vasilis J; Iliopoulos, Ioannis
2014-11-15
The iterative process of finding relevant information in biomedical literature and performing bioinformatics analyses might result in an endless loop for an inexperienced user, considering the exponential growth of scientific corpora and the plethora of tools designed to mine PubMed(®) and related biological databases. Herein, we describe BioTextQuest(+), a web-based interactive knowledge exploration platform with significant advances to its predecessor (BioTextQuest), aiming to bridge processes such as bioentity recognition, functional annotation, document clustering and data integration towards literature mining and concept discovery. BioTextQuest(+) enables PubMed and OMIM querying, retrieval of abstracts related to a targeted request and optimal detection of genes, proteins, molecular functions, pathways and biological processes within the retrieved documents. The front-end interface facilitates the browsing of document clustering per subject, the analysis of term co-occurrence, the generation of tag clouds containing highly represented terms per cluster and at-a-glance popup windows with information about relevant genes and proteins. Moreover, to support experimental research, BioTextQuest(+) addresses integration of its primary functionality with biological repositories and software tools able to deliver further bioinformatics services. The Google-like interface extends beyond simple use by offering a range of advanced parameterization for expert users. We demonstrate the functionality of BioTextQuest(+) through several exemplary research scenarios including author disambiguation, functional term enrichment, knowledge acquisition and concept discovery linking major human diseases, such as obesity and ageing. The service is accessible at http://bioinformatics.med.uoc.gr/biotextquest. g.pavlopoulos@gmail.com or georgios.pavlopoulos@esat.kuleuven.be Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
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Figure mining for biomedical research.
Rodriguez-Esteban, Raul; Iossifov, Ivan
2009-08-15
Figures from biomedical articles contain valuable information difficult to reach without specialized tools. Currently, there is no search engine that can retrieve specific figure types. This study describes a retrieval method that takes advantage of principles in image understanding, text mining and optical character recognition (OCR) to retrieve figure types defined conceptually. A search engine was developed to retrieve tables and figure types to aid computational and experimental research. http://iossifovlab.cshl.edu/figurome/.
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Poor replication validity of biomedical association studies reported by newspapers
Smith, Andy; Boraud, Thomas; Gonon, François
2017-01-01
Objective To investigate the replication validity of biomedical association studies covered by newspapers. Methods We used a database of 4723 primary studies included in 306 meta-analysis articles. These studies associated a risk factor with a disease in three biomedical domains, psychiatry, neurology and four somatic diseases. They were classified into a lifestyle category (e.g. smoking) and a non-lifestyle category (e.g. genetic risk). Using the database Dow Jones Factiva, we investigated the newspaper coverage of each study. Their replication validity was assessed using a comparison with their corresponding meta-analyses. Results Among the 5029 articles of our database, 156 primary studies (of which 63 were lifestyle studies) and 5 meta-analysis articles were reported in 1561 newspaper articles. The percentage of covered studies and the number of newspaper articles per study strongly increased with the impact factor of the journal that published each scientific study. Newspapers almost equally covered initial (5/39 12.8%) and subsequent (58/600 9.7%) lifestyle studies. In contrast, initial non-lifestyle studies were covered more often (48/366 13.1%) than subsequent ones (45/3718 1.2%). Newspapers never covered initial studies reporting null findings and rarely reported subsequent null observations. Only 48.7% of the 156 studies reported by newspapers were confirmed by the corresponding meta-analyses. Initial non-lifestyle studies were less often confirmed (16/48) than subsequent ones (29/45) and than lifestyle studies (31/63). Psychiatric studies covered by newspapers were less often confirmed (10/38) than the neurological (26/41) or somatic (40/77) ones. This is correlated to an even larger coverage of initial studies in psychiatry. Whereas 234 newspaper articles covered the 35 initial studies that were later disconfirmed, only four press articles covered a subsequent null finding and mentioned the refutation of an initial claim. Conclusion Journalists preferentially cover initial findings although they are often contradicted by meta-analyses and rarely inform the public when they are disconfirmed. PMID:28222122
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Poor replication validity of biomedical association studies reported by newspapers.
Dumas-Mallet, Estelle; Smith, Andy; Boraud, Thomas; Gonon, François
2017-01-01
To investigate the replication validity of biomedical association studies covered by newspapers. We used a database of 4723 primary studies included in 306 meta-analysis articles. These studies associated a risk factor with a disease in three biomedical domains, psychiatry, neurology and four somatic diseases. They were classified into a lifestyle category (e.g. smoking) and a non-lifestyle category (e.g. genetic risk). Using the database Dow Jones Factiva, we investigated the newspaper coverage of each study. Their replication validity was assessed using a comparison with their corresponding meta-analyses. Among the 5029 articles of our database, 156 primary studies (of which 63 were lifestyle studies) and 5 meta-analysis articles were reported in 1561 newspaper articles. The percentage of covered studies and the number of newspaper articles per study strongly increased with the impact factor of the journal that published each scientific study. Newspapers almost equally covered initial (5/39 12.8%) and subsequent (58/600 9.7%) lifestyle studies. In contrast, initial non-lifestyle studies were covered more often (48/366 13.1%) than subsequent ones (45/3718 1.2%). Newspapers never covered initial studies reporting null findings and rarely reported subsequent null observations. Only 48.7% of the 156 studies reported by newspapers were confirmed by the corresponding meta-analyses. Initial non-lifestyle studies were less often confirmed (16/48) than subsequent ones (29/45) and than lifestyle studies (31/63). Psychiatric studies covered by newspapers were less often confirmed (10/38) than the neurological (26/41) or somatic (40/77) ones. This is correlated to an even larger coverage of initial studies in psychiatry. Whereas 234 newspaper articles covered the 35 initial studies that were later disconfirmed, only four press articles covered a subsequent null finding and mentioned the refutation of an initial claim. Journalists preferentially cover initial findings although they are often contradicted by meta-analyses and rarely inform the public when they are disconfirmed.
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Hoelzer, Simon; Schweiger, Ralf K; Rieger, Joerg; Meyer, Michael
2006-01-01
The organizational structures of web contents and electronic information resources must adapt to the demands of a growing volume of information and user requirements. Otherwise the information society will be threatened by disinformation. The biomedical sciences are especially vulnerable in this regard, since they are strongly oriented toward text-based knowledge sources. Here sustainable improvement can only be achieved by using a comprehensive, integrated approach that not only includes data management but also specifically incorporates the editorial processes, including structuring information sources and publication. The technical resources needed to effectively master these tasks are already available in the form of the data standards and tools of the Semantic Web. They include Rich Site Summaries (RSS), which have become an established means of distributing and syndicating conventional news messages and blogs. They can also provide access to the contents of the previously mentioned information sources, which are conventionally classified as 'deep web' content.
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TOXMAP: A GIS-Based Gateway to Environmental Health Resources
Hochstein, Colette; Szczur, Marti
2009-01-01
The National Library of Medicine (NLM) has an extensive collection of environmental health information, including bibliographic and technical data on hazardous chemical substances, in its TOXNET
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Wilson, S H; Merkle, S; Brown, D; Moskowitz, J; Hurley, D; Brown, D; Bailey, B J; McClain, M; Misenhimer, M; Buckalew, J; Burks, T
2000-01-01
The National Association of Physicians for the Environment (NAPE) has assumed a leadership role in protecting environmental health in recent years. The Committee of Biomedical Research Leaders was convened at the recent NAPE Leadership Conference: Biomedical Research and the Environment held on 1--2 November 1999, at the National Institutes of Health, Bethesda, Maryland. This report summarizes the discussion of the committee and its recommendations. The charge to the committee was to raise and address issues that will promote and sustain environmental health, safety, and energy efficiency within the biomedical community. Leaders from every important research sector (industry laboratories, academic health centers and institutes, hospitals and care facilities, Federal laboratories, and community-based research facilities) were gathered in this committee to discuss issues relevant to promoting environmental health. The conference and this report focus on the themes of environmental stewardship, sustainable development and "best greening practices." Environmental stewardship, an emerging theme within and outside the biomedical community, symbolizes the effort to provide an integrated, synthesized, and concerted effort to protect the health of the environment in both the present and the future. The primary goal established by the committee is to promote environmentally responsible leadership in the biomedical research community. Key outcomes of the committee's discussion and deliberation were a) the need for a central organization to evaluate, promote, and oversee efforts in environmental stewardship; and b) immediate need to facilitate efficient information transfer relevant to protecting the global environment through a database/clearinghouse. Means to fulfill these needs are discussed in this report. PMID:11121363
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Wilson, S H; Merkle, S; Brown, D; Moskowitz, J; Hurley, D; Brown, D; Bailey, B J; McClain, M; Misenhimer, M; Buckalew, J; Burks, T
2000-12-01
The National Association of Physicians for the Environment (NAPE) has assumed a leadership role in protecting environmental health in recent years. The Committee of Biomedical Research Leaders was convened at the recent NAPE Leadership Conference: Biomedical Research and the Environment held on 1--2 November 1999, at the National Institutes of Health, Bethesda, Maryland. This report summarizes the discussion of the committee and its recommendations. The charge to the committee was to raise and address issues that will promote and sustain environmental health, safety, and energy efficiency within the biomedical community. Leaders from every important research sector (industry laboratories, academic health centers and institutes, hospitals and care facilities, Federal laboratories, and community-based research facilities) were gathered in this committee to discuss issues relevant to promoting environmental health. The conference and this report focus on the themes of environmental stewardship, sustainable development and "best greening practices." Environmental stewardship, an emerging theme within and outside the biomedical community, symbolizes the effort to provide an integrated, synthesized, and concerted effort to protect the health of the environment in both the present and the future. The primary goal established by the committee is to promote environmentally responsible leadership in the biomedical research community. Key outcomes of the committee's discussion and deliberation were a) the need for a central organization to evaluate, promote, and oversee efforts in environmental stewardship; and b) immediate need to facilitate efficient information transfer relevant to protecting the global environment through a database/clearinghouse. Means to fulfill these needs are discussed in this report.
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Figure-associated text summarization and evaluation.
Polepalli Ramesh, Balaji; Sethi, Ricky J; Yu, Hong
2015-01-01
Biomedical literature incorporates millions of figures, which are a rich and important knowledge resource for biomedical researchers. Scientists need access to the figures and the knowledge they represent in order to validate research findings and to generate new hypotheses. By themselves, these figures are nearly always incomprehensible to both humans and machines and their associated texts are therefore essential for full comprehension. The associated text of a figure, however, is scattered throughout its full-text article and contains redundant information content. In this paper, we report the continued development and evaluation of several figure summarization systems, the FigSum+ systems, that automatically identify associated texts, remove redundant information, and generate a text summary for every figure in an article. Using a set of 94 annotated figures selected from 19 different journals, we conducted an intrinsic evaluation of FigSum+. We evaluate the performance by precision, recall, F1, and ROUGE scores. The best FigSum+ system is based on an unsupervised method, achieving F1 score of 0.66 and ROUGE-1 score of 0.97. The annotated data is available at figshare.com (http://figshare.com/articles/Figure_Associated_Text_Summarization_and_Evaluation/858903).
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Figure-Associated Text Summarization and Evaluation
Polepalli Ramesh, Balaji; Sethi, Ricky J.; Yu, Hong
2015-01-01
Biomedical literature incorporates millions of figures, which are a rich and important knowledge resource for biomedical researchers. Scientists need access to the figures and the knowledge they represent in order to validate research findings and to generate new hypotheses. By themselves, these figures are nearly always incomprehensible to both humans and machines and their associated texts are therefore essential for full comprehension. The associated text of a figure, however, is scattered throughout its full-text article and contains redundant information content. In this paper, we report the continued development and evaluation of several figure summarization systems, the FigSum+ systems, that automatically identify associated texts, remove redundant information, and generate a text summary for every figure in an article. Using a set of 94 annotated figures selected from 19 different journals, we conducted an intrinsic evaluation of FigSum+. We evaluate the performance by precision, recall, F1, and ROUGE scores. The best FigSum+ system is based on an unsupervised method, achieving F1 score of 0.66 and ROUGE-1 score of 0.97. The annotated data is available at figshare.com (http://figshare.com/articles/Figure_Associated_Text_Summarization_and_Evaluation/858903). PMID:25643357
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Parsing Citations in Biomedical Articles Using Conditional Random Fields
Zhang, Qing; Cao, Yong-Gang; Yu, Hong
2011-01-01
Citations are used ubiquitously in biomedical full-text articles and play an important role for representing both the rhetorical structure and the semantic content of the articles. As a result, text mining systems will significantly benefit from a tool that automatically extracts the content of a citation. In this study, we applied the supervised machine-learning algorithms Conditional Random Fields (CRFs) to automatically parse a citation into its fields (e.g., Author, Title, Journal, and Year). With a subset of html format open-access PubMed Central articles, we report an overall 97.95% F1-score. The citation parser can be accessed at: http://www.cs.uwm.edu/~qing/projects/cithit/index.html. PMID:21419403
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UCSC genome browser: deep support for molecular biomedical research.
Mangan, Mary E; Williams, Jennifer M; Lathe, Scott M; Karolchik, Donna; Lathe, Warren C
2008-01-01
The volume and complexity of genomic sequence data, and the additional experimental data required for annotation of the genomic context, pose a major challenge for display and access for biomedical researchers. Genome browsers organize this data and make it available in various ways to extract useful information to advance research projects. The UCSC Genome Browser is one of these resources. The official sequence data for a given species forms the framework to display many other types of data such as expression, variation, cross-species comparisons, and more. Visual representations of the data are available for exploration. Data can be queried with sequences. Complex database queries are also easily achieved with the Table Browser interface. Associated tools permit additional query types or access to additional data sources such as images of in situ localizations. Support for solving researcher's issues is provided with active discussion mailing lists and by providing updated training materials. The UCSC Genome Browser provides a source of deep support for a wide range of biomedical molecular research (http://genome.ucsc.edu).
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Yang, Zhihao; Lin, Yuan; Wu, Jiajin; Tang, Nan; Lin, Hongfei; Li, Yanpeng
2011-10-01
Knowledge about protein-protein interactions (PPIs) unveils the molecular mechanisms of biological processes. However, the volume and content of published biomedical literature on protein interactions is expanding rapidly, making it increasingly difficult for interaction database curators to detect and curate protein interaction information manually. We present a multiple kernel learning-based approach for automatic PPI extraction from biomedical literature. The approach combines the following kernels: feature-based, tree, and graph and combines their output with Ranking support vector machine (SVM). Experimental evaluations show that the features in individual kernels are complementary and the kernel combined with Ranking SVM achieves better performance than those of the individual kernels, equal weight combination and optimal weight combination. Our approach can achieve state-of-the-art performance with respect to the comparable evaluations, with 64.88% F-score and 88.02% AUC on the AImed corpus. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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Evaluation of relational and NoSQL database architectures to manage genomic annotations.
Schulz, Wade L; Nelson, Brent G; Felker, Donn K; Durant, Thomas J S; Torres, Richard
2016-12-01
While the adoption of next generation sequencing has rapidly expanded, the informatics infrastructure used to manage the data generated by this technology has not kept pace. Historically, relational databases have provided much of the framework for data storage and retrieval. Newer technologies based on NoSQL architectures may provide significant advantages in storage and query efficiency, thereby reducing the cost of data management. But their relative advantage when applied to biomedical data sets, such as genetic data, has not been characterized. To this end, we compared the storage, indexing, and query efficiency of a common relational database (MySQL), a document-oriented NoSQL database (MongoDB), and a relational database with NoSQL support (PostgreSQL). When used to store genomic annotations from the dbSNP database, we found the NoSQL architectures to outperform traditional, relational models for speed of data storage, indexing, and query retrieval in nearly every operation. These findings strongly support the use of novel database technologies to improve the efficiency of data management within the biological sciences. Copyright © 2016 Elsevier Inc. All rights reserved.
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Disbiome database: linking the microbiome to disease.
Janssens, Yorick; Nielandt, Joachim; Bronselaer, Antoon; Debunne, Nathan; Verbeke, Frederick; Wynendaele, Evelien; Van Immerseel, Filip; Vandewynckel, Yves-Paul; De Tré, Guy; De Spiegeleer, Bart
2018-06-04
Recent research has provided fascinating indications and evidence that the host health is linked to its microbial inhabitants. Due to the development of high-throughput sequencing technologies, more and more data covering microbial composition changes in different disease types are emerging. However, this information is dispersed over a wide variety of medical and biomedical disciplines. Disbiome is a database which collects and presents published microbiota-disease information in a standardized way. The diseases are classified using the MedDRA classification system and the micro-organisms are linked to their NCBI and SILVA taxonomy. Finally, each study included in the Disbiome database is assessed for its reporting quality using a standardized questionnaire. Disbiome is the first database giving a clear, concise and up-to-date overview of microbial composition differences in diseases, together with the relevant information of the studies published. The strength of this database lies within the combination of the presence of references to other databases, which enables both specific and diverse search strategies within the Disbiome database, and the human annotation which ensures a simple and structured presentation of the available data.
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Specialist Bibliographic Databases
2016-01-01
Specialist bibliographic databases offer essential online tools for researchers and authors who work on specific subjects and perform comprehensive and systematic syntheses of evidence. This article presents examples of the established specialist databases, which may be of interest to those engaged in multidisciplinary science communication. Access to most specialist databases is through subscription schemes and membership in professional associations. Several aggregators of information and database vendors, such as EBSCOhost and ProQuest, facilitate advanced searches supported by specialist keyword thesauri. Searches of items through specialist databases are complementary to those through multidisciplinary research platforms, such as PubMed, Web of Science, and Google Scholar. Familiarizing with the functional characteristics of biomedical and nonbiomedical bibliographic search tools is mandatory for researchers, authors, editors, and publishers. The database users are offered updates of the indexed journal lists, abstracts, author profiles, and links to other metadata. Editors and publishers may find particularly useful source selection criteria and apply for coverage of their peer-reviewed journals and grey literature sources. These criteria are aimed at accepting relevant sources with established editorial policies and quality controls. PMID:27134485
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Specialist Bibliographic Databases.
Gasparyan, Armen Yuri; Yessirkepov, Marlen; Voronov, Alexander A; Trukhachev, Vladimir I; Kostyukova, Elena I; Gerasimov, Alexey N; Kitas, George D
2016-05-01
Specialist bibliographic databases offer essential online tools for researchers and authors who work on specific subjects and perform comprehensive and systematic syntheses of evidence. This article presents examples of the established specialist databases, which may be of interest to those engaged in multidisciplinary science communication. Access to most specialist databases is through subscription schemes and membership in professional associations. Several aggregators of information and database vendors, such as EBSCOhost and ProQuest, facilitate advanced searches supported by specialist keyword thesauri. Searches of items through specialist databases are complementary to those through multidisciplinary research platforms, such as PubMed, Web of Science, and Google Scholar. Familiarizing with the functional characteristics of biomedical and nonbiomedical bibliographic search tools is mandatory for researchers, authors, editors, and publishers. The database users are offered updates of the indexed journal lists, abstracts, author profiles, and links to other metadata. Editors and publishers may find particularly useful source selection criteria and apply for coverage of their peer-reviewed journals and grey literature sources. These criteria are aimed at accepting relevant sources with established editorial policies and quality controls.
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Biomedical question answering using semantic relations.
Hristovski, Dimitar; Dinevski, Dejan; Kastrin, Andrej; Rindflesch, Thomas C
2015-01-16
The proliferation of the scientific literature in the field of biomedicine makes it difficult to keep abreast of current knowledge, even for domain experts. While general Web search engines and specialized information retrieval (IR) systems have made important strides in recent decades, the problem of accurate knowledge extraction from the biomedical literature is far from solved. Classical IR systems usually return a list of documents that have to be read by the user to extract relevant information. This tedious and time-consuming work can be lessened with automatic Question Answering (QA) systems, which aim to provide users with direct and precise answers to their questions. In this work we propose a novel methodology for QA based on semantic relations extracted from the biomedical literature. We extracted semantic relations with the SemRep natural language processing system from 122,421,765 sentences, which came from 21,014,382 MEDLINE citations (i.e., the complete MEDLINE distribution up to the end of 2012). A total of 58,879,300 semantic relation instances were extracted and organized in a relational database. The QA process is implemented as a search in this database, which is accessed through a Web-based application, called SemBT (available at http://sembt.mf.uni-lj.si ). We conducted an extensive evaluation of the proposed methodology in order to estimate the accuracy of extracting a particular semantic relation from a particular sentence. Evaluation was performed by 80 domain experts. In total 7,510 semantic relation instances belonging to 2,675 distinct relations were evaluated 12,083 times. The instances were evaluated as correct 8,228 times (68%). In this work we propose an innovative methodology for biomedical QA. The system is implemented as a Web-based application that is able to provide precise answers to a wide range of questions. A typical question is answered within a few seconds. The tool has some extensions that make it especially useful for interpretation of DNA microarray results.
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Academic Journal Embargoes and Full Text Databases.
ERIC Educational Resources Information Center
Brooks, Sam
2003-01-01
Documents the reasons for embargoes of academic journals in full text databases (i.e., publisher-imposed delays on the availability of full text content) and provides insight regarding common misconceptions. Tables present data on selected journals covering a cross-section of subjects and publishers and comparing two full text business databases.…
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The immune epitope database: a historical retrospective of the first decade.
Salimi, Nima; Fleri, Ward; Peters, Bjoern; Sette, Alessandro
2012-10-01
As the amount of biomedical information available in the literature continues to increase, databases that aggregate this information continue to grow in importance and scope. The population of databases can occur either through fully automated text mining approaches or through manual curation by human subject experts. We here report our experiences in populating the National Institute of Allergy and Infectious Diseases sponsored Immune Epitope Database and Analysis Resource (IEDB, http://iedb.org), which was created in 2003, and as of 2012 captures the epitope information from approximately 99% of all papers published to date that describe immune epitopes (with the exception of cancer and HIV data). This was achieved using a hybrid model based on automated document categorization and extensive human expert involvement. This task required automated scanning of over 22 million PubMed abstracts followed by classification and curation of over 13 000 references, including over 7000 infectious disease-related manuscripts, over 1000 allergy-related manuscripts, roughly 4000 related to autoimmunity, and 1000 transplant/alloantigen-related manuscripts. The IEDB curation involves an unprecedented level of detail, capturing for each paper the actual experiments performed for each different epitope structure. Key to enabling this process was the extensive use of ontologies to ensure rigorous and consistent data representation as well as interoperability with other bioinformatics resources, including the Protein Data Bank, Chemical Entities of Biological Interest, and the NIAID Bioinformatics Resource Centers. A growing fraction of the IEDB data derives from direct submissions by research groups engaged in epitope discovery, and is being facilitated by the implementation of novel data submission tools. The present explosion of information contained in biological databases demands effective query and display capabilities to optimize the user experience. Accordingly, the development of original ways to query the database, on the basis of ontologically driven hierarchical trees, and display of epitope data in aggregate in a biologically intuitive yet rigorous fashion is now at the forefront of the IEDB efforts. We also highlight advances made in the realm of epitope analysis and predictive tools available in the IEDB. © 2012 The Authors. Immunology © 2012 Blackwell Publishing Ltd.
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Full Text Psychology Journals Available from Popular Library Databases
ERIC Educational Resources Information Center
Joswick, Kathleen E.
2006-01-01
The author identified 433 core journals in psychology and investigated their full text availability in popular databases. While 62 percent of the studied journals were available in at least one database, access from individual databases ranged from 1.4 percent to 38.1 percent of the titles. The full text of influential psychology journals is not…
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Corpus annotation for mining biomedical events from literature
Kim, Jin-Dong; Ohta, Tomoko; Tsujii, Jun'ichi
2008-01-01
Background Advanced Text Mining (TM) such as semantic enrichment of papers, event or relation extraction, and intelligent Question Answering have increasingly attracted attention in the bio-medical domain. For such attempts to succeed, text annotation from the biological point of view is indispensable. However, due to the complexity of the task, semantic annotation has never been tried on a large scale, apart from relatively simple term annotation. Results We have completed a new type of semantic annotation, event annotation, which is an addition to the existing annotations in the GENIA corpus. The corpus has already been annotated with POS (Parts of Speech), syntactic trees, terms, etc. The new annotation was made on half of the GENIA corpus, consisting of 1,000 Medline abstracts. It contains 9,372 sentences in which 36,114 events are identified. The major challenges during event annotation were (1) to design a scheme of annotation which meets specific requirements of text annotation, (2) to achieve biology-oriented annotation which reflect biologists' interpretation of text, and (3) to ensure the homogeneity of annotation quality across annotators. To meet these challenges, we introduced new concepts such as Single-facet Annotation and Semantic Typing, which have collectively contributed to successful completion of a large scale annotation. Conclusion The resulting event-annotated corpus is the largest and one of the best in quality among similar annotation efforts. We expect it to become a valuable resource for NLP (Natural Language Processing)-based TM in the bio-medical domain. PMID:18182099
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Essie: A Concept-based Search Engine for Structured Biomedical Text
Ide, Nicholas C.; Loane, Russell F.; Demner-Fushman, Dina
2007-01-01
This article describes the algorithms implemented in the Essie search engine that is currently serving several Web sites at the National Library of Medicine. Essie is a phrase-based search engine with term and concept query expansion and probabilistic relevancy ranking. Essie’s design is motivated by an observation that query terms are often conceptually related to terms in a document, without actually occurring in the document text. Essie’s performance was evaluated using data and standard evaluation methods from the 2003 and 2006 Text REtrieval Conference (TREC) Genomics track. Essie was the best-performing search engine in the 2003 TREC Genomics track and achieved results comparable to those of the highest-ranking systems on the 2006 TREC Genomics track task. Essie shows that a judicious combination of exploiting document structure, phrase searching, and concept based query expansion is a useful approach for information retrieval in the biomedical domain. PMID:17329729
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Visualizing the semantic content of large text databases using text maps
NASA Technical Reports Server (NTRS)
Combs, Nathan
1993-01-01
A methodology for generating text map representations of the semantic content of text databases is presented. Text maps provide a graphical metaphor for conceptualizing and visualizing the contents and data interrelationships of large text databases. Described are a set of experiments conducted against the TIPSTER corpora of Wall Street Journal articles. These experiments provide an introduction to current work in the representation and visualization of documents by way of their semantic content.
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Benson, Dennis A.; Karsch-Mizrachi, Ilene; Lipman, David J.; Ostell, James; Wheeler, David L.
2007-01-01
GenBank (R) is a comprehensive database that contains publicly available nucleotide sequences for more than 240 000 named organisms, obtained primarily through submissions from individual laboratories and batch submissions from large-scale sequencing projects. Most submissions are made using the web-based BankIt or standalone Sequin programs and accession numbers are assigned by GenBank staff upon receipt. Daily data exchange with the EMBL Data Library in Europe and the DNA Data Bank of Japan ensures worldwide coverage. GenBank is accessible through NCBI's retrieval system, Entrez, which integrates data from the major DNA and protein sequence databases along with taxonomy, genome, mapping, protein structure and domain information, and the biomedical journal literature via PubMed. BLAST provides sequence similarity searches of GenBank and other sequence databases. Complete bimonthly releases and daily updates of the GenBank database are available by FTP. To access GenBank and its related retrieval and analysis services, begin at the NCBI Homepage (). PMID:17202161
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@Note: a workbench for biomedical text mining.
Lourenço, Anália; Carreira, Rafael; Carneiro, Sónia; Maia, Paulo; Glez-Peña, Daniel; Fdez-Riverola, Florentino; Ferreira, Eugénio C; Rocha, Isabel; Rocha, Miguel
2009-08-01
Biomedical Text Mining (BioTM) is providing valuable approaches to the automated curation of scientific literature. However, most efforts have addressed the benchmarking of new algorithms rather than user operational needs. Bridging the gap between BioTM researchers and biologists' needs is crucial to solve real-world problems and promote further research. We present @Note, a platform for BioTM that aims at the effective translation of the advances between three distinct classes of users: biologists, text miners and software developers. Its main functional contributions are the ability to process abstracts and full-texts; an information retrieval module enabling PubMed search and journal crawling; a pre-processing module with PDF-to-text conversion, tokenisation and stopword removal; a semantic annotation schema; a lexicon-based annotator; a user-friendly annotation view that allows to correct annotations and a Text Mining Module supporting dataset preparation and algorithm evaluation. @Note improves the interoperability, modularity and flexibility when integrating in-home and open-source third-party components. Its component-based architecture allows the rapid development of new applications, emphasizing the principles of transparency and simplicity of use. Although it is still on-going, it has already allowed the development of applications that are currently being used.
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PathNER: a tool for systematic identification of biological pathway mentions in the literature
2013-01-01
Background Biological pathways are central to many biomedical studies and are frequently discussed in the literature. Several curated databases have been established to collate the knowledge of molecular processes constituting pathways. Yet, there has been little focus on enabling systematic detection of pathway mentions in the literature. Results We developed a tool, named PathNER (Pathway Named Entity Recognition), for the systematic identification of pathway mentions in the literature. PathNER is based on soft dictionary matching and rules, with the dictionary generated from public pathway databases. The rules utilise general pathway-specific keywords, syntactic information and gene/protein mentions. Detection results from both components are merged. On a gold-standard corpus, PathNER achieved an F1-score of 84%. To illustrate its potential, we applied PathNER on a collection of articles related to Alzheimer's disease to identify associated pathways, highlighting cases that can complement an existing manually curated knowledgebase. Conclusions In contrast to existing text-mining efforts that target the automatic reconstruction of pathway details from molecular interactions mentioned in the literature, PathNER focuses on identifying specific named pathway mentions. These mentions can be used to support large-scale curation and pathway-related systems biology applications, as demonstrated in the example of Alzheimer's disease. PathNER is implemented in Java and made freely available online at http://sourceforge.net/projects/pathner/. PMID:24555844
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Venselaar, Hanka; Te Beek, Tim A H; Kuipers, Remko K P; Hekkelman, Maarten L; Vriend, Gert
2010-11-08
Many newly detected point mutations are located in protein-coding regions of the human genome. Knowledge of their effects on the protein's 3D structure provides insight into the protein's mechanism, can aid the design of further experiments, and eventually can lead to the development of new medicines and diagnostic tools. In this article we describe HOPE, a fully automatic program that analyzes the structural and functional effects of point mutations. HOPE collects information from a wide range of information sources including calculations on the 3D coordinates of the protein by using WHAT IF Web services, sequence annotations from the UniProt database, and predictions by DAS services. Homology models are built with YASARA. Data is stored in a database and used in a decision scheme to identify the effects of a mutation on the protein's 3D structure and function. HOPE builds a report with text, figures, and animations that is easy to use and understandable for (bio)medical researchers. We tested HOPE by comparing its output to the results of manually performed projects. In all straightforward cases HOPE performed similar to a trained bioinformatician. The use of 3D structures helps optimize the results in terms of reliability and details. HOPE's results are easy to understand and are presented in a way that is attractive for researchers without an extensive bioinformatics background.
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Aggregated Indexing of Biomedical Time Series Data
Woodbridge, Jonathan; Mortazavi, Bobak; Sarrafzadeh, Majid; Bui, Alex A.T.
2016-01-01
Remote and wearable medical sensing has the potential to create very large and high dimensional datasets. Medical time series databases must be able to efficiently store, index, and mine these datasets to enable medical professionals to effectively analyze data collected from their patients. Conventional high dimensional indexing methods are a two stage process. First, a superset of the true matches is efficiently extracted from the database. Second, supersets are pruned by comparing each of their objects to the query object and rejecting any objects falling outside a predetermined radius. This pruning stage heavily dominates the computational complexity of most conventional search algorithms. Therefore, indexing algorithms can be significantly improved by reducing the amount of pruning. This paper presents an online algorithm to aggregate biomedical times series data to significantly reduce the search space (index size) without compromising the quality of search results. This algorithm is built on the observation that biomedical time series signals are composed of cyclical and often similar patterns. This algorithm takes in a stream of segments and groups them to highly concentrated collections. Locality Sensitive Hashing (LSH) is used to reduce the overall complexity of the algorithm, allowing it to run online. The output of this aggregation is used to populate an index. The proposed algorithm yields logarithmic growth of the index (with respect to the total number of objects) while keeping sensitivity and specificity simultaneously above 98%. Both memory and runtime complexities of time series search are improved when using aggregated indexes. In addition, data mining tasks, such as clustering, exhibit runtimes that are orders of magnitudes faster when run on aggregated indexes. PMID:27617298
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Life sciences domain analysis model
Freimuth, Robert R; Freund, Elaine T; Schick, Lisa; Sharma, Mukesh K; Stafford, Grace A; Suzek, Baris E; Hernandez, Joyce; Hipp, Jason; Kelley, Jenny M; Rokicki, Konrad; Pan, Sue; Buckler, Andrew; Stokes, Todd H; Fernandez, Anna; Fore, Ian; Buetow, Kenneth H
2012-01-01
Objective Meaningful exchange of information is a fundamental challenge in collaborative biomedical research. To help address this, the authors developed the Life Sciences Domain Analysis Model (LS DAM), an information model that provides a framework for communication among domain experts and technical teams developing information systems to support biomedical research. The LS DAM is harmonized with the Biomedical Research Integrated Domain Group (BRIDG) model of protocol-driven clinical research. Together, these models can facilitate data exchange for translational research. Materials and methods The content of the LS DAM was driven by analysis of life sciences and translational research scenarios and the concepts in the model are derived from existing information models, reference models and data exchange formats. The model is represented in the Unified Modeling Language and uses ISO 21090 data types. Results The LS DAM v2.2.1 is comprised of 130 classes and covers several core areas including Experiment, Molecular Biology, Molecular Databases and Specimen. Nearly half of these classes originate from the BRIDG model, emphasizing the semantic harmonization between these models. Validation of the LS DAM against independently derived information models, research scenarios and reference databases supports its general applicability to represent life sciences research. Discussion The LS DAM provides unambiguous definitions for concepts required to describe life sciences research. The processes established to achieve consensus among domain experts will be applied in future iterations and may be broadly applicable to other standardization efforts. Conclusions The LS DAM provides common semantics for life sciences research. Through harmonization with BRIDG, it promotes interoperability in translational science. PMID:22744959
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Acupuncture for treating sciatica: a systematic review protocol
Qin, Zongshi; Liu, Xiaoxu; Yao, Qin; Zhai, Yanbing; Liu, Zhishun
2015-01-01
Introduction This systematic review aims to assess the effectiveness and safety of acupuncture for treating sciatica. Methods The following nine databases will be searched from their inception to 30 October 2014: MEDLINE, EMBASE, the Cochrane Central Register of Controlled Trials (CENTRAL), the Chinese Biomedical Literature Database (CBM), the Chinese Medical Current Content (CMCC), the Chinese Scientific Journal Database (VIP database), the Wan-Fang Database, the China National Knowledge Infrastructure (CNKI) and Citation Information by National Institute of Informatics (CiNii). Randomised controlled trials (RCTs) of acupuncture for sciatica in English, Chinese or Japanese without restriction of publication status will be included. Two researchers will independently undertake study selection, extraction of data and assessment of study quality. Meta-analysis will be conducted after screening of studies. Data will be analysed using risk ratio for dichotomous data, and standardised mean difference or weighted mean difference for continuous data. Dissemination This systematic review will be disseminated electronically through a peer-reviewed publication or conference presentations. Trial registration number PROSPERO CRD42014015001. PMID:25922105
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Benson, Dennis A; Karsch-Mizrachi, Ilene; Lipman, David J; Ostell, James; Wheeler, David L
2008-01-01
GenBank (R) is a comprehensive database that contains publicly available nucleotide sequences for more than 260 000 named organisms, obtained primarily through submissions from individual laboratories and batch submissions from large-scale sequencing projects. Most submissions are made using the web-based BankIt or standalone Sequin programs and accession numbers are assigned by GenBank staff upon receipt. Daily data exchange with the European Molecular Biology Laboratory Nucleotide Sequence Database in Europe and the DNA Data Bank of Japan ensures worldwide coverage. GenBank is accessible through NCBI's retrieval system, Entrez, which integrates data from the major DNA and protein sequence databases along with taxonomy, genome, mapping, protein structure and domain information, and the biomedical journal literature via PubMed. BLAST provides sequence similarity searches of GenBank and other sequence databases. Complete bimonthly releases and daily updates of the GenBank database are available by FTP. To access GenBank and its related retrieval and analysis services, begin at the NCBI Homepage: www.ncbi.nlm.nih.gov.
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Benson, Dennis A.; Karsch-Mizrachi, Ilene; Lipman, David J.; Ostell, James; Wheeler, David L.
2008-01-01
GenBank (R) is a comprehensive database that contains publicly available nucleotide sequences for more than 260 000 named organisms, obtained primarily through submissions from individual laboratories and batch submissions from large-scale sequencing projects. Most submissions are made using the web-based BankIt or standalone Sequin programs and accession numbers are assigned by GenBank staff upon receipt. Daily data exchange with the European Molecular Biology Laboratory Nucleotide Sequence Database in Europe and the DNA Data Bank of Japan ensures worldwide coverage. GenBank is accessible through NCBI's retrieval system, Entrez, which integrates data from the major DNA and protein sequence databases along with taxonomy, genome, mapping, protein structure and domain information, and the biomedical journal literature via PubMed. BLAST provides sequence similarity searches of GenBank and other sequence databases. Complete bimonthly releases and daily updates of the GenBank database are available by FTP. To access GenBank and its related retrieval and analysis services, begin at the NCBI Homepage: www.ncbi.nlm.nih.gov PMID:18073190
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CD-REST: a system for extracting chemical-induced disease relation in literature.
Xu, Jun; Wu, Yonghui; Zhang, Yaoyun; Wang, Jingqi; Lee, Hee-Jin; Xu, Hua
2016-01-01
Mining chemical-induced disease relations embedded in the vast biomedical literature could facilitate a wide range of computational biomedical applications, such as pharmacovigilance. The BioCreative V organized a Chemical Disease Relation (CDR) Track regarding chemical-induced disease relation extraction from biomedical literature in 2015. We participated in all subtasks of this challenge. In this article, we present our participation system Chemical Disease Relation Extraction SysTem (CD-REST), an end-to-end system for extracting chemical-induced disease relations in biomedical literature. CD-REST consists of two main components: (1) a chemical and disease named entity recognition and normalization module, which employs the Conditional Random Fields algorithm for entity recognition and a Vector Space Model-based approach for normalization; and (2) a relation extraction module that classifies both sentence-level and document-level candidate drug-disease pairs by support vector machines. Our system achieved the best performance on the chemical-induced disease relation extraction subtask in the BioCreative V CDR Track, demonstrating the effectiveness of our proposed machine learning-based approaches for automatic extraction of chemical-induced disease relations in biomedical literature. The CD-REST system provides web services using HTTP POST request. The web services can be accessed fromhttp://clinicalnlptool.com/cdr The online CD-REST demonstration system is available athttp://clinicalnlptool.com/cdr/cdr.html. Database URL:http://clinicalnlptool.com/cdr;http://clinicalnlptool.com/cdr/cdr.html. © The Author(s) 2016. Published by Oxford University Press.
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A common layer of interoperability for biomedical ontologies based on OWL EL.
Hoehndorf, Robert; Dumontier, Michel; Oellrich, Anika; Wimalaratne, Sarala; Rebholz-Schuhmann, Dietrich; Schofield, Paul; Gkoutos, Georgios V
2011-04-01
Ontologies are essential in biomedical research due to their ability to semantically integrate content from different scientific databases and resources. Their application improves capabilities for querying and mining biological knowledge. An increasing number of ontologies is being developed for this purpose, and considerable effort is invested into formally defining them in order to represent their semantics explicitly. However, current biomedical ontologies do not facilitate data integration and interoperability yet, since reasoning over these ontologies is very complex and cannot be performed efficiently or is even impossible. We propose the use of less expressive subsets of ontology representation languages to enable efficient reasoning and achieve the goal of genuine interoperability between ontologies. We present and evaluate EL Vira, a framework that transforms OWL ontologies into the OWL EL subset, thereby enabling the use of tractable reasoning. We illustrate which OWL constructs and inferences are kept and lost following the conversion and demonstrate the performance gain of reasoning indicated by the significant reduction of processing time. We applied EL Vira to the open biomedical ontologies and provide a repository of ontologies resulting from this conversion. EL Vira creates a common layer of ontological interoperability that, for the first time, enables the creation of software solutions that can employ biomedical ontologies to perform inferences and answer complex queries to support scientific analyses. The EL Vira software is available from http://el-vira.googlecode.com and converted OBO ontologies and their mappings are available from http://bioonto.gen.cam.ac.uk/el-ont.
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Kaewphan, Suwisa; Van Landeghem, Sofie; Ohta, Tomoko; Van de Peer, Yves; Ginter, Filip; Pyysalo, Sampo
2016-01-01
Motivation: The recognition and normalization of cell line names in text is an important task in biomedical text mining research, facilitating for instance the identification of synthetically lethal genes from the literature. While several tools have previously been developed to address cell line recognition, it is unclear whether available systems can perform sufficiently well in realistic and broad-coverage applications such as extracting synthetically lethal genes from the cancer literature. In this study, we revisit the cell line name recognition task, evaluating both available systems and newly introduced methods on various resources to obtain a reliable tagger not tied to any specific subdomain. In support of this task, we introduce two text collections manually annotated for cell line names: the broad-coverage corpus Gellus and CLL, a focused target domain corpus. Results: We find that the best performance is achieved using NERsuite, a machine learning system based on Conditional Random Fields, trained on the Gellus corpus and supported with a dictionary of cell line names. The system achieves an F-score of 88.46% on the test set of Gellus and 85.98% on the independently annotated CLL corpus. It was further applied at large scale to 24 302 102 unannotated articles, resulting in the identification of 5 181 342 cell line mentions, normalized to 11 755 unique cell line database identifiers. Availability and implementation: The manually annotated datasets, the cell line dictionary, derived corpora, NERsuite models and the results of the large-scale run on unannotated texts are available under open licenses at http://turkunlp.github.io/Cell-line-recognition/. Contact: sukaew@utu.fi PMID:26428294
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Community challenges in biomedical text mining over 10 years: success, failure and the future.
Huang, Chung-Chi; Lu, Zhiyong
2016-01-01
One effective way to improve the state of the art is through competitions. Following the success of the Critical Assessment of protein Structure Prediction (CASP) in bioinformatics research, a number of challenge evaluations have been organized by the text-mining research community to assess and advance natural language processing (NLP) research for biomedicine. In this article, we review the different community challenge evaluations held from 2002 to 2014 and their respective tasks. Furthermore, we examine these challenge tasks through their targeted problems in NLP research and biomedical applications, respectively. Next, we describe the general workflow of organizing a Biomedical NLP (BioNLP) challenge and involved stakeholders (task organizers, task data producers, task participants and end users). Finally, we summarize the impact and contributions by taking into account different BioNLP challenges as a whole, followed by a discussion of their limitations and difficulties. We conclude with future trends in BioNLP challenge evaluations. Published by Oxford University Press 2015. This work is written by US Government employees and is in the public domain in the US.
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Bakal, Gokhan; Talari, Preetham; Kakani, Elijah V; Kavuluru, Ramakanth
2018-06-01
Identifying new potential treatment options for medical conditions that cause human disease burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and clinical trials, in vitro approaches are first attempted to identify promising candidates. Likewise, identifying different causal relations between biomedical entities is also critical to understand biomedical processes. Generally, natural language processing (NLP) and machine learning are used to predict specific relations between any given pair of entities using the distant supervision approach. To build high accuracy supervised predictive models to predict previously unknown treatment and causative relations between biomedical entities based only on semantic graph pattern features extracted from biomedical knowledge graphs. We used 7000 treats and 2918 causes hand-curated relations from the UMLS Metathesaurus to train and test our models. Our graph pattern features are extracted from simple paths connecting biomedical entities in the SemMedDB graph (based on the well-known SemMedDB database made available by the U.S. National Library of Medicine). Using these graph patterns connecting biomedical entities as features of logistic regression and decision tree models, we computed mean performance measures (precision, recall, F-score) over 100 distinct 80-20% train-test splits of the datasets. For all experiments, we used a positive:negative class imbalance of 1:10 in the test set to model relatively more realistic scenarios. Our models predict treats and causes relations with high F-scores of 99% and 90% respectively. Logistic regression model coefficients also help us identify highly discriminative patterns that have an intuitive interpretation. We are also able to predict some new plausible relations based on false positives that our models scored highly based on our collaborations with two physician co-authors. Finally, our decision tree models are able to retrieve over 50% of treatment relations from a recently created external dataset. We employed semantic graph patterns connecting pairs of candidate biomedical entities in a knowledge graph as features to predict treatment/causative relations between them. We provide what we believe is the first evidence in direct prediction of biomedical relations based on graph features. Our work complements lexical pattern based approaches in that the graph patterns can be used as additional features for weakly supervised relation prediction. Copyright © 2018 Elsevier Inc. All rights reserved.
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Tsatsaronis, George; Balikas, Georgios; Malakasiotis, Prodromos; Partalas, Ioannis; Zschunke, Matthias; Alvers, Michael R; Weissenborn, Dirk; Krithara, Anastasia; Petridis, Sergios; Polychronopoulos, Dimitris; Almirantis, Yannis; Pavlopoulos, John; Baskiotis, Nicolas; Gallinari, Patrick; Artiéres, Thierry; Ngomo, Axel-Cyrille Ngonga; Heino, Norman; Gaussier, Eric; Barrio-Alvers, Liliana; Schroeder, Michael; Androutsopoulos, Ion; Paliouras, Georgios
2015-04-30
This article provides an overview of the first BIOASQ challenge, a competition on large-scale biomedical semantic indexing and question answering (QA), which took place between March and September 2013. BIOASQ assesses the ability of systems to semantically index very large numbers of biomedical scientific articles, and to return concise and user-understandable answers to given natural language questions by combining information from biomedical articles and ontologies. The 2013 BIOASQ competition comprised two tasks, Task 1a and Task 1b. In Task 1a participants were asked to automatically annotate new PUBMED documents with MESH headings. Twelve teams participated in Task 1a, with a total of 46 system runs submitted, and one of the teams performing consistently better than the MTI indexer used by NLM to suggest MESH headings to curators. Task 1b used benchmark datasets containing 29 development and 282 test English questions, along with gold standard (reference) answers, prepared by a team of biomedical experts from around Europe and participants had to automatically produce answers. Three teams participated in Task 1b, with 11 system runs. The BIOASQ infrastructure, including benchmark datasets, evaluation mechanisms, and the results of the participants and baseline methods, is publicly available. A publicly available evaluation infrastructure for biomedical semantic indexing and QA has been developed, which includes benchmark datasets, and can be used to evaluate systems that: assign MESH headings to published articles or to English questions; retrieve relevant RDF triples from ontologies, relevant articles and snippets from PUBMED Central; produce "exact" and paragraph-sized "ideal" answers (summaries). The results of the systems that participated in the 2013 BIOASQ competition are promising. In Task 1a one of the systems performed consistently better from the NLM's MTI indexer. In Task 1b the systems received high scores in the manual evaluation of the "ideal" answers; hence, they produced high quality summaries as answers. Overall, BIOASQ helped obtain a unified view of how techniques from text classification, semantic indexing, document and passage retrieval, question answering, and text summarization can be combined to allow biomedical experts to obtain concise, user-understandable answers to questions reflecting their real information needs.
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Abstracts versus Full Texts and Patents: A Quantitative Analysis of Biomedical Entities
NASA Astrophysics Data System (ADS)
Müller, Bernd; Klinger, Roman; Gurulingappa, Harsha; Mevissen, Heinz-Theodor; Hofmann-Apitius, Martin; Fluck, Juliane; Friedrich, Christoph M.
In information retrieval, named entity recognition gives the opportunity to apply semantic search in domain specific corpora. Recently, more full text patents and journal articles became freely available. As the information distribution amongst the different sections is unknown, an analysis of the diversity is of interest.
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Nominalization and Alternations in Biomedical Language
Cohen, K. Bretonnel; Palmer, Martha; Hunter, Lawrence
2008-01-01
Background This paper presents data on alternations in the argument structure of common domain-specific verbs and their associated verbal nominalizations in the PennBioIE corpus. Alternation is the term in theoretical linguistics for variations in the surface syntactic form of verbs, e.g. the different forms of stimulate in FSH stimulates follicular development and follicular development is stimulated by FSH. The data is used to assess the implications of alternations for biomedical text mining systems and to test the fit of the sublanguage model to biomedical texts. Methodology/Principal Findings We examined 1,872 tokens of the ten most common domain-specific verbs or their zero-related nouns in the PennBioIE corpus and labelled them for the presence or absence of three alternations. We then annotated the arguments of 746 tokens of the nominalizations related to these verbs and counted alternations related to the presence or absence of arguments and to the syntactic position of non-absent arguments. We found that alternations are quite common both for verbs and for nominalizations. We also found a previously undescribed alternation involving an adjectival present participle. Conclusions/Significance We found that even in this semantically restricted domain, alternations are quite common, and alternations involving nominalizations are exceptionally diverse. Nonetheless, the sublanguage model applies to biomedical language. We also report on a previously undescribed alternation involving an adjectival present participle. PMID:18779866
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MalaCards: an integrated compendium for diseases and their annotation
Rappaport, Noa; Nativ, Noam; Stelzer, Gil; Twik, Michal; Guan-Golan, Yaron; Iny Stein, Tsippi; Bahir, Iris; Belinky, Frida; Morrey, C. Paul; Safran, Marilyn; Lancet, Doron
2013-01-01
Comprehensive disease classification, integration and annotation are crucial for biomedical discovery. At present, disease compilation is incomplete, heterogeneous and often lacking systematic inquiry mechanisms. We introduce MalaCards, an integrated database of human maladies and their annotations, modeled on the architecture and strategy of the GeneCards database of human genes. MalaCards mines and merges 44 data sources to generate a computerized card for each of 16 919 human diseases. Each MalaCard contains disease-specific prioritized annotations, as well as inter-disease connections, empowered by the GeneCards relational database, its searches and GeneDecks set analyses. First, we generate a disease list from 15 ranked sources, using disease-name unification heuristics. Next, we use four schemes to populate MalaCards sections: (i) directly interrogating disease resources, to establish integrated disease names, synonyms, summaries, drugs/therapeutics, clinical features, genetic tests and anatomical context; (ii) searching GeneCards for related publications, and for associated genes with corresponding relevance scores; (iii) analyzing disease-associated gene sets in GeneDecks to yield affiliated pathways, phenotypes, compounds and GO terms, sorted by a composite relevance score and presented with GeneCards links; and (iv) searching within MalaCards itself, e.g. for additional related diseases and anatomical context. The latter forms the basis for the construction of a disease network, based on shared MalaCards annotations, embodying associations based on etiology, clinical features and clinical conditions. This broadly disposed network has a power-law degree distribution, suggesting that this might be an inherent property of such networks. Work in progress includes hierarchical malady classification, ontological mapping and disease set analyses, striving to make MalaCards an even more effective tool for biomedical research. Database URL: http://www.malacards.org/ PMID:23584832
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Construction of an annotated corpus to support biomedical information extraction
Thompson, Paul; Iqbal, Syed A; McNaught, John; Ananiadou, Sophia
2009-01-01
Background Information Extraction (IE) is a component of text mining that facilitates knowledge discovery by automatically locating instances of interesting biomedical events from huge document collections. As events are usually centred on verbs and nominalised verbs, understanding the syntactic and semantic behaviour of these words is highly important. Corpora annotated with information concerning this behaviour can constitute a valuable resource in the training of IE components and resources. Results We have defined a new scheme for annotating sentence-bound gene regulation events, centred on both verbs and nominalised verbs. For each event instance, all participants (arguments) in the same sentence are identified and assigned a semantic role from a rich set of 13 roles tailored to biomedical research articles, together with a biological concept type linked to the Gene Regulation Ontology. To our knowledge, our scheme is unique within the biomedical field in terms of the range of event arguments identified. Using the scheme, we have created the Gene Regulation Event Corpus (GREC), consisting of 240 MEDLINE abstracts, in which events relating to gene regulation and expression have been annotated by biologists. A novel method of evaluating various different facets of the annotation task showed that average inter-annotator agreement rates fall within the range of 66% - 90%. Conclusion The GREC is a unique resource within the biomedical field, in that it annotates not only core relationships between entities, but also a range of other important details about these relationships, e.g., location, temporal, manner and environmental conditions. As such, it is specifically designed to support bio-specific tool and resource development. It has already been used to acquire semantic frames for inclusion within the BioLexicon (a lexical, terminological resource to aid biomedical text mining). Initial experiments have also shown that the corpus may viably be used to train IE components, such as semantic role labellers. The corpus and annotation guidelines are freely available for academic purposes. PMID:19852798
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Building an Ontology-driven Database for Clinical Immune Research
Ma, Jingming
2006-01-01
The clinical researches of immune response usually generate a huge amount of biomedical testing data over a certain period of time. The user-friendly data management systems based on the relational database will help immunologists/clinicians to fully manage the data. On the other hand, the same biological assays such as ELISPOT and flow cytometric assays are involved in immunological experiments no matter of different study purposes. The reuse of biological knowledge is one of driving forces behind this ontology-driven data management. Therefore, an ontology-driven database will help to handle different clinical immune researches and help immunologists/clinicians easily understand the immunological data from each other. We will discuss some outlines for building an ontology-driven data management for clinical immune researches (ODMim). PMID:17238637
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Outreach and online training services at the Saccharomyces Genome Database.
MacPherson, Kevin A; Starr, Barry; Wong, Edith D; Dalusag, Kyla S; Hellerstedt, Sage T; Lang, Olivia W; Nash, Robert S; Skrzypek, Marek S; Engel, Stacia R; Cherry, J Michael
2017-01-01
The Saccharomyces Genome Database (SGD; www.yeastgenome.org ), the primary genetics and genomics resource for the budding yeast S. cerevisiae , provides free public access to expertly curated information about the yeast genome and its gene products. As the central hub for the yeast research community, SGD engages in a variety of social outreach efforts to inform our users about new developments, promote collaboration, increase public awareness of the importance of yeast to biomedical research, and facilitate scientific discovery. Here we describe these various outreach methods, from networking at scientific conferences to the use of online media such as blog posts and webinars, and include our perspectives on the benefits provided by outreach activities for model organism databases. http://www.yeastgenome.org. © The Author(s) 2017. Published by Oxford University Press.
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Taiwan Biobank: making cross-database convergence possible in the Big Data era
Lin, Jui-Chu; Fan, Chien-Te; Liao, Chia-Cheng; Chen, Yao-Sheng
2018-01-01
Abstract The Taiwan Biobank (TWB) is a biomedical research database of biopsy data from 200 000 participants. Access to this database has been granted to research communities taking part in the development of precision medicines; however, this has raised issues surrounding TWB’s access to electronic medical records (EMRs). The Personal Data Protection Act of Taiwan restricts access to EMRs for purposes not covered by patients’ original consent. This commentary explores possible legal solutions to help ensure that the access TWB has to EMR abides with legal obligations, and with governance frameworks associated with ethical, legal, and social implications. We suggest utilizing “hash function” algorithms to create nonretrospective, anonymized data for the purpose of cross-transmission and/or linkage with EMR. PMID:29149267
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González de Dios, J; Sempere, A P; Aleixandre-Benavent, R
To debate about the application of on-going 'revolutions' in medical knowledge to Spanish neurological journals in the 21st century. This article reviews the current status of five revolutions in the field of health sciences, in general, and in neurological sciences, in particular: 1) the knowledge revolution: to translate the scientific investigation to the patient, with knowledge needs-driven research agenda with founder commissioning research to answer questions posed by clinicians, managers and patients, and systematic and critical appraisal reviews as the creator of quality improved knowledge; 2) the evidence based medicine revolution: the pyramid information of '4S', with systems (guidelines and computerized decision support systems), synopses (secondary journals), syntheses (systematic reviews and meta-analysis) and studies (original studies published in journals); 3) the web revolution: the possibility of dissemination of biomedical documentation by means of the Internet network are producing changes in the traditional way of conceiving scientific publication; the Internet represents a great advantage for investigation and also for clinical practice, since it permits free, universal access to databases and the interchange of texts, images and videos; 4) the open access revolution: to take full control over all operations related to the process of publish (to create, publish, communicate, distribute, reproduce and transform) with no need of any intermediaries, and to transform fundamental aspects concerning the circulation of knowledge, its use and availability; and 5) the librarian revolution: the project of a Virtual Health Library in Spain as a tool to access and disseminate scientific and technical knowledge on health through the Internet.
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Text mining resources for the life sciences.
Przybyła, Piotr; Shardlow, Matthew; Aubin, Sophie; Bossy, Robert; Eckart de Castilho, Richard; Piperidis, Stelios; McNaught, John; Ananiadou, Sophia
2016-01-01
Text mining is a powerful technology for quickly distilling key information from vast quantities of biomedical literature. However, to harness this power the researcher must be well versed in the availability, suitability, adaptability, interoperability and comparative accuracy of current text mining resources. In this survey, we give an overview of the text mining resources that exist in the life sciences to help researchers, especially those employed in biocuration, to engage with text mining in their own work. We categorize the various resources under three sections: Content Discovery looks at where and how to find biomedical publications for text mining; Knowledge Encoding describes the formats used to represent the different levels of information associated with content that enable text mining, including those formats used to carry such information between processes; Tools and Services gives an overview of workflow management systems that can be used to rapidly configure and compare domain- and task-specific processes, via access to a wide range of pre-built tools. We also provide links to relevant repositories in each section to enable the reader to find resources relevant to their own area of interest. Throughout this work we give a special focus to resources that are interoperable-those that have the crucial ability to share information, enabling smooth integration and reusability. © The Author(s) 2016. Published by Oxford University Press.
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Chapter 16: text mining for translational bioinformatics.
Cohen, K Bretonnel; Hunter, Lawrence E
2013-04-01
Text mining for translational bioinformatics is a new field with tremendous research potential. It is a subfield of biomedical natural language processing that concerns itself directly with the problem of relating basic biomedical research to clinical practice, and vice versa. Applications of text mining fall both into the category of T1 translational research-translating basic science results into new interventions-and T2 translational research, or translational research for public health. Potential use cases include better phenotyping of research subjects, and pharmacogenomic research. A variety of methods for evaluating text mining applications exist, including corpora, structured test suites, and post hoc judging. Two basic principles of linguistic structure are relevant for building text mining applications. One is that linguistic structure consists of multiple levels. The other is that every level of linguistic structure is characterized by ambiguity. There are two basic approaches to text mining: rule-based, also known as knowledge-based; and machine-learning-based, also known as statistical. Many systems are hybrids of the two approaches. Shared tasks have had a strong effect on the direction of the field. Like all translational bioinformatics software, text mining software for translational bioinformatics can be considered health-critical and should be subject to the strictest standards of quality assurance and software testing.
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Text mining resources for the life sciences
Shardlow, Matthew; Aubin, Sophie; Bossy, Robert; Eckart de Castilho, Richard; Piperidis, Stelios; McNaught, John; Ananiadou, Sophia
2016-01-01
Text mining is a powerful technology for quickly distilling key information from vast quantities of biomedical literature. However, to harness this power the researcher must be well versed in the availability, suitability, adaptability, interoperability and comparative accuracy of current text mining resources. In this survey, we give an overview of the text mining resources that exist in the life sciences to help researchers, especially those employed in biocuration, to engage with text mining in their own work. We categorize the various resources under three sections: Content Discovery looks at where and how to find biomedical publications for text mining; Knowledge Encoding describes the formats used to represent the different levels of information associated with content that enable text mining, including those formats used to carry such information between processes; Tools and Services gives an overview of workflow management systems that can be used to rapidly configure and compare domain- and task-specific processes, via access to a wide range of pre-built tools. We also provide links to relevant repositories in each section to enable the reader to find resources relevant to their own area of interest. Throughout this work we give a special focus to resources that are interoperable—those that have the crucial ability to share information, enabling smooth integration and reusability. PMID:27888231
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The Function Biomedical Informatics Research Network Data Repository
Keator, David B.; van Erp, Theo G.M.; Turner, Jessica A.; Glover, Gary H.; Mueller, Bryon A.; Liu, Thomas T.; Voyvodic, James T.; Rasmussen, Jerod; Calhoun, Vince D.; Lee, Hyo Jong; Toga, Arthur W.; McEwen, Sarah; Ford, Judith M.; Mathalon, Daniel H.; Diaz, Michele; O’Leary, Daniel S.; Bockholt, H. Jeremy; Gadde, Syam; Preda, Adrian; Wible, Cynthia G.; Stern, Hal S.; Belger, Aysenil; McCarthy, Gregory; Ozyurt, Burak; Potkin, Steven G.
2015-01-01
The Function Biomedical Informatics Research Network (FBIRN) developed methods and tools for conducting multi-scanner functional magnetic resonance imaging (fMRI) studies. Method and tool development were based on two major goals: 1) to assess the major sources of variation in fMRI studies conducted across scanners, including instrumentation, acquisition protocols, challenge tasks, and analysis methods, and 2) to provide a distributed network infrastructure and an associated federated database to host and query large, multi-site, fMRI and clinical datasets. In the process of achieving these goals the FBIRN test bed generated several multi-scanner brain imaging data sets to be shared with the wider scientific community via the BIRN Data Repository (BDR). The FBIRN Phase 1 dataset consists of a traveling subject study of 5 healthy subjects, each scanned on 10 different 1.5 to 4 Tesla scanners. The FBIRN Phase 2 and Phase 3 datasets consist of subjects with schizophrenia or schizoaffective disorder along with healthy comparison subjects scanned at multiple sites. In this paper, we provide concise descriptions of FBIRN’s multi-scanner brain imaging data sets and details about the BIRN Data Repository instance of the Human Imaging Database (HID) used to publicly share the data. PMID:26364863
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ChemBank: a small-molecule screening and cheminformatics resource database.
Seiler, Kathleen Petri; George, Gregory A; Happ, Mary Pat; Bodycombe, Nicole E; Carrinski, Hyman A; Norton, Stephanie; Brudz, Steve; Sullivan, John P; Muhlich, Jeremy; Serrano, Martin; Ferraiolo, Paul; Tolliday, Nicola J; Schreiber, Stuart L; Clemons, Paul A
2008-01-01
ChemBank (http://chembank.broad.harvard.edu/) is a public, web-based informatics environment developed through a collaboration between the Chemical Biology Program and Platform at the Broad Institute of Harvard and MIT. This knowledge environment includes freely available data derived from small molecules and small-molecule screens and resources for studying these data. ChemBank is unique among small-molecule databases in its dedication to the storage of raw screening data, its rigorous definition of screening experiments in terms of statistical hypothesis testing, and its metadata-based organization of screening experiments into projects involving collections of related assays. ChemBank stores an increasingly varied set of measurements derived from cells and other biological assay systems treated with small molecules. Analysis tools are available and are continuously being developed that allow the relationships between small molecules, cell measurements, and cell states to be studied. Currently, ChemBank stores information on hundreds of thousands of small molecules and hundreds of biomedically relevant assays that have been performed at the Broad Institute by collaborators from the worldwide research community. The goal of ChemBank is to provide life scientists unfettered access to biomedically relevant data and tools heretofore available primarily in the private sector.
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Dizeez: An Online Game for Human Gene-Disease Annotation
Loguercio, Salvatore; Good, Benjamin M.; Su, Andrew I.
2013-01-01
Structured gene annotations are a foundation upon which many bioinformatics and statistical analyses are built. However the structured annotations available in public databases are a sparse representation of biological knowledge as a whole. The rate of biomedical data generation is such that centralized biocuration efforts struggle to keep up. New models for gene annotation need to be explored that expand the pace at which we are able to structure biomedical knowledge. Recently, online games have emerged as an effective way to recruit, engage and organize large numbers of volunteers to help address difficult biological challenges. For example, games have been successfully developed for protein folding (Foldit), multiple sequence alignment (Phylo) and RNA structure design (EteRNA). Here we present Dizeez, a simple online game built with the purpose of structuring knowledge of gene-disease associations. Preliminary results from game play online and at scientific conferences suggest that Dizeez is producing valid gene-disease annotations not yet present in any public database. These early results provide a basic proof of principle that online games can be successfully applied to the challenge of gene annotation. Dizeez is available at http://genegames.org. PMID:23951102
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Interactive publications: creation and usage
NASA Astrophysics Data System (ADS)
Thoma, George R.; Ford, Glenn; Chung, Michael; Vasudevan, Kirankumar; Antani, Sameer
2006-02-01
As envisioned here, an "interactive publication" has similarities to multimedia documents that have been in existence for a decade or more, but possesses specific differentiating characteristics. In common usage, the latter refers to online entities that, in addition to text, consist of files of images and video clips residing separately in databases, rarely providing immediate context to the document text. While an interactive publication has many media objects as does the "traditional" multimedia document, it is a self-contained document, either as a single file with media files embedded within it, or as a "folder" containing tightly linked media files. The main characteristic that differentiates an interactive publication from a traditional multimedia document is that the reader would be able to reuse the media content for analysis and presentation, and to check the underlying data and possibly derive alternative conclusions leading, for example, to more in-depth peer reviews. We have created prototype publications containing paginated text and several media types encountered in the biomedical literature: 3D animations of anatomic structures; graphs, charts and tabular data; cell development images (video sequences); and clinical images such as CT, MRI and ultrasound in the DICOM format. This paper presents developments to date including: a tool to convert static tables or graphs into interactive entities, authoring procedures followed to create prototypes, and advantages and drawbacks of each of these platforms. It also outlines future work including meeting the challenge of network distribution for these large files.
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Abbreviation definition identification based on automatic precision estimates.
Sohn, Sunghwan; Comeau, Donald C; Kim, Won; Wilbur, W John
2008-09-25
The rapid growth of biomedical literature presents challenges for automatic text processing, and one of the challenges is abbreviation identification. The presence of unrecognized abbreviations in text hinders indexing algorithms and adversely affects information retrieval and extraction. Automatic abbreviation definition identification can help resolve these issues. However, abbreviations and their definitions identified by an automatic process are of uncertain validity. Due to the size of databases such as MEDLINE only a small fraction of abbreviation-definition pairs can be examined manually. An automatic way to estimate the accuracy of abbreviation-definition pairs extracted from text is needed. In this paper we propose an abbreviation definition identification algorithm that employs a variety of strategies to identify the most probable abbreviation definition. In addition our algorithm produces an accuracy estimate, pseudo-precision, for each strategy without using a human-judged gold standard. The pseudo-precisions determine the order in which the algorithm applies the strategies in seeking to identify the definition of an abbreviation. On the Medstract corpus our algorithm produced 97% precision and 85% recall which is higher than previously reported results. We also annotated 1250 randomly selected MEDLINE records as a gold standard. On this set we achieved 96.5% precision and 83.2% recall. This compares favourably with the well known Schwartz and Hearst algorithm. We developed an algorithm for abbreviation identification that uses a variety of strategies to identify the most probable definition for an abbreviation and also produces an estimated accuracy of the result. This process is purely automatic.
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GNormPlus: An Integrative Approach for Tagging Genes, Gene Families, and Protein Domains
Lu, Zhiyong
2015-01-01
The automatic recognition of gene names and their associated database identifiers from biomedical text has been widely studied in recent years, as these tasks play an important role in many downstream text-mining applications. Despite significant previous research, only a small number of tools are publicly available and these tools are typically restricted to detecting only mention level gene names or only document level gene identifiers. In this work, we report GNormPlus: an end-to-end and open source system that handles both gene mention and identifier detection. We created a new corpus of 694 PubMed articles to support our development of GNormPlus, containing manual annotations for not only gene names and their identifiers, but also closely related concepts useful for gene name disambiguation, such as gene families and protein domains. GNormPlus integrates several advanced text-mining techniques, including SimConcept for resolving composite gene names. As a result, GNormPlus compares favorably to other state-of-the-art methods when evaluated on two widely used public benchmarking datasets, achieving 86.7% F1-score on the BioCreative II Gene Normalization task dataset and 50.1% F1-score on the BioCreative III Gene Normalization task dataset. The GNormPlus source code and its annotated corpus are freely available, and the results of applying GNormPlus to the entire PubMed are freely accessible through our web-based tool PubTator. PMID:26380306
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Pharmacovigilance and Biomedical Informatics: A Model for Future Development.
Beninger, Paul; Ibara, Michael A
2016-12-01
The discipline of pharmacovigilance is rooted in the aftermath of the thalidomide tragedy of 1961. It has evolved as a result of collaborative efforts by many individuals and organizations, including physicians, patients, Health Authorities, universities, industry, the World Health Organization, the Council for International Organizations of Medical Sciences, and the International Conference on Harmonisation. Biomedical informatics is rooted in technologically based methodologies and has evolved at the speed of computer technology. The purpose of this review is to bring a novel lens to pharmacovigilance, looking at the evolution and development of the field of pharmacovigilance from the perspective of biomedical informatics, with the explicit goal of providing a foundation for discussion of the future direction of pharmacovigilance as a discipline. For this review, we searched [publication trend for the log 10 value of the numbers of publications identified in PubMed] using the key words [informatics (INF), pharmacovigilance (PV), phar-macovigilance þ informatics (PV þ INF)], for [study types] articles published between [1994-2015]. We manually searched the reference lists of identified articles for additional information. Biomedical informatics has made significant contributions to the infrastructural development of pharmacovigilance. However, there has not otherwise been a systematic assessment of the role of biomedical informatics in enhancing the field of pharmacovigilance, and there has been little cross-discipline scholarship. Rapidly developing innovations in biomedical informatics pose a challenge to pharmacovigilance in finding ways to include new sources of safety information, including social media, massively linked databases, and mobile and wearable wellness applications and sensors. With biomedical informatics as a lens, it is evident that certain aspects of pharmacovigilance are evolving more slowly. However, the high levels of mutual interest in both fields and intense global and economic external pressures offer opportunities for a future of closer collaboration. Copyright © 2016 Elsevier HS Journals, Inc. All rights reserved.
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Evaluation and Cross-Comparison of Lexical Entities of Biological Interest (LexEBI)
Rebholz-Schuhmann, Dietrich; Kim, Jee-Hyub; Yan, Ying; Dixit, Abhishek; Friteyre, Caroline; Hoehndorf, Robert; Backofen, Rolf; Lewin, Ian
2013-01-01
Motivation Biomedical entities, their identifiers and names, are essential in the representation of biomedical facts and knowledge. In the same way, the complete set of biomedical and chemical terms, i.e. the biomedical “term space” (the “Lexeome”), forms a key resource to achieve the full integration of the scientific literature with biomedical data resources: any identified named entity can immediately be normalized to the correct database entry. This goal does not only require that we are aware of all existing terms, but would also profit from knowing all their senses and their semantic interpretation (ambiguities, nestedness). Result This study compiles a resource for lexical terms of biomedical interest in a standard format (called “LexEBI”), determines the overall number of terms, their reuse in different resources and the nestedness of terms. LexEBI comprises references for protein and gene entries and their term variants and chemical entities amongst other terms. In addition, disease terms have been identified from Medline and PubmedCentral and added to LexEBI. Our analysis demonstrates that the baseforms of terms from the different semantic types show only little polysemous use. Nonetheless, the term variants of protein and gene names (PGNs) frequently contain species mentions, which should have been avoided according to protein annotation guidelines. Furthermore, the protein and gene entities as well as the chemical entities, both do comprise enzymes leading to hierarchical polysemy, and a large portion of PGNs make reference to a chemical entity. Altogether, according to our analysis based on the Medline distribution, 401,869 unique PGNs in the documents contain a reference to 25,022 chemical entities, 3,125 disease terms or 1,576 species mentions. Conclusion LexEBI delivers the complete biomedical and chemical Lexeome in a standardized representation (http://www.ebi.ac.uk/Rebholz-srv/LexEBI/). The resource provides the disease terms as open source content, and fully interlinks terms across resources. PMID:24124474
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VarioML framework for comprehensive variation data representation and exchange.
Byrne, Myles; Fokkema, Ivo Fac; Lancaster, Owen; Adamusiak, Tomasz; Ahonen-Bishopp, Anni; Atlan, David; Béroud, Christophe; Cornell, Michael; Dalgleish, Raymond; Devereau, Andrew; Patrinos, George P; Swertz, Morris A; Taschner, Peter Em; Thorisson, Gudmundur A; Vihinen, Mauno; Brookes, Anthony J; Muilu, Juha
2012-10-03
Sharing of data about variation and the associated phenotypes is a critical need, yet variant information can be arbitrarily complex, making a single standard vocabulary elusive and re-formatting difficult. Complex standards have proven too time-consuming to implement. The GEN2PHEN project addressed these difficulties by developing a comprehensive data model for capturing biomedical observations, Observ-OM, and building the VarioML format around it. VarioML pairs a simplified open specification for describing variants, with a toolkit for adapting the specification into one's own research workflow. Straightforward variant data can be captured, federated, and exchanged with no overhead; more complex data can be described, without loss of compatibility. The open specification enables push-button submission to gene variant databases (LSDBs) e.g., the Leiden Open Variation Database, using the Cafe Variome data publishing service, while VarioML bidirectionally transforms data between XML and web-application code formats, opening up new possibilities for open source web applications building on shared data. A Java implementation toolkit makes VarioML easily integrated into biomedical applications. VarioML is designed primarily for LSDB data submission and transfer scenarios, but can also be used as a standard variation data format for JSON and XML document databases and user interface components. VarioML is a set of tools and practices improving the availability, quality, and comprehensibility of human variation information. It enables researchers, diagnostic laboratories, and clinics to share that information with ease, clarity, and without ambiguity.
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VarioML framework for comprehensive variation data representation and exchange
2012-01-01
Background Sharing of data about variation and the associated phenotypes is a critical need, yet variant information can be arbitrarily complex, making a single standard vocabulary elusive and re-formatting difficult. Complex standards have proven too time-consuming to implement. Results The GEN2PHEN project addressed these difficulties by developing a comprehensive data model for capturing biomedical observations, Observ-OM, and building the VarioML format around it. VarioML pairs a simplified open specification for describing variants, with a toolkit for adapting the specification into one's own research workflow. Straightforward variant data can be captured, federated, and exchanged with no overhead; more complex data can be described, without loss of compatibility. The open specification enables push-button submission to gene variant databases (LSDBs) e.g., the Leiden Open Variation Database, using the Cafe Variome data publishing service, while VarioML bidirectionally transforms data between XML and web-application code formats, opening up new possibilities for open source web applications building on shared data. A Java implementation toolkit makes VarioML easily integrated into biomedical applications. VarioML is designed primarily for LSDB data submission and transfer scenarios, but can also be used as a standard variation data format for JSON and XML document databases and user interface components. Conclusions VarioML is a set of tools and practices improving the availability, quality, and comprehensibility of human variation information. It enables researchers, diagnostic laboratories, and clinics to share that information with ease, clarity, and without ambiguity. PMID:23031277
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Telescience Support Center Data System Software
NASA Technical Reports Server (NTRS)
Rahman, Hasan
2010-01-01
The Telescience Support Center (TSC) team has developed a databasedriven, increment-specific Data Require - ment Document (DRD) generation tool that automates much of the work required for generating and formatting the DRD. It creates a database to load the required changes to configure the TSC data system, thus eliminating a substantial amount of labor in database entry and formatting. The TSC database contains the TSC systems configuration, along with the experimental data, in which human physiological data must be de-commutated in real time. The data for each experiment also must be cataloged and archived for future retrieval. TSC software provides tools and resources for ground operation and data distribution to remote users consisting of PIs (principal investigators), bio-medical engineers, scientists, engineers, payload specialists, and computer scientists. Operations support is provided for computer systems access, detailed networking, and mathematical and computational problems of the International Space Station telemetry data. User training is provided for on-site staff and biomedical researchers and other remote personnel in the usage of the space-bound services via the Internet, which enables significant resource savings for the physical facility along with the time savings versus traveling to NASA sites. The software used in support of the TSC could easily be adapted to other Control Center applications. This would include not only other NASA payload monitoring facilities, but also other types of control activities, such as monitoring and control of the electric grid, chemical, or nuclear plant processes, air traffic control, and the like.
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Pérez Rodríguez, Roberto; Anido Rifón, Luis E.
2015-01-01
Automatic classification of text documents into a set of categories has a lot of applications. Among those applications, the automatic classification of biomedical literature stands out as an important application for automatic document classification strategies. Biomedical staff and researchers have to deal with a lot of literature in their daily activities, so it would be useful a system that allows for accessing to documents of interest in a simple and effective way; thus, it is necessary that these documents are sorted based on some criteria—that is to say, they have to be classified. Documents to classify are usually represented following the bag-of-words (BoW) paradigm. Features are words in the text—thus suffering from synonymy and polysemy—and their weights are just based on their frequency of occurrence. This paper presents an empirical study of the efficiency of a classifier that leverages encyclopedic background knowledge—concretely Wikipedia—in order to create bag-of-concepts (BoC) representations of documents, understanding concept as “unit of meaning”, and thus tackling synonymy and polysemy. Besides, the weighting of concepts is based on their semantic relevance in the text. For the evaluation of the proposal, empirical experiments have been conducted with one of the commonly used corpora for evaluating classification and retrieval of biomedical information, OHSUMED, and also with a purpose-built corpus of MEDLINE biomedical abstracts, UVigoMED. Results obtained show that the Wikipedia-based bag-of-concepts representation outperforms the classical bag-of-words representation up to 157% in the single-label classification problem and up to 100% in the multi-label problem for OHSUMED corpus, and up to 122% in the single-label classification problem and up to 155% in the multi-label problem for UVigoMED corpus. PMID:26468436
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Curation accuracy of model organism databases
Keseler, Ingrid M.; Skrzypek, Marek; Weerasinghe, Deepika; Chen, Albert Y.; Fulcher, Carol; Li, Gene-Wei; Lemmer, Kimberly C.; Mladinich, Katherine M.; Chow, Edmond D.; Sherlock, Gavin; Karp, Peter D.
2014-01-01
Manual extraction of information from the biomedical literature—or biocuration—is the central methodology used to construct many biological databases. For example, the UniProt protein database, the EcoCyc Escherichia coli database and the Candida Genome Database (CGD) are all based on biocuration. Biological databases are used extensively by life science researchers, as online encyclopedias, as aids in the interpretation of new experimental data and as golden standards for the development of new bioinformatics algorithms. Although manual curation has been assumed to be highly accurate, we are aware of only one previous study of biocuration accuracy. We assessed the accuracy of EcoCyc and CGD by manually selecting curated assertions within randomly chosen EcoCyc and CGD gene pages and by then validating that the data found in the referenced publications supported those assertions. A database assertion is considered to be in error if that assertion could not be found in the publication cited for that assertion. We identified 10 errors in the 633 facts that we validated across the two databases, for an overall error rate of 1.58%, and individual error rates of 1.82% for CGD and 1.40% for EcoCyc. These data suggest that manual curation of the experimental literature by Ph.D-level scientists is highly accurate. Database URL: http://ecocyc.org/, http://www.candidagenome.org// PMID:24923819
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The Protein Disease Database of human body fluids: II. Computer methods and data issues.
Lemkin, P F; Orr, G A; Goldstein, M P; Creed, G J; Myrick, J E; Merril, C R
1995-01-01
The Protein Disease Database (PDD) is a relational database of proteins and diseases. With this database it is possible to screen for quantitative protein abnormalities associated with disease states. These quantitative relationships use data drawn from the peer-reviewed biomedical literature. Assays may also include those observed in high-resolution electrophoretic gels that offer the potential to quantitate many proteins in a single test as well as data gathered by enzymatic or immunologic assays. We are using the Internet World Wide Web (WWW) and the Web browser paradigm as an access method for wide distribution and querying of the Protein Disease Database. The WWW hypertext transfer protocol and its Common Gateway Interface make it possible to build powerful graphical user interfaces that can support easy-to-use data retrieval using query specification forms or images. The details of these interactions are totally transparent to the users of these forms. Using a client-server SQL relational database, user query access, initial data entry and database maintenance are all performed over the Internet with a Web browser. We discuss the underlying design issues, mapping mechanisms and assumptions that we used in constructing the system, data entry, access to the database server, security, and synthesis of derived two-dimensional gel image maps and hypertext documents resulting from SQL database searches.
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Biomedical data mining in clinical routine: expanding the impact of hospital information systems.
Müller, Marcel; Markó, Kornel; Daumke, Philipp; Paetzold, Jan; Roesner, Arnold; Klar, Rüdiger
2007-01-01
In this paper we want to describe how the promising technology of biomedical data mining can improve the use of hospital information systems: a large set of unstructured, narrative clinical data from a dermatological university hospital like discharge letters or other dermatological reports were processed through a morpho-semantic text retrieval engine ("MorphoSaurus") and integrated with other clinical data using a web-based interface and brought into daily clinical routine. The user evaluation showed a very high user acceptance - this system seems to meet the clinicians' requirements for a vertical data mining in the electronic patient records. What emerges is the need for integration of biomedical data mining into hospital information systems for clinical, scientific, educational and economic reasons.
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ERIC Educational Resources Information Center
Cotton, P. L.
1987-01-01
Defines two types of online databases: source, referring to those intended to be complete in themselves, whether full-text or abstracts; and bibliographic, meaning those that are not complete. Predictions are made about the future growth rate of these two types of databases, as well as full-text versus abstract databases. (EM)
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Automated Database Mediation Using Ontological Metadata Mappings
Marenco, Luis; Wang, Rixin; Nadkarni, Prakash
2009-01-01
Objective To devise an automated approach for integrating federated database information using database ontologies constructed from their extended metadata. Background One challenge of database federation is that the granularity of representation of equivalent data varies across systems. Dealing effectively with this problem is analogous to dealing with precoordinated vs. postcoordinated concepts in biomedical ontologies. Model Description The authors describe an approach based on ontological metadata mapping rules defined with elements of a global vocabulary, which allows a query specified at one granularity level to fetch data, where possible, from databases within the federation that use different granularities. This is implemented in OntoMediator, a newly developed production component of our previously described Query Integrator System. OntoMediator's operation is illustrated with a query that accesses three geographically separate, interoperating databases. An example based on SNOMED also illustrates the applicability of high-level rules to support the enforcement of constraints that can prevent inappropriate curator or power-user actions. Summary A rule-based framework simplifies the design and maintenance of systems where categories of data must be mapped to each other, for the purpose of either cross-database query or for curation of the contents of compositional controlled vocabularies. PMID:19567801
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The 2018 Nucleic Acids Research database issue and the online molecular biology database collection.
Rigden, Daniel J; Fernández, Xosé M
2018-01-04
The 2018 Nucleic Acids Research Database Issue contains 181 papers spanning molecular biology. Among them, 82 are new and 84 are updates describing resources that appeared in the Issue previously. The remaining 15 cover databases most recently published elsewhere. Databases in the area of nucleic acids include 3DIV for visualisation of data on genome 3D structure and RNArchitecture, a hierarchical classification of RNA families. Protein databases include the established SMART, ELM and MEROPS while GPCRdb and the newcomer STCRDab cover families of biomedical interest. In the area of metabolism, HMDB and Reactome both report new features while PULDB appears in NAR for the first time. This issue also contains reports on genomics resources including Ensembl, the UCSC Genome Browser and ENCODE. Update papers from the IUPHAR/BPS Guide to Pharmacology and DrugBank are highlights of the drug and drug target section while a number of proteomics databases including proteomicsDB are also covered. The entire Database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). The NAR online Molecular Biology Database Collection has been updated, reviewing 138 entries, adding 88 new resources and eliminating 47 discontinued URLs, bringing the current total to 1737 databases. It is available at http://www.oxfordjournals.org/nar/database/c/. © The Author(s) 2018. Published by Oxford University Press on behalf of Nucleic Acids Research.
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Jin, Yinghui; Tian, Jinhui; Sun, Mei; Yang, Kehu
2011-02-01
The purpose of this systematic review was to establish whether warmed irrigation fluid temperature could decrease the drop of body temperature and incidence of shivering and hypothermia. Irrigation fluid, which is used in large quantities during endoscopic surgeries at room temperature, is considered to be associated with hypothermia and shivering. It remains controversial whether using warmed irrigation fluid to replace room-temperature irrigation fluid will decrease the drop of core body temperature and the occurrence of hypothermia. A comprehensive search (computerised database searches, footnote chasing, citation chasing) was undertaken to identify all the randomised controlled trials that explored temperature of irrigation fluid in endoscopic surgery. An approach involving meta-analysis was used. We searched PubMed, EMBASE, Cochrane Library, SCI, China academic journals full-text databases, Chinese Biomedical Literature Database, Chinese scientific journals databases and Chinese Medical Association Journals for trials that meet the inclusion criteria. Study quality was assessed using standards recommended by Cochrane Library Handbook 5.0.1. Disagreement was resolved by consensus. Thirteen randomised controlled trials including 686 patients were identified. The results showed that room-temperature irrigation fluid caused a greater drop of core body temperature in patients, compared to warmed irrigation fluid (p < 0.00001; I(2) = 85%). The occurrence of shivering [odds ratio (OR) 5.13, 95% CI: 2.95-10.19, p < 0.00001; I(2) = 0%] and hypothermia (OR 22.01, 95% CI: 2.03-197.08, p = 0.01; I(2) = 64%) in the groups having warmed irrigation fluid were lower than the group of studies having room-temperature fluid. In endoscopic surgeries, irrigation fluid is recommended to be warmed to decrease the drop of core body temperature and the risk of perioperative shivering and hypothermia. Warming irrigating fluid should be considered standard practice in all endoscopic surgeries. © 2011 Blackwell Publishing Ltd.
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Hu, Hai; Brzeski, Henry; Hutchins, Joe; Ramaraj, Mohan; Qu, Long; Xiong, Richard; Kalathil, Surendran; Kato, Rand; Tenkillaya, Santhosh; Carney, Jerry; Redd, Rosann; Arkalgudvenkata, Sheshkumar; Shahzad, Kashif; Scott, Richard; Cheng, Hui; Meadow, Stephen; McMichael, John; Sheu, Shwu-Lin; Rosendale, David; Kvecher, Leonid; Ahern, Stephen; Yang, Song; Zhang, Yonghong; Jordan, Rick; Somiari, Stella B; Hooke, Jeffrey; Shriver, Craig D; Somiari, Richard I; Liebman, Michael N
2004-10-01
The Windber Research Institute is an integrated high-throughput research center employing clinical, genomic and proteomic platforms to produce terabyte levels of data. We use biomedical informatics technologies to integrate all of these operations. This report includes information on a multi-year, multi-phase hybrid data warehouse project currently under development in the Institute. The purpose of the warehouse is to host the terabyte-level of internal experimentally generated data as well as data from public sources. We have previously reported on the phase I development, which integrated limited internal data sources and selected public databases. Currently, we are completing phase II development, which integrates our internal automated data sources and develops visualization tools to query across these data types. This paper summarizes our clinical and experimental operations, the data warehouse development, and the challenges we have faced. In phase III we plan to federate additional manual internal and public data sources and then to develop and adapt more data analysis and mining tools. We expect that the final implementation of the data warehouse will greatly facilitate biomedical informatics research.
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GOClonto: an ontological clustering approach for conceptualizing PubMed abstracts.
Zheng, Hai-Tao; Borchert, Charles; Kim, Hong-Gee
2010-02-01
Concurrent with progress in biomedical sciences, an overwhelming of textual knowledge is accumulating in the biomedical literature. PubMed is the most comprehensive database collecting and managing biomedical literature. To help researchers easily understand collections of PubMed abstracts, numerous clustering methods have been proposed to group similar abstracts based on their shared features. However, most of these methods do not explore the semantic relationships among groupings of documents, which could help better illuminate the groupings of PubMed abstracts. To address this issue, we proposed an ontological clustering method called GOClonto for conceptualizing PubMed abstracts. GOClonto uses latent semantic analysis (LSA) and gene ontology (GO) to identify key gene-related concepts and their relationships as well as allocate PubMed abstracts based on these key gene-related concepts. Based on two PubMed abstract collections, the experimental results show that GOClonto is able to identify key gene-related concepts and outperforms the STC (suffix tree clustering) algorithm, the Lingo algorithm, the Fuzzy Ants algorithm, and the clustering based TRS (tolerance rough set) algorithm. Moreover, the two ontologies generated by GOClonto show significant informative conceptual structures.
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Dunn, Adam G; Coiera, Enrico; Mandl, Kenneth D
2014-04-22
In 2014, the vast majority of published biomedical research is still hidden behind paywalls rather than open access. For more than a decade, similar restrictions over other digitally available content have engendered illegal activity. Music file sharing became rampant in the late 1990s as communities formed around new ways to share. The frequency and scale of cyber-attacks against commercial and government interests has increased dramatically. Massive troves of classified government documents have become public through the actions of a few. Yet we have not seen significant growth in the illegal sharing of peer-reviewed academic articles. Should we truly expect that biomedical publishing is somehow at less risk than other content-generating industries? What of the larger threat--a "Biblioleaks" event--a database breach and public leak of the substantial archives of biomedical literature? As the expectation that all research should be available to everyone becomes the norm for a younger generation of researchers and the broader community, the motivations for such a leak are likely to grow. We explore the feasibility and consequences of a Biblioleaks event for researchers, journals, publishers, and the broader communities of doctors and the patients they serve.
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An active visual search interface for Medline.
Xuan, Weijian; Dai, Manhong; Mirel, Barbara; Wilson, Justin; Athey, Brian; Watson, Stanley J; Meng, Fan
2007-01-01
Searching the Medline database is almost a daily necessity for many biomedical researchers. However, available Medline search solutions are mainly designed for the quick retrieval of a small set of most relevant documents. Because of this search model, they are not suitable for the large-scale exploration of literature and the underlying biomedical conceptual relationships, which are common tasks in the age of high throughput experimental data analysis and cross-discipline research. We try to develop a new Medline exploration approach by incorporating interactive visualization together with powerful grouping, summary, sorting and active external content retrieval functions. Our solution, PubViz, is based on the FLEX platform designed for interactive web applications and its prototype is publicly available at: http://brainarray.mbni.med.umich.edu/Brainarray/DataMining/PubViz.
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A Scalable Data Access Layer to Manage Structured Heterogeneous Biomedical Data.
Delussu, Giovanni; Lianas, Luca; Frexia, Francesca; Zanetti, Gianluigi
2016-01-01
This work presents a scalable data access layer, called PyEHR, designed to support the implementation of data management systems for secondary use of structured heterogeneous biomedical and clinical data. PyEHR adopts the openEHR's formalisms to guarantee the decoupling of data descriptions from implementation details and exploits structure indexing to accelerate searches. Data persistence is guaranteed by a driver layer with a common driver interface. Interfaces for two NoSQL Database Management Systems are already implemented: MongoDB and Elasticsearch. We evaluated the scalability of PyEHR experimentally through two types of tests, called "Constant Load" and "Constant Number of Records", with queries of increasing complexity on synthetic datasets of ten million records each, containing very complex openEHR archetype structures, distributed on up to ten computing nodes.
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Keyless Entry: Building a Text Database Using OCR Technology.
ERIC Educational Resources Information Center
Grotophorst, Clyde W.
1989-01-01
Discusses the use of optical character recognition (OCR) technology to produce an ASCII text database. A tutorial on digital scanning and OCR is provided, and a systems integration project which used the Calera CDP-3000XF scanner and text retrieval software to construct a database of dissertations at George Mason University is described. (four…