A text-based data mining and toxicity prediction modeling system for a clinical decision support in radiation oncology: A preliminary study

NASA Astrophysics Data System (ADS)

Kim, Kwang Hyeon; Lee, Suk; Shim, Jang Bo; Chang, Kyung Hwan; Yang, Dae Sik; Yoon, Won Sup; Park, Young Je; Kim, Chul Yong; Cao, Yuan Jie

2017-08-01

The aim of this study is an integrated research for text-based data mining and toxicity prediction modeling system for clinical decision support system based on big data in radiation oncology as a preliminary research. The structured and unstructured data were prepared by treatment plans and the unstructured data were extracted by dose-volume data image pattern recognition of prostate cancer for research articles crawling through the internet. We modeled an artificial neural network to build a predictor model system for toxicity prediction of organs at risk. We used a text-based data mining approach to build the artificial neural network model for bladder and rectum complication predictions. The pattern recognition method was used to mine the unstructured toxicity data for dose-volume at the detection accuracy of 97.9%. The confusion matrix and training model of the neural network were achieved with 50 modeled plans (n = 50) for validation. The toxicity level was analyzed and the risk factors for 25% bladder, 50% bladder, 20% rectum, and 50% rectum were calculated by the artificial neural network algorithm. As a result, 32 plans could cause complication but 18 plans were designed as non-complication among 50 modeled plans. We integrated data mining and a toxicity modeling method for toxicity prediction using prostate cancer cases. It is shown that a preprocessing analysis using text-based data mining and prediction modeling can be expanded to personalized patient treatment decision support based on big data.

Agile text mining for the 2014 i2b2/UTHealth Cardiac risk factors challenge.

PubMed

Cormack, James; Nath, Chinmoy; Milward, David; Raja, Kalpana; Jonnalagadda, Siddhartha R

2015-12-01

This paper describes the use of an agile text mining platform (Linguamatics' Interactive Information Extraction Platform, I2E) to extract document-level cardiac risk factors in patient records as defined in the i2b2/UTHealth 2014 challenge. The approach uses a data-driven rule-based methodology with the addition of a simple supervised classifier. We demonstrate that agile text mining allows for rapid optimization of extraction strategies, while post-processing can leverage annotation guidelines, corpus statistics and logic inferred from the gold standard data. We also show how data imbalance in a training set affects performance. Evaluation of this approach on the test data gave an F-Score of 91.7%, one percent behind the top performing system. Copyright © 2015 Elsevier Inc. All rights reserved.

Complex Event Processing for Content-Based Text, Image, and Video Retrieval

DTIC Science & Technology

2016-06-01

NY): Wiley- Interscience; 2000. Feldman R, Sanger J. The text mining handbook: advanced approaches in analyzing unstructured data. New York (NY...ARL-TR-7705 ● JUNE 2016 US Army Research Laboratory Complex Event Processing for Content-Based Text , Image, and Video Retrieval...ARL-TR-7705 ● JUNE 2016 US Army Research Laboratory Complex Event Processing for Content-Based Text , Image, and Video Retrieval

Compatibility between Text Mining and Qualitative Research in the Perspectives of Grounded Theory, Content Analysis, and Reliability

ERIC Educational Resources Information Center

Yu, Chong Ho; Jannasch-Pennell, Angel; DiGangi, Samuel

2011-01-01

The objective of this article is to illustrate that text mining and qualitative research are epistemologically compatible. First, like many qualitative research approaches, such as grounded theory, text mining encourages open-mindedness and discourages preconceptions. Contrary to the popular belief that text mining is a linear and fully automated…

An Enhanced Text-Mining Framework for Extracting Disaster Relevant Data through Social Media and Remote Sensing Data Fusion

NASA Astrophysics Data System (ADS)

Scheele, C. J.; Huang, Q.

2016-12-01

In the past decade, the rise in social media has led to the development of a vast number of social media services and applications. Disaster management represents one of such applications leveraging massive data generated for event detection, response, and recovery. In order to find disaster relevant social media data, current approaches utilize natural language processing (NLP) methods based on keywords, or machine learning algorithms relying on text only. However, these approaches cannot be perfectly accurate due to the variability and uncertainty in language used on social media. To improve current methods, the enhanced text-mining framework is proposed to incorporate location information from social media and authoritative remote sensing datasets for detecting disaster relevant social media posts, which are determined by assessing the textual content using common text mining methods and how the post relates spatiotemporally to the disaster event. To assess the framework, geo-tagged Tweets were collected for three different spatial and temporal disaster events: hurricane, flood, and tornado. Remote sensing data and products for each event were then collected using RealEarthTM. Both Naive Bayes and Logistic Regression classifiers were used to compare the accuracy within the enhanced text-mining framework. Finally, the accuracies from the enhanced text-mining framework were compared to the current text-only methods for each of the case study disaster events. The results from this study address the need for more authoritative data when using social media in disaster management applications.

Introducing Text Analytics as a Graduate Business School Course

ERIC Educational Resources Information Center

Edgington, Theresa M.

2011-01-01

Text analytics refers to the process of analyzing unstructured data from documented sources, including open-ended surveys, blogs, and other types of web dialog. Text analytics has enveloped the concept of text mining, an analysis approach influenced heavily from data mining. While text mining has been covered extensively in various computer…

Gene Prioritization of Resistant Rice Gene against Xanthomas oryzae pv. oryzae by Using Text Mining Technologies

PubMed Central

Xia, Jingbo; Zhang, Xing; Yuan, Daojun; Chen, Lingling; Webster, Jonathan; Fang, Alex Chengyu

2013-01-01

To effectively assess the possibility of the unknown rice protein resistant to Xanthomonas oryzae pv. oryzae, a hybrid strategy is proposed to enhance gene prioritization by combining text mining technologies with a sequence-based approach. The text mining technique of term frequency inverse document frequency is used to measure the importance of distinguished terms which reflect biomedical activity in rice before candidate genes are screened and vital terms are produced. Afterwards, a built-in classifier under the chaos games representation algorithm is used to sieve the best possible candidate gene. Our experiment results show that the combination of these two methods achieves enhanced gene prioritization. PMID:24371834

Gene prioritization of resistant rice gene against Xanthomas oryzae pv. oryzae by using text mining technologies.

PubMed

Xia, Jingbo; Zhang, Xing; Yuan, Daojun; Chen, Lingling; Webster, Jonathan; Fang, Alex Chengyu

2013-01-01

To effectively assess the possibility of the unknown rice protein resistant to Xanthomonas oryzae pv. oryzae, a hybrid strategy is proposed to enhance gene prioritization by combining text mining technologies with a sequence-based approach. The text mining technique of term frequency inverse document frequency is used to measure the importance of distinguished terms which reflect biomedical activity in rice before candidate genes are screened and vital terms are produced. Afterwards, a built-in classifier under the chaos games representation algorithm is used to sieve the best possible candidate gene. Our experiment results show that the combination of these two methods achieves enhanced gene prioritization.

DISEASES: text mining and data integration of disease-gene associations.

PubMed

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.

A Text-Mining Framework for Supporting Systematic Reviews.

PubMed

Li, Dingcheng; Wang, Zhen; Wang, Liwei; Sohn, Sunghwan; Shen, Feichen; Murad, Mohammad Hassan; Liu, Hongfang

2016-11-01

Systematic reviews (SRs) involve the identification, appraisal, and synthesis of all relevant studies for focused questions in a structured reproducible manner. High-quality SRs follow strict procedures and require significant resources and time. We investigated advanced text-mining approaches to reduce the burden associated with abstract screening in SRs and provide high-level information summary. A text-mining SR supporting framework consisting of three self-defined semantics-based ranking metrics was proposed, including keyword relevance, indexed-term relevance and topic relevance. Keyword relevance is based on the user-defined keyword list used in the search strategy. Indexed-term relevance is derived from indexed vocabulary developed by domain experts used for indexing journal articles and books. Topic relevance is defined as the semantic similarity among retrieved abstracts in terms of topics generated by latent Dirichlet allocation, a Bayesian-based model for discovering topics. We tested the proposed framework using three published SRs addressing a variety of topics (Mass Media Interventions, Rectal Cancer and Influenza Vaccine). The results showed that when 91.8%, 85.7%, and 49.3% of the abstract screening labor was saved, the recalls were as high as 100% for the three cases; respectively. Relevant studies identified manually showed strong topic similarity through topic analysis, which supported the inclusion of topic analysis as relevance metric. It was demonstrated that advanced text mining approaches can significantly reduce the abstract screening labor of SRs and provide an informative summary of relevant studies.

Text Mining Improves Prediction of Protein Functional Sites

PubMed Central

Cohn, Judith D.; Ravikumar, Komandur E.

2012-01-01

We present an approach that integrates protein structure analysis and text mining for protein functional site prediction, called LEAP-FS (Literature Enhanced Automated Prediction of Functional Sites). The structure analysis was carried out using Dynamics Perturbation Analysis (DPA), which predicts functional sites at control points where interactions greatly perturb protein vibrations. The text mining extracts mentions of residues in the literature, and predicts that residues mentioned are functionally important. We assessed the significance of each of these methods by analyzing their performance in finding known functional sites (specifically, small-molecule binding sites and catalytic sites) in about 100,000 publicly available protein structures. The DPA predictions recapitulated many of the functional site annotations and preferentially recovered binding sites annotated as biologically relevant vs. those annotated as potentially spurious. The text-based predictions were also substantially supported by the functional site annotations: compared to other residues, residues mentioned in text were roughly six times more likely to be found in a functional site. The overlap of predictions with annotations improved when the text-based and structure-based methods agreed. Our analysis also yielded new high-quality predictions of many functional site residues that were not catalogued in the curated data sources we inspected. We conclude that both DPA and text mining independently provide valuable high-throughput protein functional site predictions, and that integrating the two methods using LEAP-FS further improves the quality of these predictions. PMID:22393388

Ion Channel ElectroPhysiology Ontology (ICEPO) - a case study of text mining assisted ontology development.

PubMed

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.

Facilitating Decision Making, Re-Use and Collaboration: A Knowledge Management Approach to Acquisition Program Self-Awareness

DTIC Science & Technology

2009-06-01

capabilities: web-based, relational/multi-dimensional, client/server, and metadata (data about data) inclusion (pp. 39-40). Text mining, on the other...and Organizational Systems ( CASOS ) (Carley, 2005). Although AutoMap can be used to conduct text-mining, it was utilized only for its visualization...provides insight into how the GMCOI is using the terms, and where there might be redundant terms and need for de -confliction and standardization

Graph-based biomedical text summarization: An itemset mining and sentence clustering approach.

PubMed

Nasr Azadani, Mozhgan; Ghadiri, Nasser; Davoodijam, Ensieh

2018-06-12

Automatic text summarization offers an efficient solution to access the ever-growing amounts of both scientific and clinical literature in the biomedical domain by summarizing the source documents while maintaining their most informative contents. In this paper, we propose a novel graph-based summarization method that takes advantage of the domain-specific knowledge and a well-established data mining technique called frequent itemset mining. Our summarizer exploits the Unified Medical Language System (UMLS) to construct a concept-based model of the source document and mapping the document to the concepts. Then, it discovers frequent itemsets to take the correlations among multiple concepts into account. The method uses these correlations to propose a similarity function based on which a represented graph is constructed. The summarizer then employs a minimum spanning tree based clustering algorithm to discover various subthemes of the document. Eventually, it generates the final summary by selecting the most informative and relative sentences from all subthemes within the text. We perform an automatic evaluation over a large number of summaries using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. The results demonstrate that the proposed summarization system outperforms various baselines and benchmark approaches. The carried out research suggests that the incorporation of domain-specific knowledge and frequent itemset mining equips the summarization system in a better way to address the informativeness measurement of the sentences. Moreover, clustering the graph nodes (sentences) can enable the summarizer to target different main subthemes of a source document efficiently. The evaluation results show that the proposed approach can significantly improve the performance of the summarization systems in the biomedical domain. Copyright © 2018. Published by Elsevier Inc.

Automatic target validation based on neuroscientific literature mining for tractography

PubMed Central

Vasques, Xavier; Richardet, Renaud; Hill, Sean L.; Slater, David; Chappelier, Jean-Cedric; Pralong, Etienne; Bloch, Jocelyne; Draganski, Bogdan; Cif, Laura

2015-01-01

Target identification for tractography studies requires solid anatomical knowledge validated by an extensive literature review across species for each seed structure to be studied. Manual literature review to identify targets for a given seed region is tedious and potentially subjective. Therefore, complementary approaches would be useful. We propose to use text-mining models to automatically suggest potential targets from the neuroscientific literature, full-text articles and abstracts, so that they can be used for anatomical connection studies and more specifically for tractography. We applied text-mining models to three structures: two well-studied structures, since validated deep brain stimulation targets, the internal globus pallidus and the subthalamic nucleus and, the nucleus accumbens, an exploratory target for treating psychiatric disorders. We performed a systematic review of the literature to document the projections of the three selected structures and compared it with the targets proposed by text-mining models, both in rat and primate (including human). We ran probabilistic tractography on the nucleus accumbens and compared the output with the results of the text-mining models and literature review. Overall, text-mining the literature could find three times as many targets as two man-weeks of curation could. The overall efficiency of the text-mining against literature review in our study was 98% recall (at 36% precision), meaning that over all the targets for the three selected seeds, only one target has been missed by text-mining. We demonstrate that connectivity for a structure of interest can be extracted from a very large amount of publications and abstracts. We believe this tool will be useful in helping the neuroscience community to facilitate connectivity studies of particular brain regions. The text mining tools used for the study are part of the HBP Neuroinformatics Platform, publicly available at http://connectivity-brainer.rhcloud.com/. PMID:26074781

A semantic-based method for extracting concept definitions from scientific publications: evaluation in the autism phenotype domain.

PubMed

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.

Assessing semantic similarity of texts - Methods and algorithms

NASA Astrophysics Data System (ADS)

Rozeva, Anna; Zerkova, Silvia

2017-12-01

Assessing the semantic similarity of texts is an important part of different text-related applications like educational systems, information retrieval, text summarization, etc. This task is performed by sophisticated analysis, which implements text-mining techniques. Text mining involves several pre-processing steps, which provide for obtaining structured representative model of the documents in a corpus by means of extracting and selecting the features, characterizing their content. Generally the model is vector-based and enables further analysis with knowledge discovery approaches. Algorithms and measures are used for assessing texts at syntactical and semantic level. An important text-mining method and similarity measure is latent semantic analysis (LSA). It provides for reducing the dimensionality of the document vector space and better capturing the text semantics. The mathematical background of LSA for deriving the meaning of the words in a given text by exploring their co-occurrence is examined. The algorithm for obtaining the vector representation of words and their corresponding latent concepts in a reduced multidimensional space as well as similarity calculation are presented.

Neural networks for data mining electronic text collections

NASA Astrophysics Data System (ADS)

Walker, Nicholas; Truman, Gregory

1997-04-01

The use of neural networks in information retrieval and text analysis has primarily suffered from the issues of adequate document representation, the ability to scale to very large collections, dynamism in the face of new information and the practical difficulties of basing the design on the use of supervised training sets. Perhaps the most important approach to begin solving these problems is the use of `intermediate entities' which reduce the dimensionality of document representations and the size of documents collections to manageable levels coupled with the use of unsupervised neural network paradigms. This paper describes the issues, a fully configured neural network-based text analysis system--dataHARVEST--aimed at data mining text collections which begins this process, along with the remaining difficulties and potential ways forward.

Chemical named entities recognition: a review on approaches and applications

PubMed Central

2014-01-01

The rapid increase in the flow rate of published digital information in all disciplines has resulted in a pressing need for techniques that can simplify the use of this information. The chemistry literature is very rich with information about chemical entities. Extracting molecules and their related properties and activities from the scientific literature to “text mine” these extracted data and determine contextual relationships helps research scientists, particularly those in drug development. One of the most important challenges in chemical text mining is the recognition of chemical entities mentioned in the texts. In this review, the authors briefly introduce the fundamental concepts of chemical literature mining, the textual contents of chemical documents, and the methods of naming chemicals in documents. We sketch out dictionary-based, rule-based and machine learning, as well as hybrid chemical named entity recognition approaches with their applied solutions. We end with an outlook on the pros and cons of these approaches and the types of chemical entities extracted. PMID:24834132

Identifying Engineering Students' English Sentence Reading Comprehension Errors: Applying a Data Mining Technique

ERIC Educational Resources Information Center

Tsai, Yea-Ru; Ouyang, Chen-Sen; Chang, Yukon

2016-01-01

The purpose of this study is to propose a diagnostic approach to identify engineering students' English reading comprehension errors. Student data were collected during the process of reading texts of English for science and technology on a web-based cumulative sentence analysis system. For the analysis, the association-rule, data mining technique…

Working with Data: Discovering Knowledge through Mining and Analysis; Systematic Knowledge Management and Knowledge Discovery; Text Mining; Methodological Approach in Discovering User Search Patterns through Web Log Analysis; Knowledge Discovery in Databases Using Formal Concept Analysis; Knowledge Discovery with a Little Perspective.

ERIC Educational Resources Information Center

Qin, Jian; Jurisica, Igor; Liddy, Elizabeth D.; Jansen, Bernard J; Spink, Amanda; Priss, Uta; Norton, Melanie J.

2000-01-01

These six articles discuss knowledge discovery in databases (KDD). Topics include data mining; knowledge management systems; applications of knowledge discovery; text and Web mining; text mining and information retrieval; user search patterns through Web log analysis; concept analysis; data collection; and data structure inconsistency. (LRW)

Mining Adverse Drug Reactions in Social Media with Named Entity Recognition and Semantic Methods.

PubMed

Chen, Xiaoyi; Deldossi, Myrtille; Aboukhamis, Rim; Faviez, Carole; Dahamna, Badisse; Karapetiantz, Pierre; Guenegou-Arnoux, Armelle; Girardeau, Yannick; Guillemin-Lanne, Sylvie; Lillo-Le-Louët, Agnès; Texier, Nathalie; Burgun, Anita; Katsahian, Sandrine

2017-01-01

Suspected adverse drug reactions (ADR) reported by patients through social media can be a complementary source to current pharmacovigilance systems. However, the performance of text mining tools applied to social media text data to discover ADRs needs to be evaluated. In this paper, we introduce the approach developed to mine ADR from French social media. A protocol of evaluation is highlighted, which includes a detailed sample size determination and evaluation corpus constitution. Our text mining approach provided very encouraging preliminary results with F-measures of 0.94 and 0.81 for recognition of drugs and symptoms respectively, and with F-measure of 0.70 for ADR detection. Therefore, this approach is promising for downstream pharmacovigilance analysis.

Unapparent Information Revelation: Text Mining for Counterterrorism

NASA Astrophysics Data System (ADS)

Srihari, Rohini K.

Unapparent information revelation (UIR) is a special case of text mining that focuses on detecting possible links between concepts across multiple text documents by generating an evidence trail explaining the connection. A traditional search involving, for example, two or more person names will attempt to find documents mentioning both these individuals. This research focuses on a different interpretation of such a query: what is the best evidence trail across documents that explains a connection between these individuals? For example, all may be good golfers. A generalization of this task involves query terms representing general concepts (e.g. indictment, foreign policy). Previous approaches to this problem have focused on graph mining involving hyperlinked documents, and link analysis exploiting named entities. A new robust framework is presented, based on (i) generating concept chain graphs, a hybrid content representation, (ii) performing graph matching to select candidate subgraphs, and (iii) subsequently using graphical models to validate hypotheses using ranked evidence trails. We adapt the DUC data set for cross-document summarization to evaluate evidence trails generated by this approach

Citation Mining: Integrating Text Mining and Bibliometrics for Research User Profiling.

ERIC Educational Resources Information Center

Kostoff, Ronald N.; del Rio, J. Antonio; Humenik, James A.; Garcia, Esther Ofilia; Ramirez, Ana Maria

2001-01-01

Discusses the importance of identifying the users and impact of research, and describes an approach for identifying the pathways through which research can impact other research, technology development, and applications. Describes a study that used citation mining, an integration of citation bibliometrics and text mining, on articles from the…

Conceptual biology, hypothesis discovery, and text mining: Swanson's legacy.

PubMed

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.

Development and testing of a text-mining approach to analyse patients' comments on their experiences of colorectal cancer care.

PubMed

Wagland, Richard; Recio-Saucedo, Alejandra; Simon, Michael; Bracher, Michael; Hunt, Katherine; Foster, Claire; Downing, Amy; Glaser, Adam; Corner, Jessica

2016-08-01

Quality of cancer care may greatly impact on patients' health-related quality of life (HRQoL). Free-text responses to patient-reported outcome measures (PROMs) provide rich data but analysis is time and resource-intensive. This study developed and tested a learning-based text-mining approach to facilitate analysis of patients' experiences of care and develop an explanatory model illustrating impact on HRQoL. Respondents to a population-based survey of colorectal cancer survivors provided free-text comments regarding their experience of living with and beyond cancer. An existing coding framework was tested and adapted, which informed learning-based text mining of the data. Machine-learning algorithms were trained to identify comments relating to patients' specific experiences of service quality, which were verified by manual qualitative analysis. Comparisons between coded retrieved comments and a HRQoL measure (EQ5D) were explored. The survey response rate was 63.3% (21 802/34 467), of which 25.8% (n=5634) participants provided free-text comments. Of retrieved comments on experiences of care (n=1688), over half (n=1045, 62%) described positive care experiences. Most negative experiences concerned a lack of post-treatment care (n=191, 11% of retrieved comments) and insufficient information concerning self-management strategies (n=135, 8%) or treatment side effects (n=160, 9%). Associations existed between HRQoL scores and coded algorithm-retrieved comments. Analysis indicated that the mechanism by which service quality impacted on HRQoL was the extent to which services prevented or alleviated challenges associated with disease and treatment burdens. Learning-based text mining techniques were found useful and practical tools to identify specific free-text comments within a large dataset, facilitating resource-efficient qualitative analysis. This method should be considered for future PROM analysis to inform policy and practice. Study findings indicated that perceived care quality directly impacts on HRQoL. 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/

@Note: a workbench for biomedical text mining.

PubMed

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.

GoWeb: a semantic search engine for the life science web.

PubMed

Dietze, Heiko; Schroeder, Michael

2009-10-01

Current search engines are keyword-based. Semantic technologies promise a next generation of semantic search engines, which will be able to answer questions. Current approaches either apply natural language processing to unstructured text or they assume the existence of structured statements over which they can reason. Here, we introduce a third approach, GoWeb, which combines classical keyword-based Web search with text-mining and ontologies to navigate large results sets and facilitate question answering. We evaluate GoWeb on three benchmarks of questions on genes and functions, on symptoms and diseases, and on proteins and diseases. The first benchmark is based on the BioCreAtivE 1 Task 2 and links 457 gene names with 1352 functions. GoWeb finds 58% of the functional GeneOntology annotations. The second benchmark is based on 26 case reports and links symptoms with diseases. GoWeb achieves 77% success rate improving an existing approach by nearly 20%. The third benchmark is based on 28 questions in the TREC genomics challenge and links proteins to diseases. GoWeb achieves a success rate of 79%. GoWeb's combination of classical Web search with text-mining and ontologies is a first step towards answering questions in the biomedical domain. GoWeb is online at: http://www.gopubmed.org/goweb.

An Evaluation of Text Mining Tools as Applied to Selected Scientific and Engineering Literature.

ERIC Educational Resources Information Center

Trybula, Walter J.; Wyllys, Ronald E.

2000-01-01

Addresses an approach to the discovery of scientific knowledge through an examination of data mining and text mining techniques. Presents the results of experiments that investigated knowledge acquisition from a selected set of technical documents by domain experts. (Contains 15 references.) (Author/LRW)

Integration of Text- and Data-Mining Technologies for Use in Banking Applications

NASA Astrophysics Data System (ADS)

Maslankowski, Jacek

Unstructured data, most of it in the form of text files, typically accounts for 85% of an organization's knowledge stores, but it's not always easy to find, access, analyze or use (Robb 2004). That is why it is important to use solutions based on text and data mining. This solution is known as duo mining. This leads to improve management based on knowledge owned in organization. The results are interesting. Data mining provides to lead with structuralized data, usually powered from data warehouses. Text mining, sometimes called web mining, looks for patterns in unstructured data — memos, document and www. Integrating text-based information with structured data enriches predictive modeling capabilities and provides new stores of insightful and valuable information for driving business and research initiatives forward.

Text Mining in Organizational Research

PubMed Central

Kobayashi, Vladimer B.; Berkers, Hannah A.; Kismihók, Gábor; Den Hartog, Deanne N.

2017-01-01

Despite the ubiquity of textual data, so far few researchers have applied text mining to answer organizational research questions. Text mining, which essentially entails a quantitative approach to the analysis of (usually) voluminous textual data, helps accelerate knowledge discovery by radically increasing the amount data that can be analyzed. This article aims to acquaint organizational researchers with the fundamental logic underpinning text mining, the analytical stages involved, and contemporary techniques that may be used to achieve different types of objectives. The specific analytical techniques reviewed are (a) dimensionality reduction, (b) distance and similarity computing, (c) clustering, (d) topic modeling, and (e) classification. We describe how text mining may extend contemporary organizational research by allowing the testing of existing or new research questions with data that are likely to be rich, contextualized, and ecologically valid. After an exploration of how evidence for the validity of text mining output may be generated, we conclude the article by illustrating the text mining process in a job analysis setting using a dataset composed of job vacancies. PMID:29881248

Text Mining in Organizational Research.

PubMed

Kobayashi, Vladimer B; Mol, Stefan T; Berkers, Hannah A; Kismihók, Gábor; Den Hartog, Deanne N

2018-07-01

Despite the ubiquity of textual data, so far few researchers have applied text mining to answer organizational research questions. Text mining, which essentially entails a quantitative approach to the analysis of (usually) voluminous textual data, helps accelerate knowledge discovery by radically increasing the amount data that can be analyzed. This article aims to acquaint organizational researchers with the fundamental logic underpinning text mining, the analytical stages involved, and contemporary techniques that may be used to achieve different types of objectives. The specific analytical techniques reviewed are (a) dimensionality reduction, (b) distance and similarity computing, (c) clustering, (d) topic modeling, and (e) classification. We describe how text mining may extend contemporary organizational research by allowing the testing of existing or new research questions with data that are likely to be rich, contextualized, and ecologically valid. After an exploration of how evidence for the validity of text mining output may be generated, we conclude the article by illustrating the text mining process in a job analysis setting using a dataset composed of job vacancies.

Semantic Theme Analysis of Pilot Incident Reports

NASA Technical Reports Server (NTRS)

Thirumalainambi, Rajkumar

2009-01-01

Pilots report accidents or incidents during take-off, on flight and landing to airline authorities and Federal aviation authority as well. The description of pilot reports for an incident contains technical terms related to Flight instruments and operations. Normal text mining approaches collect keywords from text documents and relate them among documents that are stored in database. Present approach will extract specific theme analysis of incident reports and semantically relate hierarchy of terms assigning weights of themes. Once the theme extraction has been performed for a given document, a unique key can be assigned to that document to cross linking the documents. Semantic linking will be used to categorize the documents based on specific rules that can help an end-user to analyze certain types of accidents. This presentation outlines the architecture of text mining for pilot incident reports for autonomous categorization of pilot incident reports using semantic theme analysis.

Combining Natural Language Processing and Statistical Text Mining: A Study of Specialized versus Common Languages

ERIC Educational Resources Information Center

Jarman, Jay

2011-01-01

This dissertation focuses on developing and evaluating hybrid approaches for analyzing free-form text in the medical domain. This research draws on natural language processing (NLP) techniques that are used to parse and extract concepts based on a controlled vocabulary. Once important concepts are extracted, additional machine learning algorithms,…

Redundancy and Novelty Mining in the Business Blogosphere

ERIC Educational Resources Information Center

Tsai, Flora S.; Chan, Kap Luk

2010-01-01

Purpose: The paper aims to explore the performance of redundancy and novelty mining in the business blogosphere, which has not been studied before. Design/methodology/approach: Novelty mining techniques are implemented to single out novel information out of a massive set of text documents. This paper adopted the mixed metric approach which…

Experiences with Text Mining Large Collections of Unstructured Systems Development Artifacts at JPL

NASA Technical Reports Server (NTRS)

Port, Dan; Nikora, Allen; Hihn, Jairus; Huang, LiGuo

2011-01-01

Often repositories of systems engineering artifacts at NASA's Jet Propulsion Laboratory (JPL) are so large and poorly structured that they have outgrown our capability to effectively manually process their contents to extract useful information. Sophisticated text mining methods and tools seem a quick, low-effort approach to automating our limited manual efforts. Our experiences of exploring such methods mainly in three areas including historical risk analysis, defect identification based on requirements analysis, and over-time analysis of system anomalies at JPL, have shown that obtaining useful results requires substantial unanticipated efforts - from preprocessing the data to transforming the output for practical applications. We have not observed any quick 'wins' or realized benefit from short-term effort avoidance through automation in this area. Surprisingly we have realized a number of unexpected long-term benefits from the process of applying text mining to our repositories. This paper elaborates some of these benefits and our important lessons learned from the process of preparing and applying text mining to large unstructured system artifacts at JPL aiming to benefit future TM applications in similar problem domains and also in hope for being extended to broader areas of applications.

A Review of Recent Advancement in Integrating Omics Data with Literature Mining towards Biomedical Discoveries

PubMed Central

Raja, Kalpana; Patrick, Matthew; Gao, Yilin; Madu, Desmond; Yang, Yuyang

2017-01-01

In the past decade, the volume of “omics” data generated by the different high-throughput technologies has expanded exponentially. The managing, storing, and analyzing of this big data have been a great challenge for the researchers, especially when moving towards the goal of generating testable data-driven hypotheses, which has been the promise of the high-throughput experimental techniques. Different bioinformatics approaches have been developed to streamline the downstream analyzes by providing independent information to interpret and provide biological inference. Text mining (also known as literature mining) is one of the commonly used approaches for automated generation of biological knowledge from the huge number of published articles. In this review paper, we discuss the recent advancement in approaches that integrate results from omics data and information generated from text mining approaches to uncover novel biomedical information. PMID:28331849

The potential of text mining in data integration and network biology for plant research: a case study on Arabidopsis.

PubMed

Van Landeghem, Sofie; De Bodt, Stefanie; Drebert, Zuzanna J; Inzé, Dirk; Van de Peer, Yves

2013-03-01

Despite the availability of various data repositories for plant research, a wealth of information currently remains hidden within the biomolecular literature. Text mining provides the necessary means to retrieve these data through automated processing of texts. However, only recently has advanced text mining methodology been implemented with sufficient computational power to process texts at a large scale. In this study, we assess the potential of large-scale text mining for plant biology research in general and for network biology in particular using a state-of-the-art text mining system applied to all PubMed abstracts and PubMed Central full texts. We present extensive evaluation of the textual data for Arabidopsis thaliana, assessing the overall accuracy of this new resource for usage in plant network analyses. Furthermore, we combine text mining information with both protein-protein and regulatory interactions from experimental databases. Clusters of tightly connected genes are delineated from the resulting network, illustrating how such an integrative approach is essential to grasp the current knowledge available for Arabidopsis and to uncover gene information through guilt by association. All large-scale data sets, as well as the manually curated textual data, are made publicly available, hereby stimulating the application of text mining data in future plant biology studies.

Text Mining Genotype-Phenotype Relationships from Biomedical Literature for Database Curation and Precision Medicine.

PubMed

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.

Combining QSAR Modeling and Text-Mining Techniques to Link Chemical Structures and Carcinogenic Modes of Action.

PubMed

Papamokos, George; Silins, Ilona

2016-01-01

There is an increasing need for new reliable non-animal based methods to predict and test toxicity of chemicals. Quantitative structure-activity relationship (QSAR), a computer-based method linking chemical structures with biological activities, is used in predictive toxicology. In this study, we tested the approach to combine QSAR data with literature profiles of carcinogenic modes of action automatically generated by a text-mining tool. The aim was to generate data patterns to identify associations between chemical structures and biological mechanisms related to carcinogenesis. Using these two methods, individually and combined, we evaluated 96 rat carcinogens of the hematopoietic system, liver, lung, and skin. We found that skin and lung rat carcinogens were mainly mutagenic, while the group of carcinogens affecting the hematopoietic system and the liver also included a large proportion of non-mutagens. The automatic literature analysis showed that mutagenicity was a frequently reported endpoint in the literature of these carcinogens, however, less common endpoints such as immunosuppression and hormonal receptor-mediated effects were also found in connection with some of the carcinogens, results of potential importance for certain target organs. The combined approach, using QSAR and text-mining techniques, could be useful for identifying more detailed information on biological mechanisms and the relation with chemical structures. The method can be particularly useful in increasing the understanding of structure and activity relationships for non-mutagens.

Combining QSAR Modeling and Text-Mining Techniques to Link Chemical Structures and Carcinogenic Modes of Action

PubMed Central

Papamokos, George; Silins, Ilona

2016-01-01

There is an increasing need for new reliable non-animal based methods to predict and test toxicity of chemicals. Quantitative structure-activity relationship (QSAR), a computer-based method linking chemical structures with biological activities, is used in predictive toxicology. In this study, we tested the approach to combine QSAR data with literature profiles of carcinogenic modes of action automatically generated by a text-mining tool. The aim was to generate data patterns to identify associations between chemical structures and biological mechanisms related to carcinogenesis. Using these two methods, individually and combined, we evaluated 96 rat carcinogens of the hematopoietic system, liver, lung, and skin. We found that skin and lung rat carcinogens were mainly mutagenic, while the group of carcinogens affecting the hematopoietic system and the liver also included a large proportion of non-mutagens. The automatic literature analysis showed that mutagenicity was a frequently reported endpoint in the literature of these carcinogens, however, less common endpoints such as immunosuppression and hormonal receptor-mediated effects were also found in connection with some of the carcinogens, results of potential importance for certain target organs. The combined approach, using QSAR and text-mining techniques, could be useful for identifying more detailed information on biological mechanisms and the relation with chemical structures. The method can be particularly useful in increasing the understanding of structure and activity relationships for non-mutagens. PMID:27625608

A sentence sliding window approach to extract protein annotations from biomedical articles

PubMed Central

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

Health Terrain: Visualizing Large Scale Health Data

DTIC Science & Technology

2015-12-01

Text mining ; Data mining . 16. SECURITY  CLASSIFICATION  OF: 17... text   mining  algorithms  to  construct  a  concept  space.  A   browser-­‐based  user  interface  is  developed  to...Public  health  data,  Notifiable  condition  detector,   Text   mining ,  Data   mining   4 of 29 Disease Patient Location Term

Trends in Fetal Medicine: A 10-Year Bibliometric Analysis of Prenatal Diagnosis

PubMed Central

Dhombres, Ferdinand; Bodenreider, Olivier

2018-01-01

The objective is to automatically identify trends in Fetal Medicine over the past 10 years through a bibliometric analysis of articles published in Prenatal Diagnosis, using text mining techniques. We processed 2,423 full-text articles published in Prenatal Diagnosis between 2006 and 2015. We extracted salient terms, calculated their frequencies over time, and established evolution profiles for terms, from which we derived falling, stable, and rising trends. We identified 618 terms with a falling trend, 2,142 stable terms, and 839 terms with a rising trend. Terms with increasing frequencies include those related to statistics and medical study design. The most recent of these terms reflect the new opportunities of next- generation sequencing. Many terms related to cytogenetics exhibit a falling trend. A bibliometric analysis based on text mining effectively supports identification of trends over time. This scalable approach is complementary to analyses based on metadata or expert opinion. PMID:29295220

The Potential of Text Mining in Data Integration and Network Biology for Plant Research: A Case Study on Arabidopsis[C][W

PubMed Central

Van Landeghem, Sofie; De Bodt, Stefanie; Drebert, Zuzanna J.; Inzé, Dirk; Van de Peer, Yves

2013-01-01

Despite the availability of various data repositories for plant research, a wealth of information currently remains hidden within the biomolecular literature. Text mining provides the necessary means to retrieve these data through automated processing of texts. However, only recently has advanced text mining methodology been implemented with sufficient computational power to process texts at a large scale. In this study, we assess the potential of large-scale text mining for plant biology research in general and for network biology in particular using a state-of-the-art text mining system applied to all PubMed abstracts and PubMed Central full texts. We present extensive evaluation of the textual data for Arabidopsis thaliana, assessing the overall accuracy of this new resource for usage in plant network analyses. Furthermore, we combine text mining information with both protein–protein and regulatory interactions from experimental databases. Clusters of tightly connected genes are delineated from the resulting network, illustrating how such an integrative approach is essential to grasp the current knowledge available for Arabidopsis and to uncover gene information through guilt by association. All large-scale data sets, as well as the manually curated textual data, are made publicly available, hereby stimulating the application of text mining data in future plant biology studies. PMID:23532071

Commercial Online Social Network Data and Statin Side-Effect Surveillance: A Pilot Observational Study of Aggregate Mentions on Facebook.

PubMed

Huesch, Marco D

2017-12-01

Surveillance of the safety of prescribed drugs after marketing approval has been secured remains fraught with complications. Formal ascertainment by providers and reporting to adverse-event registries, formal surveys by manufacturers, and mining of electronic medical records are all well-known approaches with varying degrees of difficulty, cost, and success. Novel approaches may be a useful adjunct, especially approaches that mine or sample internet-based methods such as online social networks. A novel commercial software-as-a-service data-mining product supplied by Sysomos from Datasift/Facebook was used to mine all mentions on Facebook of statins and stain-related side effects in the US in the 1-month period 9 January 2017 through 8 February 2017. A total of 4.3% of all 25,700 mentions of statins also mentioned typical stain-related side effects. Multiple methodological weaknesses stymie interpretation of this percentage, which is however not inconsistent with estimates that 5-20% of patients taking statins will experience typical side effects at some time. Future work on pharmacovigilance may be informed by this novel commercial tool, but the inability to mine the full text of a posting poses serious challenges to content categorization.

Text mining of rheumatoid arthritis and diabetes mellitus to understand the mechanisms of Chinese medicine in different diseases with same treatment.

PubMed

Zhao, Ning; Zheng, Guang; Li, Jian; Zhao, Hong-Yan; Lu, Cheng; Jiang, Miao; Zhang, Chi; Guo, Hong-Tao; Lu, Ai-Ping

2018-01-09

To identify the commonalities between rheumatoid arthritis (RA) and diabetes mellitus (DM) to understand the mechanisms of Chinese medicine (CM) in different diseases with the same treatment. A text mining approach was adopted to analyze the commonalities between RA and DM according to CM and biological elements. The major commonalities were subsequently verifified in RA and DM rat models, in which herbal formula for the treatment of both RA and DM identifified via text mining was used as the intervention. Similarities were identifified between RA and DM regarding the CM approach used for diagnosis and treatment, as well as the networks of biological activities affected by each disease, including the involvement of adhesion molecules, oxidative stress, cytokines, T-lymphocytes, apoptosis, and inflfl ammation. The Ramulus Cinnamomi-Radix Paeoniae Alba-Rhizoma Anemarrhenae is an herbal combination used to treat RA and DM. This formula demonstrated similar effects on oxidative stress and inflfl ammation in rats with collagen-induced arthritis, which supports the text mining results regarding the commonalities between RA and DM. Commonalities between the biological activities involved in RA and DM were identifified through text mining, and both RA and DM might be responsive to the same intervention at a specifific stage.

Automated Assessment of Patients' Self-Narratives for Posttraumatic Stress Disorder Screening Using Natural Language Processing and Text Mining.

PubMed

He, Qiwei; Veldkamp, Bernard P; Glas, Cees A W; de Vries, Theo

2017-03-01

Patients' narratives about traumatic experiences and symptoms are useful in clinical screening and diagnostic procedures. In this study, we presented an automated assessment system to screen patients for posttraumatic stress disorder via a natural language processing and text-mining approach. Four machine-learning algorithms-including decision tree, naive Bayes, support vector machine, and an alternative classification approach called the product score model-were used in combination with n-gram representation models to identify patterns between verbal features in self-narratives and psychiatric diagnoses. With our sample, the product score model with unigrams attained the highest prediction accuracy when compared with practitioners' diagnoses. The addition of multigrams contributed most to balancing the metrics of sensitivity and specificity. This article also demonstrates that text mining is a promising approach for analyzing patients' self-expression behavior, thus helping clinicians identify potential patients from an early stage.

Machine learning approaches to analysing textual injury surveillance data: a systematic review.

PubMed

Vallmuur, Kirsten

2015-06-01

To synthesise recent research on the use of machine learning approaches to mining textual injury surveillance data. Systematic review. The electronic databases which were searched included PubMed, Cinahl, Medline, Google Scholar, and Proquest. The bibliography of all relevant articles was examined and associated articles were identified using a snowballing technique. For inclusion, articles were required to meet the following criteria: (a) used a health-related database, (b) focused on injury-related cases, AND used machine learning approaches to analyse textual data. The papers identified through the search were screened resulting in 16 papers selected for review. Articles were reviewed to describe the databases and methodology used, the strength and limitations of different techniques, and quality assurance approaches used. Due to heterogeneity between studies meta-analysis was not performed. Occupational injuries were the focus of half of the machine learning studies and the most common methods described were Bayesian probability or Bayesian network based methods to either predict injury categories or extract common injury scenarios. Models were evaluated through either comparison with gold standard data or content expert evaluation or statistical measures of quality. Machine learning was found to provide high precision and accuracy when predicting a small number of categories, was valuable for visualisation of injury patterns and prediction of future outcomes. However, difficulties related to generalizability, source data quality, complexity of models and integration of content and technical knowledge were discussed. The use of narrative text for injury surveillance has grown in popularity, complexity and quality over recent years. With advances in data mining techniques, increased capacity for analysis of large databases, and involvement of computer scientists in the injury prevention field, along with more comprehensive use and description of quality assurance methods in text mining approaches, it is likely that we will see a continued growth and advancement in knowledge of text mining in the injury field. Copyright © 2015 Elsevier Ltd. All rights reserved.

The systematic assessment of traditional evidence from the premodern Chinese medical literature: a text-mining approach.

PubMed

May, Brian H; Zhang, Anthony; Lu, Yubo; Lu, Chuanjian; Xue, Charlie C L

2014-12-01

This project aimed to develop an approach to evaluating information contained in the premodern Traditional Chinese Medicine (TCM) literature that was (1) comprehensive, systematic, and replicable and (2) able to produce quantifiable output that could be used to answer specific research questions in order to identify natural products for clinical and experimental research. The project involved two stages. In stage 1, 14 TCM collections and compendia were evaluated for suitability as sources for searching; 8 of these were compared in detail. The results were published in the Journal of Alternative and Complementary Medicine. Stage 2 developed a text-mining approach for two of these sources. The text-mining approach was developed for Zhong Hua Yi Dian; Encyclopaedia of Traditional Chinese Medicine, 4th edition) and Zhong Yi Fang Ji Da Ci Dian; Great Compendium of Chinese Medical Formulae). This approach developed procedures for search term selection; methods for screening, classifying, and scoring data; procedures for systematic searching and data extraction; data checking procedures; and approaches for analyzing results. Examples are provided for studies of memory impairment and diabetic nephropathy, and issues relating to data interpretation are discussed. This approach to the analysis of large collections of the premodern TCM literature uses widely available sources and provides a text-mining approach that is systematic, replicable, and adaptable to the requirements of the particular project. Researchers can use these methods to explore changes in the names and conceptions of a disease over time, to identify which therapeutic methods have been more or less frequently used in different eras for particular disorders, and to assist in the selection of natural products for research efforts.

The Voice of Chinese Health Consumers: A Text Mining Approach to Web-Based Physician Reviews

PubMed Central

Zhang, Kunpeng

2016-01-01

Background Many Web-based health care platforms allow patients to evaluate physicians by posting open-end textual reviews based on their experiences. These reviews are helpful resources for other patients to choose high-quality doctors, especially in countries like China where no doctor referral systems exist. Analyzing such a large amount of user-generated content to understand the voice of health consumers has attracted much attention from health care providers and health care researchers. Objective The aim of this paper is to automatically extract hidden topics from Web-based physician reviews using text-mining techniques to examine what Chinese patients have said about their doctors and whether these topics differ across various specialties. This knowledge will help health care consumers, providers, and researchers better understand this information. Methods We conducted two-fold analyses on the data collected from the “Good Doctor Online” platform, the largest online health community in China. First, we explored all reviews from 2006-2014 using descriptive statistics. Second, we applied the well-known topic extraction algorithm Latent Dirichlet Allocation to more than 500,000 textual reviews from over 75,000 Chinese doctors across four major specialty areas to understand what Chinese health consumers said online about their doctor visits. Results On the “Good Doctor Online” platform, 112,873 out of 314,624 doctors had been reviewed at least once by April 11, 2014. Among the 772,979 textual reviews, we chose to focus on four major specialty areas that received the most reviews: Internal Medicine, Surgery, Obstetrics/Gynecology and Pediatrics, and Chinese Traditional Medicine. Among the doctors who received reviews from those four medical specialties, two-thirds of them received more than two reviews and in a few extreme cases, some doctors received more than 500 reviews. Across the four major areas, the most popular topics reviewers found were the experience of finding doctors, doctors’ technical skills and bedside manner, general appreciation from patients, and description of various symptoms. Conclusions To the best of our knowledge, our work is the first study using an automated text-mining approach to analyze a large amount of unstructured textual data of Web-based physician reviews in China. Based on our analysis, we found that Chinese reviewers mainly concentrate on a few popular topics. This is consistent with the goal of Chinese online health platforms and demonstrates the health care focus in China’s health care system. Our text-mining approach reveals a new research area on how to use big data to help health care providers, health care administrators, and policy makers hear patient voices, target patient concerns, and improve the quality of care in this age of patient-centered care. Also, on the health care consumer side, our text mining technique helps patients make more informed decisions about which specialists to see without reading thousands of reviews, which is simply not feasible. In addition, our comparison analysis of Web-based physician reviews in China and the United States also indicates some cultural differences. PMID:27165558

The Voice of Chinese Health Consumers: A Text Mining Approach to Web-Based Physician Reviews.

PubMed

Hao, Haijing; Zhang, Kunpeng

2016-05-10

Many Web-based health care platforms allow patients to evaluate physicians by posting open-end textual reviews based on their experiences. These reviews are helpful resources for other patients to choose high-quality doctors, especially in countries like China where no doctor referral systems exist. Analyzing such a large amount of user-generated content to understand the voice of health consumers has attracted much attention from health care providers and health care researchers. The aim of this paper is to automatically extract hidden topics from Web-based physician reviews using text-mining techniques to examine what Chinese patients have said about their doctors and whether these topics differ across various specialties. This knowledge will help health care consumers, providers, and researchers better understand this information. We conducted two-fold analyses on the data collected from the "Good Doctor Online" platform, the largest online health community in China. First, we explored all reviews from 2006-2014 using descriptive statistics. Second, we applied the well-known topic extraction algorithm Latent Dirichlet Allocation to more than 500,000 textual reviews from over 75,000 Chinese doctors across four major specialty areas to understand what Chinese health consumers said online about their doctor visits. On the "Good Doctor Online" platform, 112,873 out of 314,624 doctors had been reviewed at least once by April 11, 2014. Among the 772,979 textual reviews, we chose to focus on four major specialty areas that received the most reviews: Internal Medicine, Surgery, Obstetrics/Gynecology and Pediatrics, and Chinese Traditional Medicine. Among the doctors who received reviews from those four medical specialties, two-thirds of them received more than two reviews and in a few extreme cases, some doctors received more than 500 reviews. Across the four major areas, the most popular topics reviewers found were the experience of finding doctors, doctors' technical skills and bedside manner, general appreciation from patients, and description of various symptoms. To the best of our knowledge, our work is the first study using an automated text-mining approach to analyze a large amount of unstructured textual data of Web-based physician reviews in China. Based on our analysis, we found that Chinese reviewers mainly concentrate on a few popular topics. This is consistent with the goal of Chinese online health platforms and demonstrates the health care focus in China's health care system. Our text-mining approach reveals a new research area on how to use big data to help health care providers, health care administrators, and policy makers hear patient voices, target patient concerns, and improve the quality of care in this age of patient-centered care. Also, on the health care consumer side, our text mining technique helps patients make more informed decisions about which specialists to see without reading thousands of reviews, which is simply not feasible. In addition, our comparison analysis of Web-based physician reviews in China and the United States also indicates some cultural differences.

Combining evidence, biomedical literature and statistical dependence: new insights for functional annotation of gene sets

PubMed Central

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

Text mining for traditional Chinese medical knowledge discovery: a survey.

PubMed

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.

Mining Patients' Narratives in Social Media for Pharmacovigilance: Adverse Effects and Misuse of Methylphenidate.

PubMed

Chen, Xiaoyi; Faviez, Carole; Schuck, Stéphane; Lillo-Le-Louët, Agnès; Texier, Nathalie; Dahamna, Badisse; Huot, Charles; Foulquié, Pierre; Pereira, Suzanne; Leroux, Vincent; Karapetiantz, Pierre; Guenegou-Arnoux, Armelle; Katsahian, Sandrine; Bousquet, Cédric; Burgun, Anita

2018-01-01

Background: The Food and Drug Administration (FDA) in the United States and the European Medicines Agency (EMA) have recognized social media as a new data source to strengthen their activities regarding drug safety. Objective: Our objective in the ADR-PRISM project was to provide text mining and visualization tools to explore a corpus of posts extracted from social media. We evaluated this approach on a corpus of 21 million posts from five patient forums, and conducted a qualitative analysis of the data available on methylphenidate in this corpus. Methods: We applied text mining methods based on named entity recognition and relation extraction in the corpus, followed by signal detection using proportional reporting ratio (PRR). We also used topic modeling based on the Correlated Topic Model to obtain the list of the matics in the corpus and classify the messages based on their topics. Results: We automatically identified 3443 posts about methylphenidate published between 2007 and 2016, among which 61 adverse drug reactions (ADR) were automatically detected. Two pharmacovigilance experts evaluated manually the quality of automatic identification, and a f-measure of 0.57 was reached. Patient's reports were mainly neuro-psychiatric effects. Applying PRR, 67% of the ADRs were signals, including most of the neuro-psychiatric symptoms but also palpitations. Topic modeling showed that the most represented topics were related to Childhood and Treatment initiation , but also Side effects . Cases of misuse were also identified in this corpus, including recreational use and abuse. Conclusion: Named entity recognition combined with signal detection and topic modeling have demonstrated their complementarity in mining social media data. An in-depth analysis focused on methylphenidate showed that this approach was able to detect potential signals and to provide better understanding of patients' behaviors regarding drugs, including misuse.

Mining Tasks from the Web Anchor Text Graph: MSR Notebook Paper for the TREC 2015 Tasks Track

DTIC Science & Technology

2015-11-20

Mining Tasks from the Web Anchor Text Graph: MSR Notebook Paper for the TREC 2015 Tasks Track Paul N. Bennett Microsoft Research Redmond, USA pauben...anchor text graph has proven useful in the general realm of query reformulation [2], we sought to quantify the value of extracting key phrases from...anchor text in the broader setting of the task understanding track. Given a query, our approach considers a simple method for identifying a relevant

Coronary artery disease risk assessment from unstructured electronic health records using text mining.

PubMed

Jonnagaddala, Jitendra; Liaw, Siaw-Teng; Ray, Pradeep; Kumar, Manish; Chang, Nai-Wen; Dai, Hong-Jie

2015-12-01

Coronary artery disease (CAD) often leads to myocardial infarction, which may be fatal. Risk factors can be used to predict CAD, which may subsequently lead to prevention or early intervention. Patient data such as co-morbidities, medication history, social history and family history are required to determine the risk factors for a disease. However, risk factor data are usually embedded in unstructured clinical narratives if the data is not collected specifically for risk assessment purposes. Clinical text mining can be used to extract data related to risk factors from unstructured clinical notes. This study presents methods to extract Framingham risk factors from unstructured electronic health records using clinical text mining and to calculate 10-year coronary artery disease risk scores in a cohort of diabetic patients. We developed a rule-based system to extract risk factors: age, gender, total cholesterol, HDL-C, blood pressure, diabetes history and smoking history. The results showed that the output from the text mining system was reliable, but there was a significant amount of missing data to calculate the Framingham risk score. A systematic approach for understanding missing data was followed by implementation of imputation strategies. An analysis of the 10-year Framingham risk scores for coronary artery disease in this cohort has shown that the majority of the diabetic patients are at moderate risk of CAD. Copyright © 2015 Elsevier Inc. All rights reserved.

Web-Based Collaborative Writing in L2 Contexts: Methodological Insights from Text Mining

ERIC Educational Resources Information Center

Yim, Soobin; Warschauer, Mark

2017-01-01

The increasingly widespread use of social software (e.g., Wikis, Google Docs) in second language (L2) settings has brought a renewed attention to collaborative writing. Although the current methodological approaches to examining collaborative writing are valuable to understand L2 students' interactional patterns or perceived experiences, they can…

A knowledge engineering approach to recognizing and extracting sequences of nucleic acids from scientific literature.

PubMed

García-Remesal, Miguel; Maojo, Victor; Crespo, José

2010-01-01

In this paper we present a knowledge engineering approach to automatically recognize and extract genetic sequences from scientific articles. To carry out this task, we use a preliminary recognizer based on a finite state machine to extract all candidate DNA/RNA sequences. The latter are then fed into a knowledge-based system that automatically discards false positives and refines noisy and incorrectly merged sequences. We created the knowledge base by manually analyzing different manuscripts containing genetic sequences. Our approach was evaluated using a test set of 211 full-text articles in PDF format containing 3134 genetic sequences. For such set, we achieved 87.76% precision and 97.70% recall respectively. This method can facilitate different research tasks. These include text mining, information extraction, and information retrieval research dealing with large collections of documents containing genetic sequences.

Text mining in livestock animal science: introducing the potential of text mining to animal sciences.

PubMed

Sahadevan, S; Hofmann-Apitius, M; Schellander, K; Tesfaye, D; Fluck, J; Friedrich, C M

2012-10-01

In biological research, establishing the prior art by searching and collecting information already present in the domain has equal importance as the experiments done. To obtain a complete overview about the relevant knowledge, researchers mainly rely on 2 major information sources: i) various biological databases and ii) scientific publications in the field. The major difference between the 2 information sources is that information from databases is available, typically well structured and condensed. The information content in scientific literature is vastly unstructured; that is, dispersed among the many different sections of scientific text. The traditional method of information extraction from scientific literature occurs by generating a list of relevant publications in the field of interest and manually scanning these texts for relevant information, which is very time consuming. It is more than likely that in using this "classical" approach the researcher misses some relevant information mentioned in the literature or has to go through biological databases to extract further information. Text mining and named entity recognition methods have already been used in human genomics and related fields as a solution to this problem. These methods can process and extract information from large volumes of scientific text. Text mining is defined as the automatic extraction of previously unknown and potentially useful information from text. Named entity recognition (NER) is defined as the method of identifying named entities (names of real world objects; for example, gene/protein names, drugs, enzymes) in text. In animal sciences, text mining and related methods have been briefly used in murine genomics and associated fields, leaving behind other fields of animal sciences, such as livestock genomics. The aim of this work was to develop an information retrieval platform in the livestock domain focusing on livestock publications and the recognition of relevant data from cattle and pigs. For this purpose, the rather noncomprehensive resources of pig and cattle gene and protein terminologies were enriched with orthologue synonyms, integrated in the NER platform, ProMiner, which is successfully used in human genomics domain. Based on the performance tests done, the present system achieved a fair performance with precision 0.64, recall 0.74, and F(1) measure of 0.69 in a test scenario based on cattle literature.

Web services-based text-mining demonstrates broad impacts for interoperability and process simplification.

PubMed

Wiegers, Thomas C; Davis, Allan Peter; Mattingly, Carolyn J

2014-01-01

The Critical Assessment of Information Extraction systems in Biology (BioCreAtIvE) challenge evaluation tasks collectively represent a community-wide effort to evaluate a variety of text-mining and information extraction systems applied to the biological domain. The BioCreative IV Workshop included five independent subject areas, including Track 3, which focused on named-entity recognition (NER) for the Comparative Toxicogenomics Database (CTD; http://ctdbase.org). Previously, CTD had organized document ranking and NER-related tasks for the BioCreative Workshop 2012; a key finding of that effort was that interoperability and integration complexity were major impediments to the direct application of the systems to CTD's text-mining pipeline. This underscored a prevailing problem with software integration efforts. Major interoperability-related issues included lack of process modularity, operating system incompatibility, tool configuration complexity and lack of standardization of high-level inter-process communications. One approach to potentially mitigate interoperability and general integration issues is the use of Web services to abstract implementation details; rather than integrating NER tools directly, HTTP-based calls from CTD's asynchronous, batch-oriented text-mining pipeline could be made to remote NER Web services for recognition of specific biological terms using BioC (an emerging family of XML formats) for inter-process communications. To test this concept, participating groups developed Representational State Transfer /BioC-compliant Web services tailored to CTD's NER requirements. Participants were provided with a comprehensive set of training materials. CTD evaluated results obtained from the remote Web service-based URLs against a test data set of 510 manually curated scientific articles. Twelve groups participated in the challenge. Recall, precision, balanced F-scores and response times were calculated. Top balanced F-scores for gene, chemical and disease NER were 61, 74 and 51%, respectively. Response times ranged from fractions-of-a-second to over a minute per article. We present a description of the challenge and summary of results, demonstrating how curation groups can effectively use interoperable NER technologies to simplify text-mining pipeline implementation. Database URL: http://ctdbase.org/ © The Author(s) 2014. Published by Oxford University Press.

Web services-based text-mining demonstrates broad impacts for interoperability and process simplification

PubMed Central

Wiegers, Thomas C.; Davis, Allan Peter; Mattingly, Carolyn J.

2014-01-01

The Critical Assessment of Information Extraction systems in Biology (BioCreAtIvE) challenge evaluation tasks collectively represent a community-wide effort to evaluate a variety of text-mining and information extraction systems applied to the biological domain. The BioCreative IV Workshop included five independent subject areas, including Track 3, which focused on named-entity recognition (NER) for the Comparative Toxicogenomics Database (CTD; http://ctdbase.org). Previously, CTD had organized document ranking and NER-related tasks for the BioCreative Workshop 2012; a key finding of that effort was that interoperability and integration complexity were major impediments to the direct application of the systems to CTD's text-mining pipeline. This underscored a prevailing problem with software integration efforts. Major interoperability-related issues included lack of process modularity, operating system incompatibility, tool configuration complexity and lack of standardization of high-level inter-process communications. One approach to potentially mitigate interoperability and general integration issues is the use of Web services to abstract implementation details; rather than integrating NER tools directly, HTTP-based calls from CTD's asynchronous, batch-oriented text-mining pipeline could be made to remote NER Web services for recognition of specific biological terms using BioC (an emerging family of XML formats) for inter-process communications. To test this concept, participating groups developed Representational State Transfer /BioC-compliant Web services tailored to CTD's NER requirements. Participants were provided with a comprehensive set of training materials. CTD evaluated results obtained from the remote Web service-based URLs against a test data set of 510 manually curated scientific articles. Twelve groups participated in the challenge. Recall, precision, balanced F-scores and response times were calculated. Top balanced F-scores for gene, chemical and disease NER were 61, 74 and 51%, respectively. Response times ranged from fractions-of-a-second to over a minute per article. We present a description of the challenge and summary of results, demonstrating how curation groups can effectively use interoperable NER technologies to simplify text-mining pipeline implementation. Database URL: http://ctdbase.org/ PMID:24919658

DiMeX: A Text Mining System for Mutation-Disease Association Extraction.

PubMed

Mahmood, A S M Ashique; Wu, Tsung-Jung; Mazumder, Raja; Vijay-Shanker, K

2016-01-01

The number of published articles describing associations between mutations and diseases is increasing at a fast pace. There is a pressing need to gather such mutation-disease associations into public knowledge bases, but manual curation slows down the growth of such databases. We have addressed this problem by developing a text-mining system (DiMeX) to extract mutation to disease associations from publication abstracts. DiMeX consists of a series of natural language processing modules that preprocess input text and apply syntactic and semantic patterns to extract mutation-disease associations. DiMeX achieves high precision and recall with F-scores of 0.88, 0.91 and 0.89 when evaluated on three different datasets for mutation-disease associations. DiMeX includes a separate component that extracts mutation mentions in text and associates them with genes. This component has been also evaluated on different datasets and shown to achieve state-of-the-art performance. The results indicate that our system outperforms the existing mutation-disease association tools, addressing the low precision problems suffered by most approaches. DiMeX was applied on a large set of abstracts from Medline to extract mutation-disease associations, as well as other relevant information including patient/cohort size and population data. The results are stored in a database that can be queried and downloaded at http://biotm.cis.udel.edu/dimex/. We conclude that this high-throughput text-mining approach has the potential to significantly assist researchers and curators to enrich mutation databases.

DiMeX: A Text Mining System for Mutation-Disease Association Extraction

PubMed Central

Mahmood, A. S. M. Ashique; Wu, Tsung-Jung; Mazumder, Raja; Vijay-Shanker, K.

2016-01-01

The number of published articles describing associations between mutations and diseases is increasing at a fast pace. There is a pressing need to gather such mutation-disease associations into public knowledge bases, but manual curation slows down the growth of such databases. We have addressed this problem by developing a text-mining system (DiMeX) to extract mutation to disease associations from publication abstracts. DiMeX consists of a series of natural language processing modules that preprocess input text and apply syntactic and semantic patterns to extract mutation-disease associations. DiMeX achieves high precision and recall with F-scores of 0.88, 0.91 and 0.89 when evaluated on three different datasets for mutation-disease associations. DiMeX includes a separate component that extracts mutation mentions in text and associates them with genes. This component has been also evaluated on different datasets and shown to achieve state-of-the-art performance. The results indicate that our system outperforms the existing mutation-disease association tools, addressing the low precision problems suffered by most approaches. DiMeX was applied on a large set of abstracts from Medline to extract mutation-disease associations, as well as other relevant information including patient/cohort size and population data. The results are stored in a database that can be queried and downloaded at http://biotm.cis.udel.edu/dimex/. We conclude that this high-throughput text-mining approach has the potential to significantly assist researchers and curators to enrich mutation databases. PMID:27073839

Application of the EVEX resource to event extraction and network construction: Shared Task entry and result analysis

PubMed Central

2015-01-01

Background Modern methods for mining biomolecular interactions from literature typically make predictions based solely on the immediate textual context, in effect a single sentence. No prior work has been published on extending this context to the information automatically gathered from the whole biomedical literature. Thus, our motivation for this study is to explore whether mutually supporting evidence, aggregated across several documents can be utilized to improve the performance of the state-of-the-art event extraction systems. In this paper, we describe our participation in the latest BioNLP Shared Task using the large-scale text mining resource EVEX. We participated in the Genia Event Extraction (GE) and Gene Regulation Network (GRN) tasks with two separate systems. In the GE task, we implemented a re-ranking approach to improve the precision of an existing event extraction system, incorporating features from the EVEX resource. In the GRN task, our system relied solely on the EVEX resource and utilized a rule-based conversion algorithm between the EVEX and GRN formats. Results In the GE task, our re-ranking approach led to a modest performance increase and resulted in the first rank of the official Shared Task results with 50.97% F-score. Additionally, in this paper we explore and evaluate the usage of distributed vector representations for this challenge. In the GRN task, we ranked fifth in the official results with a strict/relaxed SER score of 0.92/0.81 respectively. To try and improve upon these results, we have implemented a novel machine learning based conversion system and benchmarked its performance against the original rule-based system. Conclusions For the GRN task, we were able to produce a gene regulatory network from the EVEX data, warranting the use of such generic large-scale text mining data in network biology settings. A detailed performance and error analysis provides more insight into the relatively low recall rates. In the GE task we demonstrate that both the re-ranking approach and the word vectors can provide slight performance improvement. A manual evaluation of the re-ranking results pinpoints some of the challenges faced in applying large-scale text mining knowledge to event extraction. PMID:26551766

Introduction to the JASIST Special Topic Issue on Web Retrieval and Mining: A Machine Learning Perspective.

ERIC Educational Resources Information Center

Chen, Hsinchun

2003-01-01

Discusses information retrieval techniques used on the World Wide Web. Topics include machine learning in information extraction; relevance feedback; information filtering and recommendation; text classification and text clustering; Web mining, based on data mining techniques; hyperlink structure; and Web size. (LRW)

Recent Advances and Emerging Applications in Text and Data Mining for Biomedical Discovery.

PubMed

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.

Recent Advances and Emerging Applications in Text and Data Mining for Biomedical Discovery

PubMed Central

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

The Distribution of the Informative Intensity of the Text in Terms of its Structure (On Materials of the English Texts in the Mining Sphere)

NASA Astrophysics Data System (ADS)

Znikina, Ludmila; Rozhneva, Elena

2017-11-01

The article deals with the distribution of informative intensity of the English-language scientific text based on its structural features contributing to the process of formalization of the scientific text and the preservation of the adequacy of the text with derived semantic information in relation to the primary. Discourse analysis is built on specific compositional and meaningful examples of scientific texts taken from the mining field. It also analyzes the adequacy of the translation of foreign texts into another language, the relationships between elements of linguistic systems, the degree of a formal conformance, translation with the specific objectives and information needs of the recipient. Some key words and ideas are emphasized in the paragraphs of the English-language mining scientific texts. The article gives the characteristic features of the structure of paragraphs of technical text and examples of constructions in English scientific texts based on a mining theme with the aim to explain the possible ways of their adequate translation.

A New Pivoting and Iterative Text Detection Algorithm for Biomedical Images

PubMed Central

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

The contribution of the vaccine adverse event text mining system to the classification of possible Guillain-Barré syndrome reports.

PubMed

Botsis, T; Woo, E J; Ball, R

2013-01-01

We previously demonstrated that a general purpose text mining system, the Vaccine adverse event Text Mining (VaeTM) system, could be used to automatically classify reports of an-aphylaxis for post-marketing safety surveillance of vaccines. To evaluate the ability of VaeTM to classify reports to the Vaccine Adverse Event Reporting System (VAERS) of possible Guillain-Barré Syndrome (GBS). We used VaeTM to extract the key diagnostic features from the text of reports in VAERS. Then, we applied the Brighton Collaboration (BC) case definition for GBS, and an information retrieval strategy (i.e. the vector space model) to quantify the specific information that is included in the key features extracted by VaeTM and compared it with the encoded information that is already stored in VAERS as Medical Dictionary for Regulatory Activities (MedDRA) Preferred Terms (PTs). We also evaluated the contribution of the primary (diagnosis and cause of death) and secondary (second level diagnosis and symptoms) diagnostic VaeTM-based features to the total VaeTM-based information. MedDRA captured more information and better supported the classification of reports for GBS than VaeTM (AUC: 0.904 vs. 0.777); the lower performance of VaeTM is likely due to the lack of extraction by VaeTM of specific laboratory results that are included in the BC criteria for GBS. On the other hand, the VaeTM-based classification exhibited greater specificity than the MedDRA-based approach (94.96% vs. 87.65%). Most of the VaeTM-based information was contained in the secondary diagnostic features. For GBS, clinical signs and symptoms alone are not sufficient to match MedDRA coding for purposes of case classification, but are preferred if specificity is the priority.

Imitating manual curation of text-mined facts in biomedicine.

PubMed

Rodriguez-Esteban, Raul; Iossifov, Ivan; Rzhetsky, Andrey

2006-09-08

Text-mining algorithms make mistakes in extracting facts from natural-language texts. In biomedical applications, which rely on use of text-mined data, it is critical to assess the quality (the probability that the message is correctly extracted) of individual facts--to resolve data conflicts and inconsistencies. Using a large set of almost 100,000 manually produced evaluations (most facts were independently reviewed more than once, producing independent evaluations), we implemented and tested a collection of algorithms that mimic human evaluation of facts provided by an automated information-extraction system. The performance of our best automated classifiers closely approached that of our human evaluators (ROC score close to 0.95). Our hypothesis is that, were we to use a larger number of human experts to evaluate any given sentence, we could implement an artificial-intelligence curator that would perform the classification job at least as accurately as an average individual human evaluator. We illustrated our analysis by visualizing the predicted accuracy of the text-mined relations involving the term cocaine.

Automatic detection of adverse events to predict drug label changes using text and data mining techniques.

PubMed

Gurulingappa, Harsha; Toldo, Luca; Rajput, Abdul Mateen; Kors, Jan A; Taweel, Adel; Tayrouz, Yorki

2013-11-01

The aim of this study was to assess the impact of automatically detected adverse event signals from text and open-source data on the prediction of drug label changes. Open-source adverse effect data were collected from FAERS, Yellow Cards and SIDER databases. A shallow linguistic relation extraction system (JSRE) was applied for extraction of adverse effects from MEDLINE case reports. Statistical approach was applied on the extracted datasets for signal detection and subsequent prediction of label changes issued for 29 drugs by the UK Regulatory Authority in 2009. 76% of drug label changes were automatically predicted. Out of these, 6% of drug label changes were detected only by text mining. JSRE enabled precise identification of four adverse drug events from MEDLINE that were undetectable otherwise. Changes in drug labels can be predicted automatically using data and text mining techniques. Text mining technology is mature and well-placed to support the pharmacovigilance tasks. Copyright © 2013 John Wiley & Sons, Ltd.

Two Different Approaches to Automated Mark Up of Emotions in Text

NASA Astrophysics Data System (ADS)

Francisco, Virginia; Hervás, Raqucl; Gervás, Pablo

This paper presents two different approaches to automated marking up of texts with emotional labels. For the first approach a corpus of example texts previously annotated by human evaluators is mined for an initial assignment of emotional features to words. This results in a List of Emotional Words (LEW) which becomes a useful resource for later automated mark up. The mark up algorithm in this first approach mirrors closely the steps taken during feature extraction, employing for the actual assignment of emotional features a combination of the LEW resource and WordNet for knowledge-based expansion of words not occurring in LEW. The algorithm for automated mark up is tested against new text samples to test its coverage. The second approach mark up texts during their generation. We have a knowledge base which contains the necessary information for marking up the text. This information is related to actions and characters. The algorithm in this case employ the information of the knowledge database and decides the correct emotion for every sentence. The algorithm for automated mark up is tested against four different texts. The results of the two approaches are compared and discussed with respect to three main issues: relative adequacy of each one of the representations used, correctness and coverage of the proposed algorithms, and additional techniques and solutions that may be employed to improve the results.

Mining adverse drug reactions from online healthcare forums using hidden Markov model.

PubMed

Sampathkumar, Hariprasad; Chen, Xue-wen; Luo, Bo

2014-10-23

Adverse Drug Reactions are one of the leading causes of injury or death among patients undergoing medical treatments. Not all Adverse Drug Reactions are identified before a drug is made available in the market. Current post-marketing drug surveillance methods, which are based purely on voluntary spontaneous reports, are unable to provide the early indications necessary to prevent the occurrence of such injuries or fatalities. The objective of this research is to extract reports of adverse drug side-effects from messages in online healthcare forums and use them as early indicators to assist in post-marketing drug surveillance. We treat the task of extracting adverse side-effects of drugs from healthcare forum messages as a sequence labeling problem and present a Hidden Markov Model(HMM) based Text Mining system that can be used to classify a message as containing drug side-effect information and then extract the adverse side-effect mentions from it. A manually annotated dataset from http://www.medications.com is used in the training and validation of the HMM based Text Mining system. A 10-fold cross-validation on the manually annotated dataset yielded on average an F-Score of 0.76 from the HMM Classifier, in comparison to 0.575 from the Baseline classifier. Without the Plain Text Filter component as a part of the Text Processing module, the F-Score of the HMM Classifier was reduced to 0.378 on average, while absence of the HTML Filter component was found to have no impact. Reducing the Drug names dictionary size by half, on average reduced the F-Score of the HMM Classifier to 0.359, while a similar reduction to the side-effects dictionary yielded an F-Score of 0.651 on average. Adverse side-effects mined from http://www.medications.com and http://www.steadyhealth.com were found to match the Adverse Drug Reactions on the Drug Package Labels of several drugs. In addition, some novel adverse side-effects, which can be potential Adverse Drug Reactions, were also identified. The results from the HMM based Text Miner are encouraging to pursue further enhancements to this approach. The mined novel side-effects can act as early indicators for health authorities to help focus their efforts in post-marketing drug surveillance.

Adverse Event extraction from Structured Product Labels using the Event-based Text-mining of Health Electronic Records (ETHER)system.

PubMed

Pandey, Abhishek; Kreimeyer, Kory; Foster, Matthew; Botsis, Taxiarchis; Dang, Oanh; Ly, Thomas; Wang, Wei; Forshee, Richard

2018-01-01

Structured Product Labels follow an XML-based document markup standard approved by the Health Level Seven organization and adopted by the US Food and Drug Administration as a mechanism for exchanging medical products information. Their current organization makes their secondary use rather challenging. We used the Side Effect Resource database and DailyMed to generate a comparison dataset of 1159 Structured Product Labels. We processed the Adverse Reaction section of these Structured Product Labels with the Event-based Text-mining of Health Electronic Records system and evaluated its ability to extract and encode Adverse Event terms to Medical Dictionary for Regulatory Activities Preferred Terms. A small sample of 100 labels was then selected for further analysis. Of the 100 labels, Event-based Text-mining of Health Electronic Records achieved a precision and recall of 81 percent and 92 percent, respectively. This study demonstrated Event-based Text-mining of Health Electronic Record's ability to extract and encode Adverse Event terms from Structured Product Labels which may potentially support multiple pharmacoepidemiological tasks.

A random set scoring model for prioritization of disease candidate genes using protein complexes and data-mining of GeneRIF, OMIM and PubMed records.

PubMed

Jiang, Li; Edwards, Stefan M; Thomsen, Bo; Workman, Christopher T; Guldbrandtsen, Bernt; Sørensen, Peter

2014-09-24

Prioritizing genetic variants is a challenge because disease susceptibility loci are often located in genes of unknown function or the relationship with the corresponding phenotype is unclear. A global data-mining exercise on the biomedical literature can establish the phenotypic profile of genes with respect to their connection to disease phenotypes. The importance of protein-protein interaction networks in the genetic heterogeneity of common diseases or complex traits is becoming increasingly recognized. Thus, the development of a network-based approach combined with phenotypic profiling would be useful for disease gene prioritization. We developed a random-set scoring model and implemented it to quantify phenotype relevance in a network-based disease gene-prioritization approach. We validated our approach based on different gene phenotypic profiles, which were generated from PubMed abstracts, OMIM, and GeneRIF records. We also investigated the validity of several vocabulary filters and different likelihood thresholds for predicted protein-protein interactions in terms of their effect on the network-based gene-prioritization approach, which relies on text-mining of the phenotype data. Our method demonstrated good precision and sensitivity compared with those of two alternative complex-based prioritization approaches. We then conducted a global ranking of all human genes according to their relevance to a range of human diseases. The resulting accurate ranking of known causal genes supported the reliability of our approach. Moreover, these data suggest many promising novel candidate genes for human disorders that have a complex mode of inheritance. We have implemented and validated a network-based approach to prioritize genes for human diseases based on their phenotypic profile. We have devised a powerful and transparent tool to identify and rank candidate genes. Our global gene prioritization provides a unique resource for the biological interpretation of data from genome-wide association studies, and will help in the understanding of how the associated genetic variants influence disease or quantitative phenotypes.

Text mining to decipher free-response consumer complaints: insights from the NHTSA vehicle owner's complaint database.

PubMed

Ghazizadeh, Mahtab; McDonald, Anthony D; Lee, John D

2014-09-01

This study applies text mining to extract clusters of vehicle problems and associated trends from free-response data in the National Highway Traffic Safety Administration's vehicle owner's complaint database. As the automotive industry adopts new technologies, it is important to systematically assess the effect of these changes on traffic safety. Driving simulators, naturalistic driving data, and crash databases all contribute to a better understanding of how drivers respond to changing vehicle technology, but other approaches, such as automated analysis of incident reports, are needed. Free-response data from incidents representing two severity levels (fatal incidents and incidents involving injury) were analyzed using a text mining approach: latent semantic analysis (LSA). LSA and hierarchical clustering identified clusters of complaints for each severity level, which were compared and analyzed across time. Cluster analysis identified eight clusters of fatal incidents and six clusters of incidents involving injury. Comparisons showed that although the airbag clusters across the two severity levels have the same most frequent terms, the circumstances around the incidents differ. The time trends show clear increases in complaints surrounding the Ford/Firestone tire recall and the Toyota unintended acceleration recall. Increases in complaints may be partially driven by these recall announcements and the associated media attention. Text mining can reveal useful information from free-response databases that would otherwise be prohibitively time-consuming and difficult to summarize manually. Text mining can extend human analysis capabilities for large free-response databases to support earlier detection of problems and more timely safety interventions.

Approach for Text Classification Based on the Similarity Measurement between Normal Cloud Models

PubMed Central

Dai, Jin; Liu, Xin

2014-01-01

The similarity between objects is the core research area of data mining. In order to reduce the interference of the uncertainty of nature language, a similarity measurement between normal cloud models is adopted to text classification research. On this basis, a novel text classifier based on cloud concept jumping up (CCJU-TC) is proposed. It can efficiently accomplish conversion between qualitative concept and quantitative data. Through the conversion from text set to text information table based on VSM model, the text qualitative concept, which is extraction from the same category, is jumping up as a whole category concept. According to the cloud similarity between the test text and each category concept, the test text is assigned to the most similar category. By the comparison among different text classifiers in different feature selection set, it fully proves that not only does CCJU-TC have a strong ability to adapt to the different text features, but also the classification performance is also better than the traditional classifiers. PMID:24711737

A Framework for Text Mining in Scientometric Study: A Case Study in Biomedicine Publications

NASA Astrophysics Data System (ADS)

Silalahi, V. M. M.; Hardiyati, R.; Nadhiroh, I. M.; Handayani, T.; Rahmaida, R.; Amelia, M.

2018-04-01

The data of Indonesians research publications in the domain of biomedicine has been collected to be text mined for the purpose of a scientometric study. The goal is to build a predictive model that provides a classification of research publications on the potency for downstreaming. The model is based on the drug development processes adapted from the literatures. An effort is described to build the conceptual model and the development of a corpus on the research publications in the domain of Indonesian biomedicine. Then an investigation is conducted relating to the problems associated with building a corpus and validating the model. Based on our experience, a framework is proposed to manage the scientometric study based on text mining. Our method shows the effectiveness of conducting a scientometric study based on text mining in order to get a valid classification model. This valid model is mainly supported by the iterative and close interactions with the domain experts starting from identifying the issues, building a conceptual model, to the labelling, validation and results interpretation.

Thematic clustering of text documents using an EM-based approach

PubMed Central

2012-01-01

Clustering textual contents is an important step in mining useful information on the web or other text-based resources. The common task in text clustering is to handle text in a multi-dimensional space, and to partition documents into groups, where each group contains documents that are similar to each other. However, this strategy lacks a comprehensive view for humans in general since it cannot explain the main subject of each cluster. Utilizing semantic information can solve this problem, but it needs a well-defined ontology or pre-labeled gold standard set. In this paper, we present a thematic clustering algorithm for text documents. Given text, subject terms are extracted and used for clustering documents in a probabilistic framework. An EM approach is used to ensure documents are assigned to correct subjects, hence it converges to a locally optimal solution. The proposed method is distinctive because its results are sufficiently explanatory for human understanding as well as efficient for clustering performance. The experimental results show that the proposed method provides a competitive performance compared to other state-of-the-art approaches. We also show that the extracted themes from the MEDLINE® dataset represent the subjects of clusters reasonably well. PMID:23046528

Managing the data deluge: data-driven GO category assignment improves while complexity of functional annotation increases.

PubMed

Gobeill, Julien; Pasche, Emilie; Vishnyakova, Dina; Ruch, Patrick

2013-01-01

The available curated data lag behind current biological knowledge contained in the literature. Text mining can assist biologists and curators to locate and access this knowledge, for instance by characterizing the functional profile of publications. Gene Ontology (GO) category assignment in free text already supports various applications, such as powering ontology-based search engines, finding curation-relevant articles (triage) or helping the curator to identify and encode functions. Popular text mining tools for GO classification are based on so called thesaurus-based--or dictionary-based--approaches, which exploit similarities between the input text and GO terms themselves. But their effectiveness remains limited owing to the complex nature of GO terms, which rarely occur in text. In contrast, machine learning approaches exploit similarities between the input text and already curated instances contained in a knowledge base to infer a functional profile. GO Annotations (GOA) and MEDLINE make possible to exploit a growing amount of curated abstracts (97 000 in November 2012) for populating this knowledge base. Our study compares a state-of-the-art thesaurus-based system with a machine learning system (based on a k-Nearest Neighbours algorithm) for the task of proposing a functional profile for unseen MEDLINE abstracts, and shows how resources and performances have evolved. Systems are evaluated on their ability to propose for a given abstract the GO terms (2.8 on average) used for curation in GOA. We show that since 2006, although a massive effort was put into adding synonyms in GO (+300%), our thesaurus-based system effectiveness is rather constant, reaching from 0.28 to 0.31 for Recall at 20 (R20). In contrast, thanks to its knowledge base growth, our machine learning system has steadily improved, reaching from 0.38 in 2006 to 0.56 for R20 in 2012. Integrated in semi-automatic workflows or in fully automatic pipelines, such systems are more and more efficient to provide assistance to biologists. DATABASE URL: http://eagl.unige.ch/GOCat/

A Node Linkage Approach for Sequential Pattern Mining

PubMed Central

Navarro, Osvaldo; Cumplido, René; Villaseñor-Pineda, Luis; Feregrino-Uribe, Claudia; Carrasco-Ochoa, Jesús Ariel

2014-01-01

Sequential Pattern Mining is a widely addressed problem in data mining, with applications such as analyzing Web usage, examining purchase behavior, and text mining, among others. Nevertheless, with the dramatic increase in data volume, the current approaches prove inefficient when dealing with large input datasets, a large number of different symbols and low minimum supports. In this paper, we propose a new sequential pattern mining algorithm, which follows a pattern-growth scheme to discover sequential patterns. Unlike most pattern growth algorithms, our approach does not build a data structure to represent the input dataset, but instead accesses the required sequences through pseudo-projection databases, achieving better runtime and reducing memory requirements. Our algorithm traverses the search space in a depth-first fashion and only preserves in memory a pattern node linkage and the pseudo-projections required for the branch being explored at the time. Experimental results show that our new approach, the Node Linkage Depth-First Traversal algorithm (NLDFT), has better performance and scalability in comparison with state of the art algorithms. PMID:24933123

THE FUTURE OF COMPUTER-BASED TOXICITY PREDICTION: MECHANISM-BASED MODELS VS. INFORMATION MINING APPROACHES

EPA Science Inventory

The Future of Computer-Based Toxicity Prediction:
Mechanism-Based Models vs. Information Mining Approaches

When we speak of computer-based toxicity prediction, we are generally referring to a broad array of approaches which rely primarily upon chemical structure ...

Constructing Model of Relationship among Behaviors and Injuries to Products Based on Large Scale Text Data on Injuries

NASA Astrophysics Data System (ADS)

Nomori, Koji; Kitamura, Koji; Motomura, Yoichi; Nishida, Yoshifumi; Yamanaka, Tatsuhiro; Komatsubara, Akinori

In Japan, childhood injury prevention is urgent issue. Safety measures through creating knowledge of injury data are essential for preventing childhood injuries. Especially the injury prevention approach by product modification is very important. The risk assessment is one of the most fundamental methods to design safety products. The conventional risk assessment has been carried out subjectively because product makers have poor data on injuries. This paper deals with evidence-based risk assessment, in which artificial intelligence technologies are strongly needed. This paper describes a new method of foreseeing usage of products, which is the first step of the evidence-based risk assessment, and presents a retrieval system of injury data. The system enables a product designer to foresee how children use a product and which types of injuries occur due to the product in daily environment. The developed system consists of large scale injury data, text mining technology and probabilistic modeling technology. Large scale text data on childhood injuries was collected from medical institutions by an injury surveillance system. Types of behaviors to a product were derived from the injury text data using text mining technology. The relationship among products, types of behaviors, types of injuries and characteristics of children was modeled by Bayesian Network. The fundamental functions of the developed system and examples of new findings obtained by the system are reported in this paper.

Protein-protein interaction predictions using text mining methods.

PubMed

Papanikolaou, Nikolas; Pavlopoulos, Georgios A; Theodosiou, Theodosios; Iliopoulos, Ioannis

2015-03-01

It is beyond any doubt that proteins and their interactions play an essential role in most complex biological processes. The understanding of their function individually, but also in the form of protein complexes is of a great importance. Nowadays, despite the plethora of various high-throughput experimental approaches for detecting protein-protein interactions, many computational methods aiming to predict new interactions have appeared and gained interest. In this review, we focus on text-mining based computational methodologies, aiming to extract information for proteins and their interactions from public repositories such as literature and various biological databases. We discuss their strengths, their weaknesses and how they complement existing experimental techniques by simultaneously commenting on the biological databases which hold such information and the benchmark datasets that can be used for evaluating new tools. Copyright © 2014 Elsevier Inc. All rights reserved.

Extracting semantically enriched events from biomedical literature

PubMed Central

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

Extracting semantically enriched events from biomedical literature.

PubMed

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.

A method for integrating and ranking the evidence for biochemical pathways by mining reactions from text

PubMed Central

Miwa, Makoto; Ohta, Tomoko; Rak, Rafal; Rowley, Andrew; Kell, Douglas B.; Pyysalo, Sampo; Ananiadou, Sophia

2013-01-01

Motivation: To create, verify and maintain pathway models, curators must discover and assess knowledge distributed over the vast body of biological literature. Methods supporting these tasks must understand both the pathway model representations and the natural language in the literature. These methods should identify and order documents by relevance to any given pathway reaction. No existing system has addressed all aspects of this challenge. Method: We present novel methods for associating pathway model reactions with relevant publications. Our approach extracts the reactions directly from the models and then turns them into queries for three text mining-based MEDLINE literature search systems. These queries are executed, and the resulting documents are combined and ranked according to their relevance to the reactions of interest. We manually annotate document-reaction pairs with the relevance of the document to the reaction and use this annotation to study several ranking methods, using various heuristic and machine-learning approaches. Results: Our evaluation shows that the annotated document-reaction pairs can be used to create a rule-based document ranking system, and that machine learning can be used to rank documents by their relevance to pathway reactions. We find that a Support Vector Machine-based system outperforms several baselines and matches the performance of the rule-based system. The success of the query extraction and ranking methods are used to update our existing pathway search system, PathText. Availability: An online demonstration of PathText 2 and the annotated corpus are available for research purposes at http://www.nactem.ac.uk/pathtext2/. Contact: makoto.miwa@manchester.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23813008

Detection and Evaluation of Cheating on College Exams Using Supervised Classification

ERIC Educational Resources Information Center

Cavalcanti, Elmano Ramalho; Pires, Carlos Eduardo; Cavalcanti, Elmano Pontes; Pires, Vládia Freire

2012-01-01

Text mining has been used for various purposes, such as document classification and extraction of domain-specific information from text. In this paper we present a study in which text mining methodology and algorithms were properly employed for academic dishonesty (cheating) detection and evaluation on open-ended college exams, based on document…

Text mining applied to electronic cardiovascular procedure reports to identify patients with trileaflet aortic stenosis and coronary artery disease.

PubMed

Small, Aeron M; Kiss, Daniel H; Zlatsin, Yevgeny; Birtwell, David L; Williams, Heather; Guerraty, Marie A; Han, Yuchi; Anwaruddin, Saif; Holmes, John H; Chirinos, Julio A; Wilensky, Robert L; Giri, Jay; Rader, Daniel J

2017-08-01

Interrogation of the electronic health record (EHR) using billing codes as a surrogate for diagnoses of interest has been widely used for clinical research. However, the accuracy of this methodology is variable, as it reflects billing codes rather than severity of disease, and depends on the disease and the accuracy of the coding practitioner. Systematic application of text mining to the EHR has had variable success for the detection of cardiovascular phenotypes. We hypothesize that the application of text mining algorithms to cardiovascular procedure reports may be a superior method to identify patients with cardiovascular conditions of interest. We adapted the Oracle product Endeca, which utilizes text mining to identify terms of interest from a NoSQL-like database, for purposes of searching cardiovascular procedure reports and termed the tool "PennSeek". We imported 282,569 echocardiography reports representing 81,164 individuals and 27,205 cardiac catheterization reports representing 14,567 individuals from non-searchable databases into PennSeek. We then applied clinical criteria to these reports in PennSeek to identify patients with trileaflet aortic stenosis (TAS) and coronary artery disease (CAD). Accuracy of patient identification by text mining through PennSeek was compared with ICD-9 billing codes. Text mining identified 7115 patients with TAS and 9247 patients with CAD. ICD-9 codes identified 8272 patients with TAS and 6913 patients with CAD. 4346 patients with AS and 6024 patients with CAD were identified by both approaches. A randomly selected sample of 200-250 patients uniquely identified by text mining was compared with 200-250 patients uniquely identified by billing codes for both diseases. We demonstrate that text mining was superior, with a positive predictive value (PPV) of 0.95 compared to 0.53 by ICD-9 for TAS, and a PPV of 0.97 compared to 0.86 for CAD. These results highlight the superiority of text mining algorithms applied to electronic cardiovascular procedure reports in the identification of phenotypes of interest for cardiovascular research. Copyright © 2017. Published by Elsevier Inc.

Supervised Learning Based Hypothesis Generation from Biomedical Literature.

PubMed

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.

Mining biomedical images towards valuable information retrieval in biomedical and life sciences

PubMed Central

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

Text and Structural Data Mining of Influenza Mentions in Web and Social Media

DOE Office of Scientific and Technical Information (OSTI.GOV)

Corley, Courtney D.; Cook, Diane; Mikler, Armin R.

Text and structural data mining of Web and social media (WSM) provides a novel disease surveillance resource and can identify online communities for targeted public health communications (PHC) to assure wide dissemination of pertinent information. WSM that mention influenza are harvested over a 24-week period, 5-October-2008 to 21-March-2009. Link analysis reveals communities for targeted PHC. Text mining is shown to identify trends in flu posts that correlate to real-world influenza-like-illness patient report data. We also bring to bear a graph-based data mining technique to detect anomalies among flu blogs connected by publisher type, links, and user-tags.

A Cloud-based Approach to Medical NLP

PubMed Central

Chard, Kyle; Russell, Michael; Lussier, Yves A.; Mendonça, Eneida A; Silverstein, Jonathan C.

2011-01-01

Natural Language Processing (NLP) enables access to deep content embedded in medical texts. To date, NLP has not fulfilled its promise of enabling robust clinical encoding, clinical use, quality improvement, and research. We submit that this is in part due to poor accessibility, scalability, and flexibility of NLP systems. We describe here an approach and system which leverages cloud-based approaches such as virtual machines and Representational State Transfer (REST) to extract, process, synthesize, mine, compare/contrast, explore, and manage medical text data in a flexibly secure and scalable architecture. Available architectures in which our Smntx (pronounced as semantics) system can be deployed include: virtual machines in a HIPAA-protected hospital environment, brought up to run analysis over bulk data and destroyed in a local cloud; a commercial cloud for a large complex multi-institutional trial; and within other architectures such as caGrid, i2b2, or NHIN. PMID:22195072

A cloud-based approach to medical NLP.

PubMed

Chard, Kyle; Russell, Michael; Lussier, Yves A; Mendonça, Eneida A; Silverstein, Jonathan C

2011-01-01

Natural Language Processing (NLP) enables access to deep content embedded in medical texts. To date, NLP has not fulfilled its promise of enabling robust clinical encoding, clinical use, quality improvement, and research. We submit that this is in part due to poor accessibility, scalability, and flexibility of NLP systems. We describe here an approach and system which leverages cloud-based approaches such as virtual machines and Representational State Transfer (REST) to extract, process, synthesize, mine, compare/contrast, explore, and manage medical text data in a flexibly secure and scalable architecture. Available architectures in which our Smntx (pronounced as semantics) system can be deployed include: virtual machines in a HIPAA-protected hospital environment, brought up to run analysis over bulk data and destroyed in a local cloud; a commercial cloud for a large complex multi-institutional trial; and within other architectures such as caGrid, i2b2, or NHIN.

Text mining patents for biomedical knowledge.

PubMed

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.

Evaluation of a rule-based method for epidemiological document classification towards the automation of systematic reviews.

PubMed

Karystianis, George; Thayer, Kristina; Wolfe, Mary; Tsafnat, Guy

2017-06-01

Most data extraction efforts in epidemiology are focused on obtaining targeted information from clinical trials. In contrast, limited research has been conducted on the identification of information from observational studies, a major source for human evidence in many fields, including environmental health. The recognition of key epidemiological information (e.g., exposures) through text mining techniques can assist in the automation of systematic reviews and other evidence summaries. We designed and applied a knowledge-driven, rule-based approach to identify targeted information (study design, participant population, exposure, outcome, confounding factors, and the country where the study was conducted) from abstracts of epidemiological studies included in several systematic reviews of environmental health exposures. The rules were based on common syntactical patterns observed in text and are thus not specific to any systematic review. To validate the general applicability of our approach, we compared the data extracted using our approach versus hand curation for 35 epidemiological study abstracts manually selected for inclusion in two systematic reviews. The returned F-score, precision, and recall ranged from 70% to 98%, 81% to 100%, and 54% to 97%, respectively. The highest precision was observed for exposure, outcome and population (100%) while recall was best for exposure and study design with 97% and 89%, respectively. The lowest recall was observed for the population (54%), which also had the lowest F-score (70%). The generated performance of our text-mining approach demonstrated encouraging results for the identification of targeted information from observational epidemiological study abstracts related to environmental exposures. We have demonstrated that rules based on generic syntactic patterns in one corpus can be applied to other observational study design by simple interchanging the dictionaries aiming to identify certain characteristics (i.e., outcomes, exposures). At the document level, the recognised information can assist in the selection and categorization of studies included in a systematic review. Copyright © 2017 Elsevier Inc. All rights reserved.

Text Mining to Support Gene Ontology Curation and Vice Versa.

PubMed

Ruch, Patrick

2017-01-01

In this chapter, we explain how text mining can support the curation of molecular biology databases dealing with protein functions. We also show how curated data can play a disruptive role in the developments of text mining methods. We review a decade of efforts to improve the automatic assignment of Gene Ontology (GO) descriptors, the reference ontology for the characterization of genes and gene products. To illustrate the high potential of this approach, we compare the performances of an automatic text categorizer and show a large improvement of +225 % in both precision and recall on benchmarked data. We argue that automatic text categorization functions can ultimately be embedded into a Question-Answering (QA) system to answer questions related to protein functions. Because GO descriptors can be relatively long and specific, traditional QA systems cannot answer such questions. A new type of QA system, so-called Deep QA which uses machine learning methods trained with curated contents, is thus emerging. Finally, future advances of text mining instruments are directly dependent on the availability of high-quality annotated contents at every curation step. Databases workflows must start recording explicitly all the data they curate and ideally also some of the data they do not curate.

pubmed.mineR: an R package with text-mining algorithms to analyse PubMed abstracts.

PubMed

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.

Uncovering text mining: A survey of current work on web-based epidemic intelligence

PubMed Central

Collier, Nigel

2012-01-01

Real world pandemics such as SARS 2002 as well as popular fiction like the movie Contagion graphically depict the health threat of a global pandemic and the key role of epidemic intelligence (EI). While EI relies heavily on established indicator sources a new class of methods based on event alerting from unstructured digital Internet media is rapidly becoming acknowledged within the public health community. At the heart of automated information gathering systems is a technology called text mining. My contribution here is to provide an overview of the role that text mining technology plays in detecting epidemics and to synthesise my existing research on the BioCaster project. PMID:22783909

Biomedical hypothesis generation by text mining and gene prioritization.

PubMed

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.

Mining biomedical images towards valuable information retrieval in biomedical and life sciences.

PubMed

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.

Research on Classification of Chinese Text Data Based on SVM

NASA Astrophysics Data System (ADS)

Lin, Yuan; Yu, Hongzhi; Wan, Fucheng; Xu, Tao

2017-09-01

Data Mining has important application value in today’s industry and academia. Text classification is a very important technology in data mining. At present, there are many mature algorithms for text classification. KNN, NB, AB, SVM, decision tree and other classification methods all show good classification performance. Support Vector Machine’ (SVM) classification method is a good classifier in machine learning research. This paper will study the classification effect based on the SVM method in the Chinese text data, and use the support vector machine method in the chinese text to achieve the classify chinese text, and to able to combination of academia and practical application.

Mines, Quarries and Landscape. Visuality and Transformation

NASA Astrophysics Data System (ADS)

Jimeno, Carlos López; Torrijos, Ignacio Díez; González, Carmen Mataix

2016-06-01

In this paper a review of two basic concepts is carried out: scenery and landscape integration, proposing a new concept: "visuality", alternative to the classical "visibility" used in landscape studies related to mining activity, which explores the qualitative aspects that define the visual relationships between observer and environment. In relation to landscape integration studies, some reflections on substantive issues are made which induce certain prejudices at the time of addressing the issue of mining operations landscape integration, and some guidance and integration strategies are formulated. In the second part of the text, a new approach to the landscape integration of mines and quarries is raised, closely linked to the concept of visuality which are based on a basic goal: the re-qualification of the place, and give innovative answers to re-qualify the place and show how to catch the opportunity in the deep transformation generated by the development of mining activities. As a conclusion, a case study is presented in the last section, the landscape integration study conducted on marble exploitations Coto Pinos (Alicante, Spain), considered the largest ornamental rock quarry in Europe.

Using text-mining techniques in electronic patient records to identify ADRs from medicine use.

PubMed

Warrer, Pernille; Hansen, Ebba Holme; Juhl-Jensen, Lars; Aagaard, Lise

2012-05-01

This literature review included studies that use text-mining techniques in narrative documents stored in electronic patient records (EPRs) to investigate ADRs. We searched PubMed, Embase, Web of Science and International Pharmaceutical Abstracts without restrictions from origin until July 2011. We included empirically based studies on text mining of electronic patient records (EPRs) that focused on detecting ADRs, excluding those that investigated adverse events not related to medicine use. We extracted information on study populations, EPR data sources, frequencies and types of the identified ADRs, medicines associated with ADRs, text-mining algorithms used and their performance. Seven studies, all from the United States, were eligible for inclusion in the review. Studies were published from 2001, the majority between 2009 and 2010. Text-mining techniques varied over time from simple free text searching of outpatient visit notes and inpatient discharge summaries to more advanced techniques involving natural language processing (NLP) of inpatient discharge summaries. Performance appeared to increase with the use of NLP, although many ADRs were still missed. Due to differences in study design and populations, various types of ADRs were identified and thus we could not make comparisons across studies. The review underscores the feasibility and potential of text mining to investigate narrative documents in EPRs for ADRs. However, more empirical studies are needed to evaluate whether text mining of EPRs can be used systematically to collect new information about ADRs. © 2011 The Authors. British Journal of Clinical Pharmacology © 2011 The British Pharmacological Society.

Using text-mining techniques in electronic patient records to identify ADRs from medicine use

PubMed Central

Warrer, Pernille; Hansen, Ebba Holme; Juhl-Jensen, Lars; Aagaard, Lise

2012-01-01

This literature review included studies that use text-mining techniques in narrative documents stored in electronic patient records (EPRs) to investigate ADRs. We searched PubMed, Embase, Web of Science and International Pharmaceutical Abstracts without restrictions from origin until July 2011. We included empirically based studies on text mining of electronic patient records (EPRs) that focused on detecting ADRs, excluding those that investigated adverse events not related to medicine use. We extracted information on study populations, EPR data sources, frequencies and types of the identified ADRs, medicines associated with ADRs, text-mining algorithms used and their performance. Seven studies, all from the United States, were eligible for inclusion in the review. Studies were published from 2001, the majority between 2009 and 2010. Text-mining techniques varied over time from simple free text searching of outpatient visit notes and inpatient discharge summaries to more advanced techniques involving natural language processing (NLP) of inpatient discharge summaries. Performance appeared to increase with the use of NLP, although many ADRs were still missed. Due to differences in study design and populations, various types of ADRs were identified and thus we could not make comparisons across studies. The review underscores the feasibility and potential of text mining to investigate narrative documents in EPRs for ADRs. However, more empirical studies are needed to evaluate whether text mining of EPRs can be used systematically to collect new information about ADRs. PMID:22122057

ANDSystem: an Associative Network Discovery System for automated literature mining in the field of biology

PubMed Central

2015-01-01

Background Sufficient knowledge of molecular and genetic interactions, which comprise the entire basis of the functioning of living systems, is one of the necessary requirements for successfully answering almost any research question in the field of biology and medicine. To date, more than 24 million scientific papers can be found in PubMed, with many of them containing descriptions of a wide range of biological processes. The analysis of such tremendous amounts of data requires the use of automated text-mining approaches. Although a handful of tools have recently been developed to meet this need, none of them provide error-free extraction of highly detailed information. Results The ANDSystem package was developed for the reconstruction and analysis of molecular genetic networks based on an automated text-mining technique. It provides a detailed description of the various types of interactions between genes, proteins, microRNA's, metabolites, cellular components, pathways and diseases, taking into account the specificity of cell lines and organisms. Although the accuracy of ANDSystem is comparable to other well known text-mining tools, such as Pathway Studio and STRING, it outperforms them in having the ability to identify an increased number of interaction types. Conclusion The use of ANDSystem, in combination with Pathway Studio and STRING, can improve the quality of the automated reconstruction of molecular and genetic networks. ANDSystem should provide a useful tool for researchers working in a number of different fields, including biology, biotechnology, pharmacology and medicine. PMID:25881313

The Contribution of the Vaccine Adverse Event Text Mining System to the Classification of Possible Guillain-Barré Syndrome Reports

PubMed Central

Botsis, T.; Woo, E. J.; Ball, R.

2013-01-01

Background We previously demonstrated that a general purpose text mining system, the Vaccine adverse event Text Mining (VaeTM) system, could be used to automatically classify reports of an-aphylaxis for post-marketing safety surveillance of vaccines. Objective To evaluate the ability of VaeTM to classify reports to the Vaccine Adverse Event Reporting System (VAERS) of possible Guillain-Barré Syndrome (GBS). Methods We used VaeTM to extract the key diagnostic features from the text of reports in VAERS. Then, we applied the Brighton Collaboration (BC) case definition for GBS, and an information retrieval strategy (i.e. the vector space model) to quantify the specific information that is included in the key features extracted by VaeTM and compared it with the encoded information that is already stored in VAERS as Medical Dictionary for Regulatory Activities (MedDRA) Preferred Terms (PTs). We also evaluated the contribution of the primary (diagnosis and cause of death) and secondary (second level diagnosis and symptoms) diagnostic VaeTM-based features to the total VaeTM-based information. Results MedDRA captured more information and better supported the classification of reports for GBS than VaeTM (AUC: 0.904 vs. 0.777); the lower performance of VaeTM is likely due to the lack of extraction by VaeTM of specific laboratory results that are included in the BC criteria for GBS. On the other hand, the VaeTM-based classification exhibited greater specificity than the MedDRA-based approach (94.96% vs. 87.65%). Most of the VaeTM-based information was contained in the secondary diagnostic features. Conclusion For GBS, clinical signs and symptoms alone are not sufficient to match MedDRA coding for purposes of case classification, but are preferred if specificity is the priority. PMID:23650490

Method to Select Technical Terms for Glossaries in Support of Joint Task Force Operations

DTIC Science & Technology

2012-01-01

have been prohibitively time-consuming. Instead, we identified two publicly available terminology extractor tools: TerMine (NaCTEM, 2011) and Alchemy ...and that from the latter, by high recall. The Alchemy approach contrasts with that used in TerMine in that Alchemy will process the text with...information categories, such as person, location, and organization, in addition to returning topic keywords. Output from both TerMine and Alchemy

Can abstract screening workload be reduced using text mining? User experiences of the tool Rayyan.

PubMed

Olofsson, Hanna; Brolund, Agneta; Hellberg, Christel; Silverstein, Rebecca; Stenström, Karin; Österberg, Marie; Dagerhamn, Jessica

2017-09-01

One time-consuming aspect of conducting systematic reviews is the task of sifting through abstracts to identify relevant studies. One promising approach for reducing this burden uses text mining technology to identify those abstracts that are potentially most relevant for a project, allowing those abstracts to be screened first. To examine the effectiveness of the text mining functionality of the abstract screening tool Rayyan. User experiences were collected. Rayyan was used to screen abstracts for 6 reviews in 2015. After screening 25%, 50%, and 75% of the abstracts, the screeners logged the relevant references identified. A survey was sent to users. After screening half of the search result with Rayyan, 86% to 99% of the references deemed relevant to the study were identified. Of those studies included in the final reports, 96% to 100% were already identified in the first half of the screening process. Users rated Rayyan 4.5 out of 5. The text mining function in Rayyan successfully helped reviewers identify relevant studies early in the screening process. Copyright © 2017 John Wiley & Sons, Ltd.

Building a protein name dictionary from full text: a machine learning term extraction approach.

PubMed

Shi, Lei; Campagne, Fabien

2005-04-07

The majority of information in the biological literature resides in full text articles, instead of abstracts. Yet, abstracts remain the focus of many publicly available literature data mining tools. Most literature mining tools rely on pre-existing lexicons of biological names, often extracted from curated gene or protein databases. This is a limitation, because such databases have low coverage of the many name variants which are used to refer to biological entities in the literature. We present an approach to recognize named entities in full text. The approach collects high frequency terms in an article, and uses support vector machines (SVM) to identify biological entity names. It is also computationally efficient and robust to noise commonly found in full text material. We use the method to create a protein name dictionary from a set of 80,528 full text articles. Only 8.3% of the names in this dictionary match SwissProt description lines. We assess the quality of the dictionary by studying its protein name recognition performance in full text. This dictionary term lookup method compares favourably to other published methods, supporting the significance of our direct extraction approach. The method is strong in recognizing name variants not found in SwissProt.

Building a protein name dictionary from full text: a machine learning term extraction approach

PubMed Central

Shi, Lei; Campagne, Fabien

2005-01-01

Background The majority of information in the biological literature resides in full text articles, instead of abstracts. Yet, abstracts remain the focus of many publicly available literature data mining tools. Most literature mining tools rely on pre-existing lexicons of biological names, often extracted from curated gene or protein databases. This is a limitation, because such databases have low coverage of the many name variants which are used to refer to biological entities in the literature. Results We present an approach to recognize named entities in full text. The approach collects high frequency terms in an article, and uses support vector machines (SVM) to identify biological entity names. It is also computationally efficient and robust to noise commonly found in full text material. We use the method to create a protein name dictionary from a set of 80,528 full text articles. Only 8.3% of the names in this dictionary match SwissProt description lines. We assess the quality of the dictionary by studying its protein name recognition performance in full text. Conclusion This dictionary term lookup method compares favourably to other published methods, supporting the significance of our direct extraction approach. The method is strong in recognizing name variants not found in SwissProt. PMID:15817129

Mining the Geophysical Research Abstracts Corpus: Mapping the impact of Free and Open Source Software on the EGU Divisions

NASA Astrophysics Data System (ADS)

Löwe, Peter; Klump, Jens; Robertson, Jesse

2015-04-01

Text mining is commonly employed as a tool in data science to investigate and chart emergent information from corpora of research abstracts, such as the Geophysical Research Abstracts (GRA) published by Copernicus. In this context current standards, such as persistent identifiers like DOI and ORCID, allow us to trace, cite and map links between journal publications, the underlying research data and scientific software. This network can be expressed as a directed graph which enables us to chart networks of cooperation and innovation, thematic foci and the locations of research communities in time and space. However, this approach of data science, focusing on the research process in a self-referential manner, rather than the topical work, is still in a developing stage. Scientific work presented at the EGU General Assembly is often the first step towards new approaches and innovative ideas to the geospatial community. It represents a rich, deep and heterogeneous source of geoscientific thought. This corpus is a significant data source for data science, which has not been analysed on this scale previously. In this work, the corpus of the Geophysical Research Abstracts is used for the first time as a data base for analyses of topical text mining. For this, we used a sturdy and customizable software framework, based on the work of Schmitt et al. [1]. For the analysis we used the High Performance Computing infrastructure of the German Research Centre for Geosciences GFZ in Potsdam, Germany. Here, we report on the first results from the analysis of the continuous spreading the of use of Free and Open Source Software Tools (FOSS) within the EGU communities, mapping the general increase of FOSS-themed GRA articles in the last decade and the developing spatial patterns of involved parties and FOSS topics. References: [1] Schmitt, L. M., Christianson, K.T, Gupta R..: Linguistic Computing with UNIX Tools, in Kao, A., Poteet S.R. (Eds.): Natural Language processing and Text Mining, Springer, 2007. doi:10.1007/978-1-84628-754-1_12.

Using uncertainty to link and rank evidence from biomedical literature for model curation

PubMed Central

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

Using uncertainty to link and rank evidence from biomedical literature for model curation.

PubMed

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.

Mining of Business-Oriented Conversations at a Call Center

NASA Astrophysics Data System (ADS)

Takeuchi, Hironori; Nasukawa, Tetsuya; Watanabe, Hideo

Recently it has become feasible to transcribe textual records from telephone conversations at call centers by using automatic speech recognition. In this research, we extended a text mining system for call summary records and constructed a conversation mining system for the business-oriented conversations at the call center. To acquire useful business insights from the conversational data through the text mining system, it is critical to identify appropriate textual segments and expressions as the viewpoints to focus on. In the analysis of call summary data using a text mining system, some experts defined the viewpoints for the analysis by looking at some sample records and by preparing the dictionaries based on frequent keywords in the sample dataset. However with conversations it is difficult to identify such viewpoints manually and in advance because the target data consists of complete transcripts that are often lengthy and redundant. In this research, we defined a model of the business-oriented conversations and proposed a mining method to identify segments that have impacts on the outcomes of the conversations and can then extract useful expressions in each of these identified segments. In the experiment, we processed the real datasets from a car rental service center and constructed a mining system. With this system, we show the effectiveness of the method based on the defined conversation model.

[Text mining, a method for computer-assisted analysis of scientific texts, demonstrated by an analysis of author networks].

PubMed

Hahn, P; Dullweber, F; Unglaub, F; Spies, C K

2014-06-01

Searching for relevant publications is becoming more difficult with the increasing number of scientific articles. Text mining as a specific form of computer-based data analysis may be helpful in this context. Highlighting relations between authors and finding relevant publications concerning a specific subject using text analysis programs are illustrated graphically by 2 performed examples. © Georg Thieme Verlag KG Stuttgart · New York.

Biocuration workflows and text mining: overview of the BioCreative 2012 Workshop Track II.

PubMed

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/.

Motif-Based Text Mining of Microbial Metagenome Redundancy Profiling Data for Disease Classification.

PubMed

Wang, Yin; Li, Rudong; Zhou, Yuhua; Ling, Zongxin; Guo, Xiaokui; Xie, Lu; Liu, Lei

2016-01-01

Text data of 16S rRNA are informative for classifications of microbiota-associated diseases. However, the raw text data need to be systematically processed so that features for classification can be defined/extracted; moreover, the high-dimension feature spaces generated by the text data also pose an additional difficulty. Here we present a Phylogenetic Tree-Based Motif Finding algorithm (PMF) to analyze 16S rRNA text data. By integrating phylogenetic rules and other statistical indexes for classification, we can effectively reduce the dimension of the large feature spaces generated by the text datasets. Using the retrieved motifs in combination with common classification methods, we can discriminate different samples of both pneumonia and dental caries better than other existing methods. We extend the phylogenetic approaches to perform supervised learning on microbiota text data to discriminate the pathological states for pneumonia and dental caries. The results have shown that PMF may enhance the efficiency and reliability in analyzing high-dimension text data.

Redundancy in electronic health record corpora: analysis, impact on text mining performance and mitigation strategies.

PubMed

Cohen, Raphael; Elhadad, Michael; Elhadad, Noémie

2013-01-16

The increasing availability of Electronic Health Record (EHR) data and specifically free-text patient notes presents opportunities for phenotype extraction. Text-mining methods in particular can help disease modeling by mapping named-entities mentions to terminologies and clustering semantically related terms. EHR corpora, however, exhibit specific statistical and linguistic characteristics when compared with corpora in the biomedical literature domain. We focus on copy-and-paste redundancy: clinicians typically copy and paste information from previous notes when documenting a current patient encounter. Thus, within a longitudinal patient record, one expects to observe heavy redundancy. In this paper, we ask three research questions: (i) How can redundancy be quantified in large-scale text corpora? (ii) Conventional wisdom is that larger corpora yield better results in text mining. But how does the observed EHR redundancy affect text mining? Does such redundancy introduce a bias that distorts learned models? Or does the redundancy introduce benefits by highlighting stable and important subsets of the corpus? (iii) How can one mitigate the impact of redundancy on text mining? We analyze a large-scale EHR corpus and quantify redundancy both in terms of word and semantic concept repetition. We observe redundancy levels of about 30% and non-standard distribution of both words and concepts. We measure the impact of redundancy on two standard text-mining applications: collocation identification and topic modeling. We compare the results of these methods on synthetic data with controlled levels of redundancy and observe significant performance variation. Finally, we compare two mitigation strategies to avoid redundancy-induced bias: (i) a baseline strategy, keeping only the last note for each patient in the corpus; (ii) removing redundant notes with an efficient fingerprinting-based algorithm. (a)For text mining, preprocessing the EHR corpus with fingerprinting yields significantly better results. Before applying text-mining techniques, one must pay careful attention to the structure of the analyzed corpora. While the importance of data cleaning has been known for low-level text characteristics (e.g., encoding and spelling), high-level and difficult-to-quantify corpus characteristics, such as naturally occurring redundancy, can also hurt text mining. Fingerprinting enables text-mining techniques to leverage available data in the EHR corpus, while avoiding the bias introduced by redundancy.

Optimizing the Information Presentation on Mining Potential by using Web Services Technology with Restful Protocol

NASA Astrophysics Data System (ADS)

Abdillah, T.; Dai, R.; Setiawan, E.

2018-02-01

This study aims to develop the application of Web Services technology with RestFul Protocol to optimize the information presentation on mining potential. This study used User Interface Design approach for the information accuracy and relevance as well as the Web Service for the reliability in presenting the information. The results show that: the information accuracy and relevance regarding mining potential can be seen from the achievement of User Interface implementation in the application that is based on the following rules: The consideration of the appropriate colours and objects, the easiness of using the navigation, and users’ interaction with the applications that employs symbols and languages understood by the users; the information accuracy and relevance related to mining potential can be observed by the information presented by using charts and Tool Tip Text to help the users understand the provided chart/figure; the reliability of the information presentation is evident by the results of Web Services testing in Figure 4.5.6. This study finds out that User Interface Design and Web Services approaches (for the access of different Platform apps) are able to optimize the presentation. The results of this study can be used as a reference for software developers and Provincial Government of Gorontalo.

New directions in biomedical text annotation: definitions, guidelines and corpus construction

PubMed Central

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

PubstractHelper: A Web-based Text-Mining Tool for Marking Sentences in Abstracts from PubMed Using Multiple User-Defined Keywords.

PubMed

Chen, Chou-Cheng; Ho, Chung-Liang

2014-01-01

While a huge amount of information about biological literature can be obtained by searching the PubMed database, reading through all the titles and abstracts resulting from such a search for useful information is inefficient. Text mining makes it possible to increase this efficiency. Some websites use text mining to gather information from the PubMed database; however, they are database-oriented, using pre-defined search keywords while lacking a query interface for user-defined search inputs. We present the PubMed Abstract Reading Helper (PubstractHelper) website which combines text mining and reading assistance for an efficient PubMed search. PubstractHelper can accept a maximum of ten groups of keywords, within each group containing up to ten keywords. The principle behind the text-mining function of PubstractHelper is that keywords contained in the same sentence are likely to be related. PubstractHelper highlights sentences with co-occurring keywords in different colors. The user can download the PMID and the abstracts with color markings to be reviewed later. The PubstractHelper website can help users to identify relevant publications based on the presence of related keywords, which should be a handy tool for their research. http://bio.yungyun.com.tw/ATM/PubstractHelper.aspx and http://holab.med.ncku.edu.tw/ATM/PubstractHelper.aspx.

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

DeepText2GO: Improving large-scale protein function prediction with deep semantic text representation.

PubMed

You, Ronghui; Huang, Xiaodi; Zhu, Shanfeng

2018-06-06

As of April 2018, UniProtKB has collected more than 115 million protein sequences. Less than 0.15% of these proteins, however, have been associated with experimental GO annotations. As such, the use of automatic protein function prediction (AFP) to reduce this huge gap becomes increasingly important. The previous studies conclude that sequence homology based methods are highly effective in AFP. In addition, mining motif, domain, and functional information from protein sequences has been found very helpful for AFP. Other than sequences, alternative information sources such as text, however, may be useful for AFP as well. Instead of using BOW (bag of words) representation in traditional text-based AFP, we propose a new method called DeepText2GO that relies on deep semantic text representation, together with different kinds of available protein information such as sequence homology, families, domains, and motifs, to improve large-scale AFP. Furthermore, DeepText2GO integrates text-based methods with sequence-based ones by means of a consensus approach. Extensive experiments on the benchmark dataset extracted from UniProt/SwissProt have demonstrated that DeepText2GO significantly outperformed both text-based and sequence-based methods, validating its superiority. Copyright © 2018 Elsevier Inc. All rights reserved.

Graphics-based intelligent search and abstracting using Data Modeling

NASA Astrophysics Data System (ADS)

Jaenisch, Holger M.; Handley, James W.; Case, Carl T.; Songy, Claude G.

2002-11-01

This paper presents an autonomous text and context-mining algorithm that converts text documents into point clouds for visual search cues. This algorithm is applied to the task of data-mining a scriptural database comprised of the Old and New Testaments from the Bible and the Book of Mormon, Doctrine and Covenants, and the Pearl of Great Price. Results are generated which graphically show the scripture that represents the average concept of the database and the mining of the documents down to the verse level.

Clustering and Dimensionality Reduction to Discover Interesting Patterns in Binary Data

NASA Astrophysics Data System (ADS)

Palumbo, Francesco; D'Enza, Alfonso Iodice

The attention towards binary data coding increased consistently in the last decade due to several reasons. The analysis of binary data characterizes several fields of application, such as market basket analysis, DNA microarray data, image mining, text mining and web-clickstream mining. The paper illustrates two different approaches exploiting a profitable combination of clustering and dimensionality reduction for the identification of non-trivial association structures in binary data. An application in the Association Rules framework supports the theory with the empirical evidence.

WHAT INNOVATIVE APPROACHES CAN BE DEVELOPED FOR MINING SITES?

EPA Science Inventory

Mining is essential to maintain our way of life. However, based upon industry's reporting in the most recent Toxic Release Inventory (TRI), the primary sources of heavy metal releases to the environment are mining and mining related activities. The hard rock mining industry rel...

Application of text mining for customer evaluations in commercial banking

NASA Astrophysics Data System (ADS)

Tan, Jing; Du, Xiaojiang; Hao, Pengpeng; Wang, Yanbo J.

2015-07-01

Nowadays customer attrition is increasingly serious in commercial banks. To combat this problem roundly, mining customer evaluation texts is as important as mining customer structured data. In order to extract hidden information from customer evaluations, Textual Feature Selection, Classification and Association Rule Mining are necessary techniques. This paper presents all three techniques by using Chinese Word Segmentation, C5.0 and Apriori, and a set of experiments were run based on a collection of real textual data that includes 823 customer evaluations taken from a Chinese commercial bank. Results, consequent solutions, some advice for the commercial bank are given in this paper.

Full-text automated detection of surgical site infections secondary to neurosurgery in Rennes, France.

PubMed

Campillo-Gimenez, Boris; Garcelon, Nicolas; Jarno, Pascal; Chapplain, Jean Marc; Cuggia, Marc

2013-01-01

The surveillance of Surgical Site Infections (SSI) contributes to the management of risk in French hospitals. Manual identification of infections is costly, time-consuming and limits the promotion of preventive procedures by the dedicated teams. The introduction of alternative methods using automated detection strategies is promising to improve this surveillance. The present study describes an automated detection strategy for SSI in neurosurgery, based on textual analysis of medical reports stored in a clinical data warehouse. The method consists firstly, of enrichment and concept extraction from full-text reports using NOMINDEX, and secondly, text similarity measurement using a vector space model. The text detection was compared to the conventional strategy based on self-declaration and to the automated detection using the diagnosis-related group database. The text-mining approach showed the best detection accuracy, with recall and precision equal to 92% and 40% respectively, and confirmed the interest of reusing full-text medical reports to perform automated detection of SSI.

LimTox: a web tool for applied text mining of adverse event and toxicity associations of compounds, drugs and genes

PubMed Central

Cañada, Andres; Rabal, Obdulia; Oyarzabal, Julen; Valencia, Alfonso

2017-01-01

Abstract A considerable effort has been devoted to retrieve systematically information for genes and proteins as well as relationships between them. Despite the importance of chemical compounds and drugs as a central bio-entity in pharmacological and biological research, only a limited number of freely available chemical text-mining/search engine technologies are currently accessible. Here we present LimTox (Literature Mining for Toxicology), a web-based online biomedical search tool with special focus on adverse hepatobiliary reactions. It integrates a range of text mining, named entity recognition and information extraction components. LimTox relies on machine-learning, rule-based, pattern-based and term lookup strategies. This system processes scientific abstracts, a set of full text articles and medical agency assessment reports. Although the main focus of LimTox is on adverse liver events, it enables also basic searches for other organ level toxicity associations (nephrotoxicity, cardiotoxicity, thyrotoxicity and phospholipidosis). This tool supports specialized search queries for: chemical compounds/drugs, genes (with additional emphasis on key enzymes in drug metabolism, namely P450 cytochromes—CYPs) and biochemical liver markers. The LimTox website is free and open to all users and there is no login requirement. LimTox can be accessed at: http://limtox.bioinfo.cnio.es PMID:28531339

Text Mining in Biomedical Domain with Emphasis on Document Clustering.

PubMed

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.

Beyond accuracy: creating interoperable and scalable text-mining web services.

PubMed

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.

Reproducibility of studies on text mining for citation screening in systematic reviews: Evaluation and checklist.

PubMed

Olorisade, Babatunde Kazeem; Brereton, Pearl; Andras, Peter

2017-09-01

Independent validation of published scientific results through study replication is a pre-condition for accepting the validity of such results. In computation research, full replication is often unrealistic for independent results validation, therefore, study reproduction has been justified as the minimum acceptable standard to evaluate the validity of scientific claims. The application of text mining techniques to citation screening in the context of systematic literature reviews is a relatively young and growing computational field with high relevance for software engineering, medical research and other fields. However, there is little work so far on reproduction studies in the field. In this paper, we investigate the reproducibility of studies in this area based on information contained in published articles and we propose reporting guidelines that could improve reproducibility. The study was approached in two ways. Initially we attempted to reproduce results from six studies, which were based on the same raw dataset. Then, based on this experience, we identified steps considered essential to successful reproduction of text mining experiments and characterized them to measure how reproducible is a study given the information provided on these steps. 33 articles were systematically assessed for reproducibility using this approach. Our work revealed that it is currently difficult if not impossible to independently reproduce the results published in any of the studies investigated. The lack of information about the datasets used limits reproducibility of about 80% of the studies assessed. Also, information about the machine learning algorithms is inadequate in about 27% of the papers. On the plus side, the third party software tools used are mostly free and available. The reproducibility potential of most of the studies can be significantly improved if more attention is paid to information provided on the datasets used, how they were partitioned and utilized, and how any randomization was controlled. We introduce a checklist of information that needs to be provided in order to ensure that a published study can be reproduced. Copyright © 2017 Elsevier Inc. All rights reserved.

Chapter 2: The forestry reclamation approach

Treesearch

Jim Burger; Don Graves; Patrick Angel; Vic Davis; Carl Zipper

2017-01-01

The Forestry Reclamation Approach (FRA) is a method for reclaiming coal-mined land to forest under the federal Surface Mining Control and Reclamation Act of 1977 (SMCRA). The FRA is based on knowledge gained from both scientific research and experience (Fig. 2-1). The FRA can achieve cost-effective regulatory compliance for mine operators while creating productive...

Benchmarking infrastructure for mutation text mining

PubMed Central

2014-01-01

Background Experimental research on the automatic extraction of information about mutations from texts is greatly hindered by the lack of consensus evaluation infrastructure for the testing and benchmarking of mutation text mining systems. Results We propose a community-oriented annotation and benchmarking infrastructure to support development, testing, benchmarking, and comparison of mutation text mining systems. The design is based on semantic standards, where RDF is used to represent annotations, an OWL ontology provides an extensible schema for the data and SPARQL is used to compute various performance metrics, so that in many cases no programming is needed to analyze results from a text mining system. While large benchmark corpora for biological entity and relation extraction are focused mostly on genes, proteins, diseases, and species, our benchmarking infrastructure fills the gap for mutation information. The core infrastructure comprises (1) an ontology for modelling annotations, (2) SPARQL queries for computing performance metrics, and (3) a sizeable collection of manually curated documents, that can support mutation grounding and mutation impact extraction experiments. Conclusion We have developed the principal infrastructure for the benchmarking of mutation text mining tasks. The use of RDF and OWL as the representation for corpora ensures extensibility. The infrastructure is suitable for out-of-the-box use in several important scenarios and is ready, in its current state, for initial community adoption. PMID:24568600

Benchmarking infrastructure for mutation text mining.

PubMed

Klein, Artjom; Riazanov, Alexandre; Hindle, Matthew M; Baker, Christopher Jo

2014-02-25

Experimental research on the automatic extraction of information about mutations from texts is greatly hindered by the lack of consensus evaluation infrastructure for the testing and benchmarking of mutation text mining systems. We propose a community-oriented annotation and benchmarking infrastructure to support development, testing, benchmarking, and comparison of mutation text mining systems. The design is based on semantic standards, where RDF is used to represent annotations, an OWL ontology provides an extensible schema for the data and SPARQL is used to compute various performance metrics, so that in many cases no programming is needed to analyze results from a text mining system. While large benchmark corpora for biological entity and relation extraction are focused mostly on genes, proteins, diseases, and species, our benchmarking infrastructure fills the gap for mutation information. The core infrastructure comprises (1) an ontology for modelling annotations, (2) SPARQL queries for computing performance metrics, and (3) a sizeable collection of manually curated documents, that can support mutation grounding and mutation impact extraction experiments. We have developed the principal infrastructure for the benchmarking of mutation text mining tasks. The use of RDF and OWL as the representation for corpora ensures extensibility. The infrastructure is suitable for out-of-the-box use in several important scenarios and is ready, in its current state, for initial community adoption.

Text mining and natural language processing approaches for automatic categorization of lay requests to web-based expert forums.

PubMed

Himmel, Wolfgang; Reincke, Ulrich; Michelmann, Hans Wilhelm

2009-07-22

Both healthy and sick people increasingly use electronic media to obtain medical information and advice. For example, Internet users may send requests to Web-based expert forums, or so-called "ask the doctor" services. To automatically classify lay requests to an Internet medical expert forum using a combination of different text-mining strategies. We first manually classified a sample of 988 requests directed to a involuntary childlessness forum on the German website "Rund ums Baby" ("Everything about Babies") into one or more of 38 categories belonging to two dimensions ("subject matter" and "expectations"). After creating start and synonym lists, we calculated the average Cramer's V statistic for the association of each word with each category. We also used principle component analysis and singular value decomposition as further text-mining strategies. With these measures we trained regression models and determined, on the basis of best regression models, for any request the probability of belonging to each of the 38 different categories, with a cutoff of 50%. Recall and precision of a test sample were calculated as a measure of quality for the automatic classification. According to the manual classification of 988 documents, 102 (10%) documents fell into the category "in vitro fertilization (IVF)," 81 (8%) into the category "ovulation," 79 (8%) into "cycle," and 57 (6%) into "semen analysis." These were the four most frequent categories in the subject matter dimension (consisting of 32 categories). The expectation dimension comprised six categories; we classified 533 documents (54%) as "general information" and 351 (36%) as a wish for "treatment recommendations." The generation of indicator variables based on the chi-square analysis and Cramer's V proved to be the best approach for automatic classification in about half of the categories. In combination with the two other approaches, 100% precision and 100% recall were realized in 18 (47%) out of the 38 categories in the test sample. For 35 (92%) categories, precision and recall were better than 80%. For some categories, the input variables (ie, "words") also included variables from other categories, most often with a negative sign. For example, absence of words predictive for "menstruation" was a strong indicator for the category "pregnancy test." Our approach suggests a way of automatically classifying and analyzing unstructured information in Internet expert forums. The technique can perform a preliminary categorization of new requests and help Internet medical experts to better handle the mass of information and to give professional feedback.

Phylogeny-guided (meta)genome mining approach for the targeted discovery of new microbial natural products.

PubMed

Kang, Hahk-Soo

2017-02-01

Genomics-based methods are now commonplace in natural products research. A phylogeny-guided mining approach provides a means to quickly screen a large number of microbial genomes or metagenomes in search of new biosynthetic gene clusters of interest. In this approach, biosynthetic genes serve as molecular markers, and phylogenetic trees built with known and unknown marker gene sequences are used to quickly prioritize biosynthetic gene clusters for their metabolites characterization. An increase in the use of this approach has been observed for the last couple of years along with the emergence of low cost sequencing technologies. The aim of this review is to discuss the basic concept of a phylogeny-guided mining approach, and also to provide examples in which this approach was successfully applied to discover new natural products from microbial genomes and metagenomes. I believe that the phylogeny-guided mining approach will continue to play an important role in genomics-based natural products research.

Text Mining in Biomedical Domain with Emphasis on Document Clustering

PubMed Central

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

A simulation-based approach for estimating premining water quality: Red Mountain Creek, Colorado

USGS Publications Warehouse

Runkel, Robert L.; Kimball, Briant A; Walton-Day, Katherine; Verplanck, Philip L.

2007-01-01

Regulatory agencies are often charged with the task of setting site-specific numeric water quality standards for impaired streams. This task is particularly difficult for streams draining highly mineralized watersheds with past mining activity. Baseline water quality data obtained prior to mining are often non-existent and application of generic water quality standards developed for unmineralized watersheds is suspect given the geology of most watersheds affected by mining. Various approaches have been used to estimate premining conditions, but none of the existing approaches rigorously consider the physical and geochemical processes that ultimately determine instream water quality. An approach based on simulation modeling is therefore proposed herein. The approach utilizes synoptic data that provide spatially-detailed profiles of concentration, streamflow, and constituent load along the study reach. This field data set is used to calibrate a reactive stream transport model that considers the suite of physical and geochemical processes that affect constituent concentrations during instream transport. A key input to the model is the quality and quantity of waters entering the study reach. This input is based on chemical analyses available from synoptic sampling and observed increases in streamflow along the study reach. Given the calibrated model, additional simulations are conducted to estimate premining conditions. In these simulations, the chemistry of mining-affected sources is replaced with the chemistry of waters that are thought to be unaffected by mining (proximal, premining analogues). The resultant simulations provide estimates of premining water quality that reflect both the reduced loads that were present prior to mining and the processes that affect these loads as they are transported downstream. This simulation-based approach is demonstrated using data from Red Mountain Creek, Colorado, a small stream draining a heavily-mined watershed. Model application to the premining problem for Red Mountain Creek is based on limited field reconnaissance and chemical analyses; additional field work and analyses may be needed to develop definitive, quantitative estimates of premining water quality.

Intelligent bar chart plagiarism detection in documents.

PubMed

Al-Dabbagh, Mohammed Mumtaz; Salim, Naomie; Rehman, Amjad; Alkawaz, Mohammed Hazim; Saba, Tanzila; Al-Rodhaan, Mznah; Al-Dhelaan, Abdullah

2014-01-01

This paper presents a novel features mining approach from documents that could not be mined via optical character recognition (OCR). By identifying the intimate relationship between the text and graphical components, the proposed technique pulls out the Start, End, and Exact values for each bar. Furthermore, the word 2-gram and Euclidean distance methods are used to accurately detect and determine plagiarism in bar charts.

Intelligent Bar Chart Plagiarism Detection in Documents

PubMed Central

Al-Dabbagh, Mohammed Mumtaz; Salim, Naomie; Alkawaz, Mohammed Hazim; Saba, Tanzila; Al-Rodhaan, Mznah; Al-Dhelaan, Abdullah

2014-01-01

This paper presents a novel features mining approach from documents that could not be mined via optical character recognition (OCR). By identifying the intimate relationship between the text and graphical components, the proposed technique pulls out the Start, End, and Exact values for each bar. Furthermore, the word 2-gram and Euclidean distance methods are used to accurately detect and determine plagiarism in bar charts. PMID:25309952

ChemBrowser: a flexible framework for mining chemical documents.

PubMed

Wu, Xian; Zhang, Li; Chen, Ying; Rhodes, James; Griffin, Thomas D; Boyer, Stephen K; Alba, Alfredo; Cai, Keke

2010-01-01

The ability to extract chemical and biological entities and relations from text documents automatically has great value to biochemical research and development activities. The growing maturity of text mining and artificial intelligence technologies shows promise in enabling such automatic chemical entity extraction capabilities (called "Chemical Annotation" in this paper). Many techniques have been reported in the literature, ranging from dictionary and rule-based techniques to machine learning approaches. In practice, we found that no single technique works well in all cases. A combinatorial approach that allows one to quickly compose different annotation techniques together for a given situation is most effective. In this paper, we describe the key challenges we face in real-world chemical annotation scenarios. We then present a solution called ChemBrowser which has a flexible framework for chemical annotation. ChemBrowser includes a suite of customizable processing units that might be utilized in a chemical annotator, a high-level language that describes the composition of various processing units that would form a chemical annotator, and an execution engine that translates the composition language to an actual annotator that can generate annotation results for a given set of documents. We demonstrate the impact of this approach by tailoring an annotator for extracting chemical names from patent documents and show how this annotator can be easily modified with simple configuration alone.

PPInterFinder--a mining tool for extracting causal relations on human proteins from literature.

PubMed

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/

PPInterFinder—a mining tool for extracting causal relations on human proteins from literature

PubMed Central

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

Pressing needs of biomedical text mining in biocuration and beyond: opportunities and challenges

PubMed Central

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

Pressing needs of biomedical text mining in biocuration and beyond: opportunities and challenges

DOE PAGES

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

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

A New Framework for Textual Information Mining over Parse Trees. CRESST Report 805

ERIC Educational Resources Information Center

Mousavi, Hamid; Kerr, Deirdre; Iseli, Markus R.

2011-01-01

Textual information mining is a challenging problem that has resulted in the creation of many different rule-based linguistic query languages. However, these languages generally are not optimized for the purpose of text mining. In other words, they usually consider queries as individuals and only return raw results for each query. Moreover they…

Text mining a self-report back-translation.

PubMed

Blanch, Angel; Aluja, Anton

2016-06-01

There are several recommendations about the routine to undertake when back translating self-report instruments in cross-cultural research. However, text mining methods have been generally ignored within this field. This work describes a text mining innovative application useful to adapt a personality questionnaire to 12 different languages. The method is divided in 3 different stages, a descriptive analysis of the available back-translated instrument versions, a dissimilarity assessment between the source language instrument and the 12 back-translations, and an item assessment of item meaning equivalence. The suggested method contributes to improve the back-translation process of self-report instruments for cross-cultural research in 2 significant intertwined ways. First, it defines a systematic approach to the back translation issue, allowing for a more orderly and informed evaluation concerning the equivalence of different versions of the same instrument in different languages. Second, it provides more accurate instrument back-translations, which has direct implications for the reliability and validity of the instrument's test scores when used in different cultures/languages. In addition, this procedure can be extended to the back-translation of self-reports measuring psychological constructs in clinical assessment. Future research works could refine the suggested methodology and use additional available text mining tools. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

LimTox: a web tool for applied text mining of adverse event and toxicity associations of compounds, drugs and genes.

PubMed

Cañada, Andres; Capella-Gutierrez, Salvador; Rabal, Obdulia; Oyarzabal, Julen; Valencia, Alfonso; Krallinger, Martin

2017-07-03

A considerable effort has been devoted to retrieve systematically information for genes and proteins as well as relationships between them. Despite the importance of chemical compounds and drugs as a central bio-entity in pharmacological and biological research, only a limited number of freely available chemical text-mining/search engine technologies are currently accessible. Here we present LimTox (Literature Mining for Toxicology), a web-based online biomedical search tool with special focus on adverse hepatobiliary reactions. It integrates a range of text mining, named entity recognition and information extraction components. LimTox relies on machine-learning, rule-based, pattern-based and term lookup strategies. This system processes scientific abstracts, a set of full text articles and medical agency assessment reports. Although the main focus of LimTox is on adverse liver events, it enables also basic searches for other organ level toxicity associations (nephrotoxicity, cardiotoxicity, thyrotoxicity and phospholipidosis). This tool supports specialized search queries for: chemical compounds/drugs, genes (with additional emphasis on key enzymes in drug metabolism, namely P450 cytochromes-CYPs) and biochemical liver markers. The LimTox website is free and open to all users and there is no login requirement. LimTox can be accessed at: http://limtox.bioinfo.cnio.es. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Using natural language processing techniques to inform research on nanotechnology.

PubMed

Lewinski, Nastassja A; McInnes, Bridget T

2015-01-01

Literature in the field of nanotechnology is exponentially increasing with more and more engineered nanomaterials being created, characterized, and tested for performance and safety. With the deluge of published data, there is a need for natural language processing approaches to semi-automate the cataloguing of engineered nanomaterials and their associated physico-chemical properties, performance, exposure scenarios, and biological effects. In this paper, we review the different informatics methods that have been applied to patent mining, nanomaterial/device characterization, nanomedicine, and environmental risk assessment. Nine natural language processing (NLP)-based tools were identified: NanoPort, NanoMapper, TechPerceptor, a Text Mining Framework, a Nanodevice Analyzer, a Clinical Trial Document Classifier, Nanotoxicity Searcher, NanoSifter, and NEIMiner. We conclude with recommendations for sharing NLP-related tools through online repositories to broaden participation in nanoinformatics.

BioC: a minimalist approach to interoperability for biomedical text processing

PubMed Central

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

A new pivoting and iterative text detection algorithm for biomedical images.

PubMed

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.

ASCOT: a text mining-based web-service for efficient search and assisted creation of clinical trials

PubMed Central

2012-01-01

Clinical trials are mandatory protocols describing medical research on humans and among the most valuable sources of medical practice evidence. Searching for trials relevant to some query is laborious due to the immense number of existing protocols. Apart from search, writing new trials includes composing detailed eligibility criteria, which might be time-consuming, especially for new researchers. In this paper we present ASCOT, an efficient search application customised for clinical trials. ASCOT uses text mining and data mining methods to enrich clinical trials with metadata, that in turn serve as effective tools to narrow down search. In addition, ASCOT integrates a component for recommending eligibility criteria based on a set of selected protocols. PMID:22595088

ASCOT: a text mining-based web-service for efficient search and assisted creation of clinical trials.

PubMed

Korkontzelos, Ioannis; Mu, Tingting; Ananiadou, Sophia

2012-04-30

Clinical trials are mandatory protocols describing medical research on humans and among the most valuable sources of medical practice evidence. Searching for trials relevant to some query is laborious due to the immense number of existing protocols. Apart from search, writing new trials includes composing detailed eligibility criteria, which might be time-consuming, especially for new researchers. In this paper we present ASCOT, an efficient search application customised for clinical trials. ASCOT uses text mining and data mining methods to enrich clinical trials with metadata, that in turn serve as effective tools to narrow down search. In addition, ASCOT integrates a component for recommending eligibility criteria based on a set of selected protocols.

Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research.

PubMed

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.

Text Mining for Protein Docking

PubMed Central

Badal, Varsha D.; Kundrotas, Petras J.; Vakser, Ilya A.

2015-01-01

The rapidly growing amount of publicly available information from biomedical research is readily accessible on the Internet, providing a powerful resource for predictive biomolecular modeling. The accumulated data on experimentally determined structures transformed structure prediction of proteins and protein complexes. Instead of exploring the enormous search space, predictive tools can simply proceed to the solution based on similarity to the existing, previously determined structures. A similar major paradigm shift is emerging due to the rapidly expanding amount of information, other than experimentally determined structures, which still can be used as constraints in biomolecular structure prediction. Automated text mining has been widely used in recreating protein interaction networks, as well as in detecting small ligand binding sites on protein structures. Combining and expanding these two well-developed areas of research, we applied the text mining to structural modeling of protein-protein complexes (protein docking). Protein docking can be significantly improved when constraints on the docking mode are available. We developed a procedure that retrieves published abstracts on a specific protein-protein interaction and extracts information relevant to docking. The procedure was assessed on protein complexes from Dockground (http://dockground.compbio.ku.edu). The results show that correct information on binding residues can be extracted for about half of the complexes. The amount of irrelevant information was reduced by conceptual analysis of a subset of the retrieved abstracts, based on the bag-of-words (features) approach. Support Vector Machine models were trained and validated on the subset. The remaining abstracts were filtered by the best-performing models, which decreased the irrelevant information for ~ 25% complexes in the dataset. The extracted constraints were incorporated in the docking protocol and tested on the Dockground unbound benchmark set, significantly increasing the docking success rate. PMID:26650466

Biomedical text mining for research rigor and integrity: tasks, challenges, directions.

PubMed

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.

Information Retrieval and Text Mining Technologies for Chemistry.

PubMed

Krallinger, Martin; Rabal, Obdulia; Lourenço, Anália; Oyarzabal, Julen; Valencia, Alfonso

2017-06-28

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.

Pressing needs of biomedical text mining in biocuration and beyond: opportunities and challenges.

PubMed

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.

Systematic analysis of molecular mechanisms for HCC metastasis via text mining approach.

PubMed

Zhen, Cheng; Zhu, Caizhong; Chen, Haoyang; Xiong, Yiru; Tan, Junyuan; Chen, Dong; Li, Jin

2017-02-21

To systematically explore the molecular mechanism for hepatocellular carcinoma (HCC) metastasis and identify regulatory genes with text mining methods. Genes with highest frequencies and significant pathways related to HCC metastasis were listed. A handful of proteins such as EGFR, MDM2, TP53 and APP, were identified as hub nodes in PPI (protein-protein interaction) network. Compared with unique genes for HBV-HCCs, genes particular to HCV-HCCs were less, but may participate in more extensive signaling processes. VEGFA, PI3KCA, MAPK1, MMP9 and other genes may play important roles in multiple phenotypes of metastasis. Genes in abstracts of HCC-metastasis literatures were identified. Word frequency analysis, KEGG pathway and PPI network analysis were performed. Then co-occurrence analysis between genes and metastasis-related phenotypes were carried out. Text mining is effective for revealing potential regulators or pathways, but the purpose of it should be specific, and the combination of various methods will be more useful.

Service-based analysis of biological pathways

PubMed Central

Zheng, George; Bouguettaya, Athman

2009-01-01

Background Computer-based pathway discovery is concerned with two important objectives: pathway identification and analysis. Conventional mining and modeling approaches aimed at pathway discovery are often effective at achieving either objective, but not both. Such limitations can be effectively tackled leveraging a Web service-based modeling and mining approach. Results Inspired by molecular recognitions and drug discovery processes, we developed a Web service mining tool, named PathExplorer, to discover potentially interesting biological pathways linking service models of biological processes. The tool uses an innovative approach to identify useful pathways based on graph-based hints and service-based simulation verifying user's hypotheses. Conclusion Web service modeling of biological processes allows the easy access and invocation of these processes on the Web. Web service mining techniques described in this paper enable the discovery of biological pathways linking these process service models. Algorithms presented in this paper for automatically highlighting interesting subgraph within an identified pathway network enable the user to formulate hypothesis, which can be tested out using our simulation algorithm that are also described in this paper. PMID:19796403

Text Mining for Adverse Drug Events: the Promise, Challenges, and State of the Art

PubMed Central

Harpaz, Rave; Callahan, Alison; Tamang, Suzanne; Low, Yen; Odgers, David; Finlayson, Sam; Jung, Kenneth; LePendu, Paea; Shah, Nigam H.

2014-01-01

Text mining is the computational process of extracting meaningful information from large amounts of unstructured text. Text mining is emerging as a tool to leverage underutilized data sources that can improve pharmacovigilance, including the objective of adverse drug event detection and assessment. This article provides an overview of recent advances in pharmacovigilance driven by the application of text mining, and discusses several data sources—such as biomedical literature, clinical narratives, product labeling, social media, and Web search logs—that are amenable to text-mining for pharmacovigilance. Given the state of the art, it appears text mining can be applied to extract useful ADE-related information from multiple textual sources. Nonetheless, further research is required to address remaining technical challenges associated with the text mining methodologies, and to conclusively determine the relative contribution of each textual source to improving pharmacovigilance. PMID:25151493

Text Mining.

ERIC Educational Resources Information Center

Trybula, Walter J.

1999-01-01

Reviews the state of research in text mining, focusing on newer developments. The intent is to describe the disparate investigations currently included under the term text mining and provide a cohesive structure for these efforts. A summary of research identifies key organizations responsible for pushing the development of text mining. A section…

Co-evolutionary data mining for fuzzy rules: automatic fitness function creation phase space, and experiments

NASA Astrophysics Data System (ADS)

Smith, James F., III; Blank, Joseph A.

2003-03-01

An approach is being explored that involves embedding a fuzzy logic based resource manager in an electronic game environment. Game agents can function under their own autonomous logic or human control. This approach automates the data mining problem. The game automatically creates a cleansed database reflecting the domain expert's knowledge, it calls a data mining function, a genetic algorithm, for data mining of the data base as required and allows easy evaluation of the information extracted. The co-evolutionary fitness functions, chromosomes and stopping criteria for ending the game are discussed. Genetic algorithm and genetic program based data mining procedures are discussed that automatically discover new fuzzy rules and strategies. The strategy tree concept and its relationship to co-evolutionary data mining are examined as well as the associated phase space representation of fuzzy concepts. The overlap of fuzzy concepts in phase space reduces the effective strategies available to adversaries. Co-evolutionary data mining alters the geometric properties of the overlap region known as the admissible region of phase space significantly enhancing the performance of the resource manager. Procedures for validation of the information data mined are discussed and significant experimental results provided.

A methodological toolkit for field assessments of artisanally mined alluvial diamond deposits

USGS Publications Warehouse

Chirico, Peter G.; Malpeli, Katherine C.

2014-01-01

This toolkit provides a standardized checklist of critical issues relevant to artisanal mining-related field research. An integrated sociophysical geographic approach to collecting data at artisanal mine sites is outlined. The implementation and results of a multistakeholder approach to data collection, carried out in the assessment of Guinea’s artisanally mined diamond deposits, also are summarized. This toolkit, based on recent and successful field campaigns in West Africa, has been developed as a reference document to assist other government agencies or organizations in collecting the data necessary for artisanal diamond mining or similar natural resource assessments.

A review on computational systems biology of pathogen–host interactions

PubMed Central

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

Mining Social Media and Web Searches For Disease Detection

PubMed Central

Yang, Y. Tony; Horneffer, Michael; DiLisio, Nicole

2013-01-01

Web-based social media is increasingly being used across different settings in the health care industry. The increased frequency in the use of the Internet via computer or mobile devices provides an opportunity for social media to be the medium through which people can be provided with valuable health information quickly and directly. While traditional methods of detection relied predominately on hierarchical or bureaucratic lines of communication, these often failed to yield timely and accurate epidemiological intelligence. New web-based platforms promise increased opportunities for a more timely and accurate spreading of information and analysis. This article aims to provide an overview and discussion of the availability of timely and accurate information. It is especially useful for the rapid identification of an outbreak of an infectious disease that is necessary to promptly and effectively develop public health responses. These web-based platforms include search queries, data mining of web and social media, process and analysis of blogs containing epidemic key words, text mining, and geographical information system data analyses. These new sources of analysis and information are intended to complement traditional sources of epidemic intelligence. Despite the attractiveness of these new approaches, further study is needed to determine the accuracy of blogger statements, as increases in public participation may not necessarily mean the information provided is more accurate. PMID:25170475

Mining social media and web searches for disease detection.

PubMed

Yang, Y Tony; Horneffer, Michael; DiLisio, Nicole

2013-04-28

Web-based social media is increasingly being used across different settings in the health care industry. The increased frequency in the use of the Internet via computer or mobile devices provides an opportunity for social media to be the medium through which people can be provided with valuable health information quickly and directly. While traditional methods of detection relied predominately on hierarchical or bureaucratic lines of communication, these often failed to yield timely and accurate epidemiological intelligence. New web-based platforms promise increased opportunities for a more timely and accurate spreading of information and analysis. This article aims to provide an overview and discussion of the availability of timely and accurate information. It is especially useful for the rapid identification of an outbreak of an infectious disease that is necessary to promptly and effectively develop public health responses. These web-based platforms include search queries, data mining of web and social media, process and analysis of blogs containing epidemic key words, text mining, and geographical information system data analyses. These new sources of analysis and information are intended to complement traditional sources of epidemic intelligence. Despite the attractiveness of these new approaches, further study is needed to determine the accuracy of blogger statements, as increases in public participation may not necessarily mean the information provided is more accurate.

Knowledge-based identification of the ERK2/STAT3 signal pathway as a therapeutic target for type 2 diabetes and drug discovery.

PubMed

Kinoshita, Takayoshi; Doi, Kentaro; Sugiyama, Hajime; Kinoshita, Shuhei; Wada, Mutsuyo; Naruto, Shuji; Tomonaga, Atsushi

2011-09-01

Many existing agents for diabetes therapy are unable to restore or maintain normal glucose homeostasis or prevent the eventual emergence of hyperglycemia-related complication. Therefore, agents based on novel mechanisms are sought to complement and extend the current therapeutic approaches. Based on the initial paper research, we focused on active STAT3 as an attractive pharmacological target for type 2 diabetes. The subsequent text mining with a unique query to identify suppressors but not activators of STAT3 revealed the ERK2/STAT3 pathway as a novel diabetes target. The description of ERK2 inhibitors as diabetes target had not been found in our text mining research at present. The mechanism-based peptide inhibitor for ERK2 was identified using the knowledge of the KIM sequence, which has an important role in the recognition of cognate kinases, phosphatases, scaffold proteins, and substrates. The peptide inhibitor was confirmed to exert effects in vitro and in vivo. The peptide inhibitor conferred a significant decrease in HOMA-IR levels on Day 28 compared with that in the vehicle group. Besides lowering the fasting blood glucose level, the peptide inhibitor also attenuated the blood glucose increment in the fed state, as compared with the vehicle group. © 2011 John Wiley & Sons A/S.

Textpresso site-specific recombinases: A text-mining server for the recombinase literature including Cre mice and conditional alleles.

PubMed

Urbanski, William M; Condie, Brian G

2009-12-01

Textpresso Site Specific Recombinases (http://ssrc.genetics.uga.edu/) is a text-mining web server for searching a database of more than 9,000 full-text publications. The papers and abstracts in this database represent a wide range of topics related to site-specific recombinase (SSR) research tools. Included in the database are most of the papers that report the characterization or use of mouse strains that express Cre recombinase as well as papers that describe or analyze mouse lines that carry conditional (floxed) alleles or SSR-activated transgenes/knockins. The database also includes reports describing SSR-based cloning methods such as the Gateway or the Creator systems, papers reporting the development or use of SSR-based tools in systems such as Drosophila, bacteria, parasites, stem cells, yeast, plants, zebrafish, and Xenopus as well as publications that describe the biochemistry, genetics, or molecular structure of the SSRs themselves. Textpresso Site Specific Recombinases is the only comprehensive text-mining resource available for the literature describing the biology and technical applications of SSRs. (c) 2009 Wiley-Liss, Inc.

Biomedical text mining and its applications in cancer research.

PubMed

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.

New approach to generating insights for aging research based on literature mining and knowledge integration

PubMed Central

Kwon, Yeondae; Natori, Yukikazu

2017-01-01

The proportion of the elderly population in most countries worldwide is increasing dramatically. Therefore, social interest in the fields of health, longevity, and anti-aging has been increasing as well. However, the basic research results obtained from a reductionist approach in biology and a bioinformatic approach in genome science have limited usefulness for generating insights on future health, longevity, and anti-aging-related research on a case by case basis. We propose a new approach that uses our literature mining technique and bioinformatics, which lead to a better perspective on research trends by providing an expanded knowledge base to work from. We demonstrate that our approach provides useful information that deepens insights on future trends which differs from data obtained conventionally, and this methodology is already paving the way for a new field in aging-related research based on literature mining. One compelling example of this is how our new approach can be a useful tool in drug repositioning. PMID:28817730

Text Mining Effectively Scores and Ranks the Literature for Improving Chemical-Gene-Disease Curation at the Comparative Toxicogenomics Database

PubMed Central

Johnson, Robin J.; Lay, Jean M.; Lennon-Hopkins, Kelley; Saraceni-Richards, Cynthia; Sciaky, Daniela; Murphy, Cynthia Grondin; Mattingly, Carolyn J.

2013-01-01

The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) is a public resource that curates interactions between environmental chemicals and gene products, and their relationships to diseases, as a means of understanding the effects of environmental chemicals on human health. CTD provides a triad of core information in the form of chemical-gene, chemical-disease, and gene-disease interactions that are manually curated from scientific articles. To increase the efficiency, productivity, and data coverage of manual curation, we have leveraged text mining to help rank and prioritize the triaged literature. Here, we describe our text-mining process that computes and assigns each article a document relevancy score (DRS), wherein a high DRS suggests that an article is more likely to be relevant for curation at CTD. We evaluated our process by first text mining a corpus of 14,904 articles triaged for seven heavy metals (cadmium, cobalt, copper, lead, manganese, mercury, and nickel). Based upon initial analysis, a representative subset corpus of 3,583 articles was then selected from the 14,094 articles and sent to five CTD biocurators for review. The resulting curation of these 3,583 articles was analyzed for a variety of parameters, including article relevancy, novel data content, interaction yield rate, mean average precision, and biological and toxicological interpretability. We show that for all measured parameters, the DRS is an effective indicator for scoring and improving the ranking of literature for the curation of chemical-gene-disease information at CTD. Here, we demonstrate how fully incorporating text mining-based DRS scoring into our curation pipeline enhances manual curation by prioritizing more relevant articles, thereby increasing data content, productivity, and efficiency. PMID:23613709

Data Processing and Text Mining Technologies on Electronic Medical Records: A Review

PubMed Central

Sun, Wencheng; Li, Yangyang; Liu, Fang; Fang, Shengqun; Wang, Guoyan

2018-01-01

Currently, medical institutes generally use EMR to record patient's condition, including diagnostic information, procedures performed, and treatment results. EMR has been recognized as a valuable resource for large-scale analysis. However, EMR has the characteristics of diversity, incompleteness, redundancy, and privacy, which make it difficult to carry out data mining and analysis directly. Therefore, it is necessary to preprocess the source data in order to improve data quality and improve the data mining results. Different types of data require different processing technologies. Most structured data commonly needs classic preprocessing technologies, including data cleansing, data integration, data transformation, and data reduction. For semistructured or unstructured data, such as medical text, containing more health information, it requires more complex and challenging processing methods. The task of information extraction for medical texts mainly includes NER (named-entity recognition) and RE (relation extraction). This paper focuses on the process of EMR processing and emphatically analyzes the key techniques. In addition, we make an in-depth study on the applications developed based on text mining together with the open challenges and research issues for future work. PMID:29849998

Stopping Antidepressants and Anxiolytics as Major Concerns Reported in Online Health Communities: A Text Mining Approach.

PubMed

Abbe, Adeline; Falissard, Bruno

2017-10-23

Internet is a particularly dynamic way to quickly capture the perceptions of a population in real time. Complementary to traditional face-to-face communication, online social networks help patients to improve self-esteem and self-help. The aim of this study was to use text mining on material from an online forum exploring patients' concerns about treatment (antidepressants and anxiolytics). Concerns about treatment were collected from discussion titles in patients' online community related to antidepressants and anxiolytics. To examine the content of these titles automatically, we used text mining methods, such as word frequency in a document-term matrix and co-occurrence of words using a network analysis. It was thus possible to identify topics discussed on the forum. The forum included 2415 discussions on antidepressants and anxiolytics over a period of 3 years. After a preprocessing step, the text mining algorithm identified the 99 most frequently occurring words in titles, among which were escitalopram, withdrawal, antidepressant, venlafaxine, paroxetine, and effect. Patients' concerns were related to antidepressant withdrawal, the need to share experience about symptoms, effects, and questions on weight gain with some drugs. Patients' expression on the Internet is a potential additional resource in addressing patients' concerns about treatment. Patient profiles are close to that of patients treated in psychiatry. ©Adeline Abbe, Bruno Falissard. Originally published in JMIR Mental Health (http://mental.jmir.org), 23.10.2017.

Using natural language processing techniques to inform research on nanotechnology

PubMed Central

Lewinski, Nastassja A

2015-01-01

Summary Literature in the field of nanotechnology is exponentially increasing with more and more engineered nanomaterials being created, characterized, and tested for performance and safety. With the deluge of published data, there is a need for natural language processing approaches to semi-automate the cataloguing of engineered nanomaterials and their associated physico-chemical properties, performance, exposure scenarios, and biological effects. In this paper, we review the different informatics methods that have been applied to patent mining, nanomaterial/device characterization, nanomedicine, and environmental risk assessment. Nine natural language processing (NLP)-based tools were identified: NanoPort, NanoMapper, TechPerceptor, a Text Mining Framework, a Nanodevice Analyzer, a Clinical Trial Document Classifier, Nanotoxicity Searcher, NanoSifter, and NEIMiner. We conclude with recommendations for sharing NLP-related tools through online repositories to broaden participation in nanoinformatics. PMID:26199848

Text-mining analysis of mHealth research.

PubMed

Ozaydin, Bunyamin; Zengul, Ferhat; Oner, Nurettin; Delen, Dursun

2017-01-01

In recent years, because of the advancements in communication and networking technologies, mobile technologies have been developing at an unprecedented rate. mHealth, the use of mobile technologies in medicine, and the related research has also surged parallel to these technological advancements. Although there have been several attempts to review mHealth research through manual processes such as systematic reviews, the sheer magnitude of the number of studies published in recent years makes this task very challenging. The most recent developments in machine learning and text mining offer some potential solutions to address this challenge by allowing analyses of large volumes of texts through semi-automated processes. The objective of this study is to analyze the evolution of mHealth research by utilizing text-mining and natural language processing (NLP) analyses. The study sample included abstracts of 5,644 mHealth research articles, which were gathered from five academic search engines by using search terms such as mobile health, and mHealth. The analysis used the Text Explorer module of JMP Pro 13 and an iterative semi-automated process involving tokenizing, phrasing, and terming. After developing the document term matrix (DTM) analyses such as single value decomposition (SVD), topic, and hierarchical document clustering were performed, along with the topic-informed document clustering approach. The results were presented in the form of word-clouds and trend analyses. There were several major findings regarding research clusters and trends. First, our results confirmed time-dependent nature of terminology use in mHealth research. For example, in earlier versus recent years the use of terminology changed from "mobile phone" to "smartphone" and from "applications" to "apps". Second, ten clusters for mHealth research were identified including (I) Clinical Research on Lifestyle Management, (II) Community Health, (III) Literature Review, (IV) Medical Interventions, (V) Research Design, (VI) Infrastructure, (VII) Applications, (VIII) Research and Innovation in Health Technologies, (IX) Sensor-based Devices and Measurement Algorithms, (X) Survey-based Research. Third, the trend analyses indicated the infrastructure cluster as the highest percentage researched area until 2014. The Research and Innovation in Health Technologies cluster experienced the largest increase in numbers of publications in recent years, especially after 2014. This study is unique because it is the only known study utilizing text-mining analyses to reveal the streams and trends for mHealth research. The fast growth in mobile technologies is expected to lead to higher numbers of studies focusing on mHealth and its implications for various healthcare outcomes. Findings of this study can be utilized by researchers in identifying areas for future studies.

Text-mining analysis of mHealth research

PubMed Central

Zengul, Ferhat; Oner, Nurettin; Delen, Dursun

2017-01-01

In recent years, because of the advancements in communication and networking technologies, mobile technologies have been developing at an unprecedented rate. mHealth, the use of mobile technologies in medicine, and the related research has also surged parallel to these technological advancements. Although there have been several attempts to review mHealth research through manual processes such as systematic reviews, the sheer magnitude of the number of studies published in recent years makes this task very challenging. The most recent developments in machine learning and text mining offer some potential solutions to address this challenge by allowing analyses of large volumes of texts through semi-automated processes. The objective of this study is to analyze the evolution of mHealth research by utilizing text-mining and natural language processing (NLP) analyses. The study sample included abstracts of 5,644 mHealth research articles, which were gathered from five academic search engines by using search terms such as mobile health, and mHealth. The analysis used the Text Explorer module of JMP Pro 13 and an iterative semi-automated process involving tokenizing, phrasing, and terming. After developing the document term matrix (DTM) analyses such as single value decomposition (SVD), topic, and hierarchical document clustering were performed, along with the topic-informed document clustering approach. The results were presented in the form of word-clouds and trend analyses. There were several major findings regarding research clusters and trends. First, our results confirmed time-dependent nature of terminology use in mHealth research. For example, in earlier versus recent years the use of terminology changed from “mobile phone” to “smartphone” and from “applications” to “apps”. Second, ten clusters for mHealth research were identified including (I) Clinical Research on Lifestyle Management, (II) Community Health, (III) Literature Review, (IV) Medical Interventions, (V) Research Design, (VI) Infrastructure, (VII) Applications, (VIII) Research and Innovation in Health Technologies, (IX) Sensor-based Devices and Measurement Algorithms, (X) Survey-based Research. Third, the trend analyses indicated the infrastructure cluster as the highest percentage researched area until 2014. The Research and Innovation in Health Technologies cluster experienced the largest increase in numbers of publications in recent years, especially after 2014. This study is unique because it is the only known study utilizing text-mining analyses to reveal the streams and trends for mHealth research. The fast growth in mobile technologies is expected to lead to higher numbers of studies focusing on mHealth and its implications for various healthcare outcomes. Findings of this study can be utilized by researchers in identifying areas for future studies. PMID:29430456

Extracting biomedical events from pairs of text entities

PubMed Central

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

Analysis of the human diseasome using phenotype similarity between common, genetic, and infectious diseases

NASA Astrophysics Data System (ADS)

Hoehndorf, Robert; Schofield, Paul N.; Gkoutos, Georgios V.

2015-06-01

Phenotypes are the observable characteristics of an organism arising from its response to the environment. Phenotypes associated with engineered and natural genetic variation are widely recorded using phenotype ontologies in model organisms, as are signs and symptoms of human Mendelian diseases in databases such as OMIM and Orphanet. Exploiting these resources, several computational methods have been developed for integration and analysis of phenotype data to identify the genetic etiology of diseases or suggest plausible interventions. A similar resource would be highly useful not only for rare and Mendelian diseases, but also for common, complex and infectious diseases. We apply a semantic text-mining approach to identify the phenotypes (signs and symptoms) associated with over 6,000 diseases. We evaluate our text-mined phenotypes by demonstrating that they can correctly identify known disease-associated genes in mice and humans with high accuracy. Using a phenotypic similarity measure, we generate a human disease network in which diseases that have similar signs and symptoms cluster together, and we use this network to identify closely related diseases based on common etiological, anatomical as well as physiological underpinnings.

Incorporating linguistic knowledge for learning distributed word representations.

PubMed

Wang, Yan; Liu, Zhiyuan; Sun, Maosong

2015-01-01

Combined with neural language models, distributed word representations achieve significant advantages in computational linguistics and text mining. Most existing models estimate distributed word vectors from large-scale data in an unsupervised fashion, which, however, do not take rich linguistic knowledge into consideration. Linguistic knowledge can be represented as either link-based knowledge or preference-based knowledge, and we propose knowledge regularized word representation models (KRWR) to incorporate these prior knowledge for learning distributed word representations. Experiment results demonstrate that our estimated word representation achieves better performance in task of semantic relatedness ranking. This indicates that our methods can efficiently encode both prior knowledge from knowledge bases and statistical knowledge from large-scale text corpora into a unified word representation model, which will benefit many tasks in text mining.

Incorporating Linguistic Knowledge for Learning Distributed Word Representations

PubMed Central

Wang, Yan; Liu, Zhiyuan; Sun, Maosong

2015-01-01

Combined with neural language models, distributed word representations achieve significant advantages in computational linguistics and text mining. Most existing models estimate distributed word vectors from large-scale data in an unsupervised fashion, which, however, do not take rich linguistic knowledge into consideration. Linguistic knowledge can be represented as either link-based knowledge or preference-based knowledge, and we propose knowledge regularized word representation models (KRWR) to incorporate these prior knowledge for learning distributed word representations. Experiment results demonstrate that our estimated word representation achieves better performance in task of semantic relatedness ranking. This indicates that our methods can efficiently encode both prior knowledge from knowledge bases and statistical knowledge from large-scale text corpora into a unified word representation model, which will benefit many tasks in text mining. PMID:25874581

Text mining meets workflow: linking U-Compare with Taverna

PubMed Central

Kano, Yoshinobu; Dobson, Paul; Nakanishi, Mio; Tsujii, Jun'ichi; Ananiadou, Sophia

2010-01-01

Summary: Text mining from the biomedical literature is of increasing importance, yet it is not easy for the bioinformatics community to create and run text mining workflows due to the lack of accessibility and interoperability of the text mining resources. The U-Compare system provides a wide range of bio text mining resources in a highly interoperable workflow environment where workflows can very easily be created, executed, evaluated and visualized without coding. We have linked U-Compare to Taverna, a generic workflow system, to expose text mining functionality to the bioinformatics community. Availability: http://u-compare.org/taverna.html, http://u-compare.org Contact: kano@is.s.u-tokyo.ac.jp Supplementary information: Supplementary data are available at Bioinformatics online. PMID:20709690

Information Gain Based Dimensionality Selection for Classifying Text Documents

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dumidu Wijayasekara; Milos Manic; Miles McQueen

2013-06-01

Selecting the optimal dimensions for various knowledge extraction applications is an essential component of data mining. Dimensionality selection techniques are utilized in classification applications to increase the classification accuracy and reduce the computational complexity. In text classification, where the dimensionality of the dataset is extremely high, dimensionality selection is even more important. This paper presents a novel, genetic algorithm based methodology, for dimensionality selection in text mining applications that utilizes information gain. The presented methodology uses information gain of each dimension to change the mutation probability of chromosomes dynamically. Since the information gain is calculated a priori, the computational complexitymore » is not affected. The presented method was tested on a specific text classification problem and compared with conventional genetic algorithm based dimensionality selection. The results show an improvement of 3% in the true positives and 1.6% in the true negatives over conventional dimensionality selection methods.« less

Text Mining and Natural Language Processing Approaches for Automatic Categorization of Lay Requests to Web-Based Expert Forums

PubMed Central

Reincke, Ulrich; Michelmann, Hans Wilhelm

2009-01-01

Background Both healthy and sick people increasingly use electronic media to obtain medical information and advice. For example, Internet users may send requests to Web-based expert forums, or so-called “ask the doctor” services. Objective To automatically classify lay requests to an Internet medical expert forum using a combination of different text-mining strategies. Methods We first manually classified a sample of 988 requests directed to a involuntary childlessness forum on the German website “Rund ums Baby” (“Everything about Babies”) into one or more of 38 categories belonging to two dimensions (“subject matter” and “expectations”). After creating start and synonym lists, we calculated the average Cramer’s V statistic for the association of each word with each category. We also used principle component analysis and singular value decomposition as further text-mining strategies. With these measures we trained regression models and determined, on the basis of best regression models, for any request the probability of belonging to each of the 38 different categories, with a cutoff of 50%. Recall and precision of a test sample were calculated as a measure of quality for the automatic classification. Results According to the manual classification of 988 documents, 102 (10%) documents fell into the category “in vitro fertilization (IVF),” 81 (8%) into the category “ovulation,” 79 (8%) into “cycle,” and 57 (6%) into “semen analysis.” These were the four most frequent categories in the subject matter dimension (consisting of 32 categories). The expectation dimension comprised six categories; we classified 533 documents (54%) as “general information” and 351 (36%) as a wish for “treatment recommendations.” The generation of indicator variables based on the chi-square analysis and Cramer’s V proved to be the best approach for automatic classification in about half of the categories. In combination with the two other approaches, 100% precision and 100% recall were realized in 18 (47%) out of the 38 categories in the test sample. For 35 (92%) categories, precision and recall were better than 80%. For some categories, the input variables (ie, “words”) also included variables from other categories, most often with a negative sign. For example, absence of words predictive for “menstruation” was a strong indicator for the category “pregnancy test.” Conclusions Our approach suggests a way of automatically classifying and analyzing unstructured information in Internet expert forums. The technique can perform a preliminary categorization of new requests and help Internet medical experts to better handle the mass of information and to give professional feedback. PMID:19632978

Flooded Underground Coal Mines: A Significant Source of Inexpensive Geothermal Energy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Watzlaf, G.R.; Ackman, T.E.

2007-04-01

Many mining regions in the United States contain extensive areas of flooded underground mines. The water within these mines represents a significant and widespread opportunity for extracting low-grade, geothermal energy. Based on current energy prices, geothermal heat pump systems using mine water could reduce the annual costs for heating to over 70 percent compared to conventional heating methods (natural gas or heating oil). These same systems could reduce annual cooling costs by up to 50 percent over standard air conditioning in many areas of the country. (Formatted full-text version is released by permission of publisher)

Survey of Natural Language Processing Techniques in Bioinformatics.

PubMed

Zeng, Zhiqiang; Shi, Hua; Wu, Yun; Hong, Zhiling

2015-01-01

Informatics methods, such as text mining and natural language processing, are always involved in bioinformatics research. In this study, we discuss text mining and natural language processing methods in bioinformatics from two perspectives. First, we aim to search for knowledge on biology, retrieve references using text mining methods, and reconstruct databases. For example, protein-protein interactions and gene-disease relationship can be mined from PubMed. Then, we analyze the applications of text mining and natural language processing techniques in bioinformatics, including predicting protein structure and function, detecting noncoding RNA. Finally, numerous methods and applications, as well as their contributions to bioinformatics, are discussed for future use by text mining and natural language processing researchers.

[The method and application to construct experience recommendation platform of acupuncture ancient books based on data mining technology].

PubMed

Chen, Chuyun; Hong, Jiaming; Zhou, Weilin; Lin, Guohua; Wang, Zhengfei; Zhang, Qufei; Lu, Cuina; Lu, Lihong

2017-07-12

To construct a knowledge platform of acupuncture ancient books based on data mining technology, and to provide retrieval service for users. The Oracle 10 g database was applied and JAVA was selected as development language; based on the standard library and ancient books database established by manual entry, a variety of data mining technologies, including word segmentation, speech tagging, dependency analysis, rule extraction, similarity calculation, ambiguity analysis, supervised classification technology were applied to achieve text automatic extraction of ancient books; in the last, through association mining and decision analysis, the comprehensive and intelligent analysis of disease and symptom, meridians, acupoints, rules of acupuncture and moxibustion in acupuncture ancient books were realized, and retrieval service was provided for users through structure of browser/server (B/S). The platform realized full-text retrieval, word frequency analysis and association analysis; when diseases or acupoints were searched, the frequencies of meridian, acupoints (diseases) and techniques were presented from high to low, meanwhile the support degree and confidence coefficient between disease and acupoints (special acupoint), acupoints and acupoints in prescription, disease or acupoints and technique were presented. The experience platform of acupuncture ancient books based on data mining technology could be used as a reference for selection of disease, meridian and acupoint in clinical treatment and education of acupuncture and moxibustion.

Untangling Topic Threads in Chat-Based Communication: A Case Study

DTIC Science & Technology

2011-08-01

learning techniques such as clustering are very popular for analyzing text for topic identification (Anjewierden,, Kollöffel and Hulshof 2007; Adams...Anjewierden, A., Kollöffel, B., and Hulshof , C. (2007). Towards educational data mining: Using data mining methods for automated chat analysis to

StemTextSearch: Stem cell gene database with evidence from abstracts.

PubMed

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.

From IHE Audit Trails to XES Event Logs Facilitating Process Mining.

PubMed

Paster, Ferdinand; Helm, Emmanuel

2015-01-01

Recently Business Intelligence approaches like process mining are applied to the healthcare domain. The goal of process mining is to gain process knowledge, compliance and room for improvement by investigating recorded event data. Previous approaches focused on process discovery by event data from various specific systems. IHE, as a globally recognized basis for healthcare information systems, defines in its ATNA profile how real-world events must be recorded in centralized event logs. The following approach presents how audit trails collected by the means of ATNA can be transformed to enable process mining. Using the standardized audit trails provides the ability to apply these methods to all IHE based information systems.

A framework for semisupervised feature generation and its applications in biomedical literature mining.

PubMed

Li, Yanpeng; Hu, Xiaohua; Lin, Hongfei; Yang, Zhihao

2011-01-01

Feature representation is essential to machine learning and text mining. In this paper, we present a feature coupling generalization (FCG) framework for generating new features from unlabeled data. It selects two special types of features, i.e., example-distinguishing features (EDFs) and class-distinguishing features (CDFs) from original feature set, and then generalizes EDFs into higher-level features based on their coupling degrees with CDFs in unlabeled data. The advantage is: EDFs with extreme sparsity in labeled data can be enriched by their co-occurrences with CDFs in unlabeled data so that the performance of these low-frequency features can be greatly boosted and new information from unlabeled can be incorporated. We apply this approach to three tasks in biomedical literature mining: gene named entity recognition (NER), protein-protein interaction extraction (PPIE), and text classification (TC) for gene ontology (GO) annotation. New features are generated from over 20 GB unlabeled PubMed abstracts. The experimental results on BioCreative 2, AIMED corpus, and TREC 2005 Genomics Track show that 1) FCG can utilize well the sparse features ignored by supervised learning. 2) It improves the performance of supervised baselines by 7.8 percent, 5.0 percent, and 5.8 percent, respectively, in the tree tasks. 3) Our methods achieve 89.1, 64.5 F-score, and 60.1 normalized utility on the three benchmark data sets.

Pathway enrichment based on text mining and its validation on carotenoid and vitamin A metabolism.

PubMed

Waagmeester, Andra; Pezik, Piotr; Coort, Susan; Tourniaire, Franck; Evelo, Chris; Rebholz-Schuhmann, Dietrich

2009-10-01

Carotenoid metabolism is relevant to the prevention of various diseases. Although the main actors in this metabolic pathway are known, our understanding of the pathway is still incomplete. The information on the carotenoids is scattered in the large and growing body of scientific literature. We designed a text-mining work flow to enrich existing pathways. It has been validated on the vitamin A pathway, which is a well-studied part of the carotenoid metabolism. In this study we used the vitamin A metabolism pathway as it has been described by an expert team on carotenoid metabolism from the European network of excellence in Nutrigenomics (NuGO). This work flow uses an initial set of publications cited in a review paper (1,191 publications), enlarges this corpus with Medline abstracts (13,579 documents), and then extracts the key terminology from all relevant publications. Domain experts validated the intermediate and final results of our text-mining work flow. With our approach we were able to enrich the pathway representing vitamin A metabolism. We found 37 new and relevant terms from a total of 89,086 terms, which have been qualified for inclusion in the analyzed pathway. These 37 terms have been assessed manually and as a result 13 new terms were then added as entities to the pathway. Another 14 entities belonged to other pathways, which could form the link of these pathways with the vitamin A pathway. The remaining 10 terms were classified as biomarkers or nutrients. Automatic literature analysis improves the enrichment of pathways with entities already described in the scientific literature.

The MicroRNA Interaction Network of Lipid Diseases

PubMed Central

Kandhro, Abdul H.; Shoombuatong, Watshara; Nantasenamat, Chanin; Prachayasittikul, Virapong; Nuchnoi, Pornlada

2017-01-01

Background: Dyslipidemia is one of the major forms of lipid disorder, characterized by increased triglycerides (TGs), increased low-density lipoprotein-cholesterol (LDL-C), and decreased high-density lipoprotein-cholesterol (HDL-C) levels in blood. Recently, MicroRNAs (miRNAs) have been reported to involve in various biological processes; their potential usage being a biomarkers and in diagnosis of various diseases. Computational approaches including text mining have been used recently to analyze abstracts from the public databases to observe the relationships/associations between the biological molecules, miRNAs, and disease phenotypes. Materials and Methods: In the present study, significance of text mined extracted pair associations (miRNA-lipid disease) were estimated by one-sided Fisher's exact test. The top 20 significant miRNA-disease associations were visualized on Cytoscape. The CyTargetLinker plug-in tool on Cytoscape was used to extend the network and predicts new miRNA target genes. The Biological Networks Gene Ontology (BiNGO) plug-in tool on Cytoscape was used to retrieve gene ontology (GO) annotations for the targeted genes. Results: We retrieved 227 miRNA-lipid disease associations including 148 miRNAs. The top 20 significant miRNAs analysis on CyTargetLinker provides defined, predicted and validated gene targets, further targeted genes analyzed by BiNGO showed targeted genes were significantly associated with lipid, cholesterol, apolipoprotein, and fatty acids GO terms. Conclusion: We are the first to provide a reliable miRNA-lipid disease association network based on text mining. This could help future experimental studies that aim to validate predicted gene targets. PMID:29018475

Data mining learning bootstrap through semantic thumbnail analysis

NASA Astrophysics Data System (ADS)

Battiato, Sebastiano; Farinella, Giovanni Maria; Giuffrida, Giovanni; Tribulato, Giuseppe

2007-01-01

The rapid increase of technological innovations in the mobile phone industry induces the research community to develop new and advanced systems to optimize services offered by mobile phones operators (telcos) to maximize their effectiveness and improve their business. Data mining algorithms can run over data produced by mobile phones usage (e.g. image, video, text and logs files) to discover user's preferences and predict the most likely (to be purchased) offer for each individual customer. One of the main challenges is the reduction of the learning time and cost of these automatic tasks. In this paper we discuss an experiment where a commercial offer is composed by a small picture augmented with a short text describing the offer itself. Each customer's purchase is properly logged with all relevant information. Upon arrival of new items we need to learn who the best customers (prospects) for each item are, that is, the ones most likely to be interested in purchasing that specific item. Such learning activity is time consuming and, in our specific case, is not applicable given the large number of new items arriving every day. Basically, given the current customer base we are not able to learn on all new items. Thus, we need somehow to select among those new items to identify the best candidates. We do so by using a joint analysis between visual features and text to estimate how good each new item could be, that is, whether or not is worth to learn on it. Preliminary results show the effectiveness of the proposed approach to improve classical data mining techniques.

Mining concepts of health responsibility using text mining and exploratory graph analysis.

PubMed

Kjellström, Sofia; Golino, Hudson

2018-05-24

Occupational therapists need to know about people's beliefs about personal responsibility for health to help them pursue everyday activities. The study aims to employ state-of-the-art quantitative approaches to understand people's views of health and responsibility at different ages. A mixed method approach was adopted, using text mining to extract information from 233 interviews with participants aged 5 to 96 years, and then exploratory graph analysis to estimate the number of latent variables. The fit of the structure estimated via the exploratory graph analysis was verified using confirmatory factor analysis. Exploratory graph analysis estimated three dimensions of health responsibility: (1) creating good health habits and feeling good; (2) thinking about one's own health and wanting to improve it; and 3) adopting explicitly normative attitudes to take care of one's health. The comparison between the three dimensions among age groups showed, in general, that children and adolescents, as well as the old elderly (>73 years old) expressed ideas about personal responsibility for health less than young adults, adults and young elderly. Occupational therapists' knowledge of the concepts of health responsibility is of value when working with a patient's health, but an identified challenge is how to engage children and older persons.

Image Mining in Remote Sensing for Coastal Wetlands Mapping: from Pixel Based to Object Based Approach

NASA Astrophysics Data System (ADS)

Farda, N. M.; Danoedoro, P.; Hartono; Harjoko, A.

2016-11-01

The availably of remote sensing image data is numerous now, and with a large amount of data it makes “knowledge gap” in extraction of selected information, especially coastal wetlands. Coastal wetlands provide ecosystem services essential to people and the environment. The aim of this research is to extract coastal wetlands information from satellite data using pixel based and object based image mining approach. Landsat MSS, Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI images located in Segara Anakan lagoon are selected to represent data at various multi temporal images. The input for image mining are visible and near infrared bands, PCA band, invers PCA bands, mean shift segmentation bands, bare soil index, vegetation index, wetness index, elevation from SRTM and ASTER GDEM, and GLCM (Harralick) or variability texture. There is three methods were applied to extract coastal wetlands using image mining: pixel based - Decision Tree C4.5, pixel based - Back Propagation Neural Network, and object based - Mean Shift segmentation and Decision Tree C4.5. The results show that remote sensing image mining can be used to map coastal wetlands ecosystem. Decision Tree C4.5 can be mapped with highest accuracy (0.75 overall kappa). The availability of remote sensing image mining for mapping coastal wetlands is very important to provide better understanding about their spatiotemporal coastal wetlands dynamics distribution.

Text mining for adverse drug events: the promise, challenges, and state of the art.

PubMed

Harpaz, Rave; Callahan, Alison; Tamang, Suzanne; Low, Yen; Odgers, David; Finlayson, Sam; Jung, Kenneth; LePendu, Paea; Shah, Nigam H

2014-10-01

Text mining is the computational process of extracting meaningful information from large amounts of unstructured text. It is emerging as a tool to leverage underutilized data sources that can improve pharmacovigilance, including the objective of adverse drug event (ADE) detection and assessment. This article provides an overview of recent advances in pharmacovigilance driven by the application of text mining, and discusses several data sources-such as biomedical literature, clinical narratives, product labeling, social media, and Web search logs-that are amenable to text mining for pharmacovigilance. Given the state of the art, it appears text mining can be applied to extract useful ADE-related information from multiple textual sources. Nonetheless, further research is required to address remaining technical challenges associated with the text mining methodologies, and to conclusively determine the relative contribution of each textual source to improving pharmacovigilance.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kargupta, H.; Stafford, B.; Hamzaoglu, I.

This paper describes an experimental parallel/distributed data mining system PADMA (PArallel Data Mining Agents) that uses software agents for local data accessing and analysis and a web based interface for interactive data visualization. It also presents the results of applying PADMA for detecting patterns in unstructured texts of postmortem reports and laboratory test data for Hepatitis C patients.

Proceedings: Fourth Workshop on Mining Scientific Datasets

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kamath, C

Commercial applications of data mining in areas such as e-commerce, market-basket analysis, text-mining, and web-mining have taken on a central focus in the JCDD community. However, there is a significant amount of innovative data mining work taking place in the context of scientific and engineering applications that is not well represented in the mainstream KDD conferences. For example, scientific data mining techniques are being developed and applied to diverse fields such as remote sensing, physics, chemistry, biology, astronomy, structural mechanics, computational fluid dynamics etc. In these areas, data mining frequently complements and enhances existing analysis methods based on statistics, exploratorymore » data analysis, and domain-specific approaches. On the surface, it may appear that data from one scientific field, say genomics, is very different from another field, such as physics. However, despite their diversity, there is much that is common across the mining of scientific and engineering data. For example, techniques used to identify objects in images are very similar, regardless of whether the images came from a remote sensing application, a physics experiment, an astronomy observation, or a medical study. Further, with data mining being applied to new types of data, such as mesh data from scientific simulations, there is the opportunity to apply and extend data mining to new scientific domains. This one-day workshop brings together data miners analyzing science data and scientists from diverse fields to share their experiences, learn how techniques developed in one field can be applied in another, and better understand some of the newer techniques being developed in the KDD community. This is the fourth workshop on the topic of Mining Scientific Data sets; for information on earlier workshops, see http://www.ahpcrc.org/conferences/. This workshop continues the tradition of addressing challenging problems in a field where the diversity of applications is matched only by the opportunities that await a practitioner.« less

Revealing Learner Interests through Topic Mining from Question-Answering Data

ERIC Educational Resources Information Center

Dun, Yijie; Wang, Na; Wang, Min; Hao, Tianyong

2017-01-01

In a question-answering system, learner generated content including asked and answered questions is a meaningful resource to capture learning interests. This paper proposes an approach based on question topic mining for revealing learners' concerned topics in real community question-answering systems. The authors' approach firstly preprocesses all…

Matisse: A Visual Analytics System for Exploring Emotion Trends in Social Media Text Streams

DOE Office of Scientific and Technical Information (OSTI.GOV)

Steed, Chad A; Drouhard, Margaret MEG G; Beaver, Justin M

Dynamically mining textual information streams to gain real-time situational awareness is especially challenging with social media systems where throughput and velocity properties push the limits of a static analytical approach. In this paper, we describe an interactive visual analytics system, called Matisse, that aids with the discovery and investigation of trends in streaming text. Matisse addresses the challenges inherent to text stream mining through the following technical contributions: (1) robust stream data management, (2) automated sentiment/emotion analytics, (3) interactive coordinated visualizations, and (4) a flexible drill-down interaction scheme that accesses multiple levels of detail. In addition to positive/negative sentiment prediction,more » Matisse provides fine-grained emotion classification based on Valence, Arousal, and Dominance dimensions and a novel machine learning process. Information from the sentiment/emotion analytics are fused with raw data and summary information to feed temporal, geospatial, term frequency, and scatterplot visualizations using a multi-scale, coordinated interaction model. After describing these techniques, we conclude with a practical case study focused on analyzing the Twitter sample stream during the week of the 2013 Boston Marathon bombings. The case study demonstrates the effectiveness of Matisse at providing guided situational awareness of significant trends in social media streams by orchestrating computational power and human cognition.« less

A data mining based approach to predict spatiotemporal changes in satellite images

NASA Astrophysics Data System (ADS)

Boulila, W.; Farah, I. R.; Ettabaa, K. Saheb; Solaiman, B.; Ghézala, H. Ben

2011-06-01

The interpretation of remotely sensed images in a spatiotemporal context is becoming a valuable research topic. However, the constant growth of data volume in remote sensing imaging makes reaching conclusions based on collected data a challenging task. Recently, data mining appears to be a promising research field leading to several interesting discoveries in various areas such as marketing, surveillance, fraud detection and scientific discovery. By integrating data mining and image interpretation techniques, accurate and relevant information (i.e. functional relation between observed parcels and a set of informational contents) can be automatically elicited. This study presents a new approach to predict spatiotemporal changes in satellite image databases. The proposed method exploits fuzzy sets and data mining concepts to build predictions and decisions for several remote sensing fields. It takes into account imperfections related to the spatiotemporal mining process in order to provide more accurate and reliable information about land cover changes in satellite images. The proposed approach is validated using SPOT images representing the Saint-Denis region, capital of Reunion Island. Results show good performances of the proposed framework in predicting change for the urban zone.

Influence on Learning of a Collaborative Learning Method Comprising the Jigsaw Method and Problem-based Learning (PBL).

PubMed

Takeda, Kayoko; Takahashi, Kiyoshi; Masukawa, Hiroyuki; Shimamori, Yoshimitsu

2017-01-01

Recently, the practice of active learning has spread, increasingly recognized as an essential component of academic studies. Classes incorporating small group discussion (SGD) are conducted at many universities. At present, assessments of the effectiveness of SGD have mostly involved evaluation by questionnaires conducted by teachers, by peer assessment, and by self-evaluation of students. However, qualitative data, such as open-ended descriptions by students, have not been widely evaluated. As a result, we have been unable to analyze the processes and methods involved in how students acquire knowledge in SGD. In recent years, due to advances in information and communication technology (ICT), text mining has enabled the analysis of qualitative data. We therefore investigated whether the introduction of a learning system comprising the jigsaw method and problem-based learning (PBL) would improve student attitudes toward learning; we did this by text mining analysis of the content of student reports. We found that by applying the jigsaw method before PBL, we were able to improve student attitudes toward learning and increase the depth of their understanding of the area of study as a result of working with others. The use of text mining to analyze qualitative data also allowed us to understand the processes and methods by which students acquired knowledge in SGD and also changes in students' understanding and performance based on improvements to the class. This finding suggests that the use of text mining to analyze qualitative data could enable teachers to evaluate the effectiveness of various methods employed to improve learning.

Finding novel relationships with integrated gene-gene association network analysis of Synechocystis sp. PCC 6803 using species-independent text-mining.

PubMed

Kreula, Sanna M; Kaewphan, Suwisa; Ginter, Filip; Jones, Patrik R

2018-01-01

The increasing move towards open access full-text scientific literature enhances our ability to utilize advanced text-mining methods to construct information-rich networks that no human will be able to grasp simply from 'reading the literature'. The utility of text-mining for well-studied species is obvious though the utility for less studied species, or those with no prior track-record at all, is not clear. Here we present a concept for how advanced text-mining can be used to create information-rich networks even for less well studied species and apply it to generate an open-access gene-gene association network resource for Synechocystis sp. PCC 6803, a representative model organism for cyanobacteria and first case-study for the methodology. By merging the text-mining network with networks generated from species-specific experimental data, network integration was used to enhance the accuracy of predicting novel interactions that are biologically relevant. A rule-based algorithm (filter) was constructed in order to automate the search for novel candidate genes with a high degree of likely association to known target genes by (1) ignoring established relationships from the existing literature, as they are already 'known', and (2) demanding multiple independent evidences for every novel and potentially relevant relationship. Using selected case studies, we demonstrate the utility of the network resource and filter to ( i ) discover novel candidate associations between different genes or proteins in the network, and ( ii ) rapidly evaluate the potential role of any one particular gene or protein. The full network is provided as an open-source resource.

The first step in the development of text mining technology for cancer risk assessment: identifying and organizing scientific evidence in risk assessment literature

PubMed Central

Korhonen, Anna; Silins, Ilona; Sun, Lin; Stenius, Ulla

2009-01-01

Background One of the most neglected areas of biomedical Text Mining (TM) is the development of systems based on carefully assessed user needs. We have recently investigated the user needs of an important task yet to be tackled by TM -- Cancer Risk Assessment (CRA). Here we take the first step towards the development of TM technology for the task: identifying and organizing the scientific evidence required for CRA in a taxonomy which is capable of supporting extensive data gathering from biomedical literature. Results The taxonomy is based on expert annotation of 1297 abstracts downloaded from relevant PubMed journals. It classifies 1742 unique keywords found in the corpus to 48 classes which specify core evidence required for CRA. We report promising results with inter-annotator agreement tests and automatic classification of PubMed abstracts to taxonomy classes. A simple user test is also reported in a near real-world CRA scenario which demonstrates along with other evaluation that the resources we have built are well-defined, accurate, and applicable in practice. Conclusion We present our annotation guidelines and a tool which we have designed for expert annotation of PubMed abstracts. A corpus annotated for keywords and document relevance is also presented, along with the taxonomy which organizes the keywords into classes defining core evidence for CRA. As demonstrated by the evaluation, the materials we have constructed provide a good basis for classification of CRA literature along multiple dimensions. They can support current manual CRA as well as facilitate the development of an approach based on TM. We discuss extending the taxonomy further via manual and machine learning approaches and the subsequent steps required to develop TM technology for the needs of CRA. PMID:19772619

Core Values in Nursing Care Based on the Experiences of Nurses Engaged in Neonatal Nursing: A Text-mining Approach for Analyzing Reflection Records

PubMed Central

Watanabe, Hiromi; Okuda, Reiko; Hagino, Hiroshi

2018-01-01

Background Strong feelings about and enthusiasm for nursing care are reflected in nurses’ thoughts and behaviors in clinical practice and affect their profession. This study was conducted to identify the characteristics of core values in nursing care based on the experiences of nurses engaged in neonatal nursing through a process for recognizing the conceptualization of nursing. Methods We conceptualized nursing care in 43 nurses who were involved in neonatal nursing using a reflection sheet. We classified descriptions on a sheet based on the Three-Staged Recognition scheme and analyzed them using a text-mining approach. Results Nurses involved in neonatal nursing recognized that they must take care of the “child,” “mother,” and “family.” Important elements of nursing in nurses with less than 5 years versus 5 or more years of neonatal nursing experience were classified into seven clusters, respectively. These elements were mainly related to family members in both groups. In nurses with less than 5 years of experience, four clusters of one-way communication by nurses were observed in the analysis of the key elements in nursing. On the other hand, five clusters of mutual relationships between patients, their family members, and nurses were observed in nurses with 5 or more years of experience. Conclusion In conclusion, the core value of nurses engaged in neonatal nursing is family-oriented nursing. Nurses with 5 or more years of neonatal nursing experience understand patients and their family members well through establishing relationships and providing comfort and safety while taking care of them. PMID:29599621

Screening Electronic Health Record-Related Patient Safety Reports Using Machine Learning.

PubMed

Marella, William M; Sparnon, Erin; Finley, Edward

2017-03-01

The objective of this study was to develop a semiautomated approach to screening cases that describe hazards associated with the electronic health record (EHR) from a mandatory, population-based patient safety reporting system. Potentially relevant cases were identified through a query of the Pennsylvania Patient Safety Reporting System. A random sample of cases were manually screened for relevance and divided into training, testing, and validation data sets to develop a machine learning model. This model was used to automate screening of remaining potentially relevant cases. Of the 4 algorithms tested, a naive Bayes kernel performed best, with an area under the receiver operating characteristic curve of 0.927 ± 0.023, accuracy of 0.855 ± 0.033, and F score of 0.877 ± 0.027. The machine learning model and text mining approach described here are useful tools for identifying and analyzing adverse event and near-miss reports. Although reporting systems are beginning to incorporate structured fields on health information technology and the EHR, these methods can identify related events that reporters classify in other ways. These methods can facilitate analysis of legacy safety reports by retrieving health information technology-related and EHR-related events from databases without fields and controlled values focused on this subject and distinguishing them from reports in which the EHR is mentioned only in passing. Machine learning and text mining are useful additions to the patient safety toolkit and can be used to semiautomate screening and analysis of unstructured text in safety reports from frontline staff.

TEPAPA: a novel in silico feature learning pipeline for mining prognostic and associative factors from text-based electronic medical records.

PubMed

Lin, Frank Po-Yen; Pokorny, Adrian; Teng, Christina; Epstein, Richard J

2017-07-31

Vast amounts of clinically relevant text-based variables lie undiscovered and unexploited in electronic medical records (EMR). To exploit this untapped resource, and thus facilitate the discovery of informative covariates from unstructured clinical narratives, we have built a novel computational pipeline termed Text-based Exploratory Pattern Analyser for Prognosticator and Associator discovery (TEPAPA). This pipeline combines semantic-free natural language processing (NLP), regular expression induction, and statistical association testing to identify conserved text patterns associated with outcome variables of clinical interest. When we applied TEPAPA to a cohort of head and neck squamous cell carcinoma patients, plausible concepts known to be correlated with human papilloma virus (HPV) status were identified from the EMR text, including site of primary disease, tumour stage, pathologic characteristics, and treatment modalities. Similarly, correlates of other variables (including gender, nodal status, recurrent disease, smoking and alcohol status) were also reliably recovered. Using highly-associated patterns as covariates, a patient's HPV status was classifiable using a bootstrap analysis with a mean area under the ROC curve of 0.861, suggesting its predictive utility in supporting EMR-based phenotyping tasks. These data support using this integrative approach to efficiently identify disease-associated factors from unstructured EMR narratives, and thus to efficiently generate testable hypotheses.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Raymond, David W.; Gaither, Katherine N.; Polsky, Yarom

Sandia National Laboratories (Sandia) has a long history in developing compact, mobile, very high-speed drilling systems and this technology could be applied to increasing the rate at which boreholes are drilled during a mine accident response. The present study reviews current technical approaches, primarily based on technology developed under other programs, analyzes mine rescue specific requirements to develop a conceptual mine rescue drilling approach, and finally, proposes development of a phased mine rescue drilling system (MRDS) that accomplishes (1) development of rapid drilling MRDS equipment; (2) structuring improved web communication through the Mine Safety & Health Administration (MSHA) web site;more » (3) development of an improved protocol for employment of existing drilling technology in emergencies; (4) deployment of advanced technologies to complement mine rescue drilling operations during emergency events; and (5) preliminary discussion of potential future technology development of specialized MRDS equipment. This phased approach allows for rapid fielding of a basic system for improved rescue drilling, with the ability to improve the system over time at a reasonable cost.« less

Fuzzy Document Clustering Approach using WordNet Lexical Categories

NASA Astrophysics Data System (ADS)

Gharib, Tarek F.; Fouad, Mohammed M.; Aref, Mostafa M.

Text mining refers generally to the process of extracting interesting information and knowledge from unstructured text. This area is growing rapidly mainly because of the strong need for analysing the huge and large amount of textual data that reside on internal file systems and the Web. Text document clustering provides an effective navigation mechanism to organize this large amount of data by grouping their documents into a small number of meaningful classes. In this paper we proposed a fuzzy text document clustering approach using WordNet lexical categories and Fuzzy c-Means algorithm. Some experiments are performed to compare efficiency of the proposed approach with the recently reported approaches. Experimental results show that Fuzzy clustering leads to great performance results. Fuzzy c-means algorithm overcomes other classical clustering algorithms like k-means and bisecting k-means in both clustering quality and running time efficiency.

A probabilistic approach for mine burial prediction

NASA Astrophysics Data System (ADS)

Barbu, Costin; Valent, Philip; Richardson, Michael; Abelev, Andrei; Plant, Nathaniel

2004-09-01

Predicting the degree of burial of mines in soft sediments is one of the main concerns of Naval Mine CounterMeasures (MCM) operations. This is a difficult problem to solve due to uncertainties and variability of the sediment parameters (i.e., density and shear strength) and of the mine state at contact with the seafloor (i.e., vertical and horizontal velocity, angular rotation rate, and pitch angle at the mudline). A stochastic approach is proposed in this paper to better incorporate the dynamic nature of free-falling cylindrical mines in the modeling of impact burial. The orientation, trajectory and velocity of cylindrical mines, after about 4 meters free-fall in the water column, are very strongly influenced by boundary layer effects causing quite chaotic behavior. The model's convolution of the uncertainty through its nonlinearity is addressed by employing Monte Carlo simulations. Finally a risk analysis based on the probability of encountering an undetectable mine is performed.

SparkText: Biomedical Text Mining on Big Data Framework.

PubMed

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.

SparkText: Biomedical Text Mining on Big Data Framework

PubMed Central

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

A new approach to preserve privacy data mining based on fuzzy theory in numerical database

NASA Astrophysics Data System (ADS)

Cui, Run; Kim, Hyoung Joong

2014-01-01

With the rapid development of information techniques, data mining approaches have become one of the most important tools to discover the in-deep associations of tuples in large-scale database. Hence how to protect the private information is quite a huge challenge, especially during the data mining procedure. In this paper, a new method is proposed for privacy protection which is based on fuzzy theory. The traditional fuzzy approach in this area will apply fuzzification to the data without considering its readability. A new style of obscured data expression is introduced to provide more details of the subsets without reducing the readability. Also we adopt a balance approach between the privacy level and utility when to achieve the suitable subgroups. An experiment is provided to show that this approach is suitable for the classification without a lower accuracy. In the future, this approach can be adapted to the data stream as the low computation complexity of the fuzzy function with a suitable modification.

Interactive text mining with Pipeline Pilot: a bibliographic web-based tool for PubMed.

PubMed

Vellay, S G P; Latimer, N E Miller; Paillard, G

2009-06-01

Text mining has become an integral part of all research in the medical field. Many text analysis software platforms support particular use cases and only those. We show an example of a bibliographic tool that can be used to support virtually any use case in an agile manner. Here we focus on a Pipeline Pilot web-based application that interactively analyzes and reports on PubMed search results. This will be of interest to any scientist to help identify the most relevant papers in a topical area more quickly and to evaluate the results of query refinement. Links with Entrez databases help both the biologist and the chemist alike. We illustrate this application with Leishmaniasis, a neglected tropical disease, as a case study.

Argo: an integrative, interactive, text mining-based workbench supporting curation

PubMed Central

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

A comprehensive and quantitative comparison of text-mining in 15 million full-text articles versus their corresponding abstracts.

PubMed

Westergaard, David; Stærfeldt, Hans-Henrik; Tønsberg, Christian; Jensen, Lars Juhl; Brunak, Søren

2018-02-01

Across academia and industry, text mining has become a popular strategy for keeping up with the rapid growth of the scientific literature. Text mining of the scientific literature has mostly been carried out on collections of abstracts, due to their availability. Here we present an analysis of 15 million English scientific full-text articles published during the period 1823-2016. We describe the development in article length and publication sub-topics during these nearly 250 years. We showcase the potential of text mining by extracting published protein-protein, disease-gene, and protein subcellular associations using a named entity recognition system, and quantitatively report on their accuracy using gold standard benchmark data sets. We subsequently compare the findings to corresponding results obtained on 16.5 million abstracts included in MEDLINE and show that text mining of full-text articles consistently outperforms using abstracts only.

A comprehensive and quantitative comparison of text-mining in 15 million full-text articles versus their corresponding abstracts

PubMed Central

Westergaard, David; Stærfeldt, Hans-Henrik

2018-01-01

Across academia and industry, text mining has become a popular strategy for keeping up with the rapid growth of the scientific literature. Text mining of the scientific literature has mostly been carried out on collections of abstracts, due to their availability. Here we present an analysis of 15 million English scientific full-text articles published during the period 1823–2016. We describe the development in article length and publication sub-topics during these nearly 250 years. We showcase the potential of text mining by extracting published protein–protein, disease–gene, and protein subcellular associations using a named entity recognition system, and quantitatively report on their accuracy using gold standard benchmark data sets. We subsequently compare the findings to corresponding results obtained on 16.5 million abstracts included in MEDLINE and show that text mining of full-text articles consistently outperforms using abstracts only. PMID:29447159

PubRunner: A light-weight framework for updating text mining results.

PubMed

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.

Climate policy: Uncovering ocean-related priorities

NASA Astrophysics Data System (ADS)

Barkemeyer, Ralf

2017-11-01

Given the complexity and multi-faceted nature of policy processes, national-level policy preferences are notoriously difficult to capture. Now, research applying an automated text mining approach helps to shed light on country-level differences and priorities in the context of marine climate issues.

Design approaches in quarrying and pit-mining reclamation

USGS Publications Warehouse

Arbogast, Belinda F.

1999-01-01

Reclaimed mine sites have been evaluated so that the public, industry, and land planners may recognize there are innovative designs available for consideration and use. People tend to see cropland, range, and road cuts as a necessary part of their everyday life, not as disturbed areas despite their high visibility. Mining also generates a disturbed landscape, unfortunately one that many consider waste until reclaimed by human beings. The development of mining provides an economic base and use of a natural resource to improve the quality of human life. Equally important is a sensitivity to the geologic origin and natural pattern of the land. Wisely shaping out environment requires a design plan and product that responds to a site's physiography, ecology, function, artistic form, and publication perception. An examination of selected sites for their landscape design suggested nine approaches for mining reclamation. The oldest design approach around is nature itself. Humans may sometimes do more damage going to an area in the attempt to repair it. Given enough geologic time, a small-site area, and stable adjacent ecosystems, disturbed areas recover without mankind's input. Visual screens and buffer zones conceal the facility in a camouflage approach. Typically, earth berms, fences, and plantings are used to disguise the mining facility. Restoration targets social or economic benefits by reusing the site for public amenities, most often in urban centers with large populations. A mitigation approach attempts to protect the environment and return mined areas to use with scientific input. The reuse of cement, building rubble, macadam meets only about 10% of the demand from aggregate. Recognizing the limited supply of mineral resources and encouraging recycling efforts are steps are steps in a renewable resource approach. An educative design approach effectively communicates mining information through outreach, land stewardship, and community service. Mine sites used for art show a celebration of beauty and experience -- abstract geology. The last design approach combines art and science in a human-nature ecosystem termed integration. With environmental concerns, an operating or reclaimed mine site can no longer be considered isolated from its surroundings. Site analysis of mine works needs to go beyond site-specific information and relate to the regional context of the greater landscape. Understanding design approach can turn undesirable features (mines and pits) into something perceived as desirable by the public.

Systematic drug repositioning through mining adverse event data in ClinicalTrials.gov.

PubMed

Su, Eric Wen; Sanger, Todd M

2017-01-01

Drug repositioning (i.e., drug repurposing) is the process of discovering new uses for marketed drugs. Historically, such discoveries were serendipitous. However, the rapid growth in electronic clinical data and text mining tools makes it feasible to systematically identify drugs with the potential to be repurposed. Described here is a novel method of drug repositioning by mining ClinicalTrials.gov. The text mining tools I2E (Linguamatics) and PolyAnalyst (Megaputer) were utilized. An I2E query extracts "Serious Adverse Events" (SAE) data from randomized trials in ClinicalTrials.gov. Through a statistical algorithm, a PolyAnalyst workflow ranks the drugs where the treatment arm has fewer predefined SAEs than the control arm, indicating that potentially the drug is reducing the level of SAE. Hypotheses could then be generated for the new use of these drugs based on the predefined SAE that is indicative of disease (for example, cancer).

Text mining for the biocuration workflow

PubMed Central

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

Text mining for the biocuration workflow.

PubMed

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.

Mining Clinicians' Electronic Documentation to Identify Heart Failure Patients with Ineffective Self-Management: A Pilot Text-Mining Study.

PubMed

Topaz, Maxim; Radhakrishnan, Kavita; Lei, Victor; Zhou, Li

2016-01-01

Effective self-management can decrease up to 50% of heart failure hospitalizations. Unfortunately, self-management by patients with heart failure remains poor. This pilot study aimed to explore the use of text-mining to identify heart failure patients with ineffective self-management. We first built a comprehensive self-management vocabulary based on the literature and clinical notes review. We then randomly selected 545 heart failure patients treated within Partners Healthcare hospitals (Boston, MA, USA) and conducted a regular expression search with the compiled vocabulary within 43,107 interdisciplinary clinical notes of these patients. We found that 38.2% (n = 208) patients had documentation of ineffective heart failure self-management in the domains of poor diet adherence (28.4%), missed medical encounters (26.4%) poor medication adherence (20.2%) and non-specified self-management issues (e.g., "compliance issues", 34.6%). We showed the feasibility of using text-mining to identify patients with ineffective self-management. More natural language processing algorithms are needed to help busy clinicians identify these patients.

Frontiers of biomedical text mining: current progress

PubMed Central

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

Sentiment analysis of Arabic tweets using text mining techniques

NASA Astrophysics Data System (ADS)

Al-Horaibi, Lamia; Khan, Muhammad Badruddin

2016-07-01

Sentiment analysis has become a flourishing field of text mining and natural language processing. Sentiment analysis aims to determine whether the text is written to express positive, negative, or neutral emotions about a certain domain. Most sentiment analysis researchers focus on English texts, with very limited resources available for other complex languages, such as Arabic. In this study, the target was to develop an initial model that performs satisfactorily and measures Arabic Twitter sentiment by using machine learning approach, Naïve Bayes and Decision Tree for classification algorithms. The datasets used contains more than 2,000 Arabic tweets collected from Twitter. We performed several experiments to check the performance of the two algorithms classifiers using different combinations of text-processing functions. We found that available facilities for Arabic text processing need to be made from scratch or improved to develop accurate classifiers. The small functionalities developed by us in a Python language environment helped improve the results and proved that sentiment analysis in the Arabic domain needs lot of work on the lexicon side.

Determining Plant – Leaf Miner – Parasitoid Interactions: A DNA Barcoding Approach

PubMed Central

Derocles, Stéphane A. P.; Evans, Darren M.; Nichols, Paul C.; Evans, S. Aifionn; Lunt, David H.

2015-01-01

A major challenge in network ecology is to describe the full-range of species interactions in a community to create highly-resolved food-webs. We developed a molecular approach based on DNA full barcoding and mini-barcoding to describe difficult to observe plant – leaf miner – parasitoid interactions, consisting of animals commonly regarded as agricultural pests and their natural enemies. We tested the ability of universal primers to amplify the remaining DNA inside leaf miner mines after the emergence of the insect. We compared the results of a) morphological identification of adult specimens; b) identification based on the shape of the mines; c) the COI Mini-barcode (130 bp) and d) the COI full barcode (658 bp) fragments to accurately identify the leaf-miner species. We used the molecular approach to build and analyse a tri-partite ecological network of plant – leaf miner – parasitoid interactions. We were able to detect the DNA of leaf-mining insects within their feeding mines on a range of host plants using mini-barcoding primers: 6% for the leaves collected empty and 33% success after we observed the emergence of the leaf miner. We suggest that the low amplification success of leaf mines collected empty was mainly due to the time since the adult emerged and discuss methodological improvements. Nevertheless our approach provided new species-interaction data for the ecological network. We found that the 130 bp fragment is variable enough to identify all the species included in this study. Both COI fragments reveal that some leaf miner species could be composed of cryptic species. The network built using the molecular approach was more accurate in describing tri-partite interactions compared with traditional approaches based on morphological criteria. PMID:25710377

Studies on medicinal herbs for cognitive enhancement based on the text mining of Dongeuibogam and preliminary evaluation of its effects.

PubMed

Pak, Malk Eun; Kim, Yu Ri; Kim, Ha Neui; Ahn, Sung Min; Shin, Hwa Kyoung; Baek, Jin Ung; Choi, Byung Tae

2016-02-17

In literature on Korean medicine, Dongeuibogam (Treasured Mirror of Eastern Medicine), published in 1613, represents the overall results of the traditional medicines of North-East Asia based on prior medicinal literature of this region. We utilized this medicinal literature by text mining to establish a list of candidate herbs for cognitive enhancement in the elderly and then performed an evaluation of their effects. Text mining was performed for selection of candidate herbs. Cell viability was determined in HT22 hippocampal cells and immunohistochemistry and behavioral analysis was performed in a kainic acid (KA) mice model in order to observe alterations of hippocampal cells and cognition. Twenty four herbs for cognitive enhancement in the elderly were selected by text mining of Dongeuibogam. In HT22 cells, pretreatment with 3 candidate herbs resulted in significantly reduced glutamate-induced cell death. Panax ginseng was the most neuroprotective herb against glutamate-induced cell death. In the hippocampus of a KA mice model, pretreatment with 11 candidate herbs resulted in suppression of caspase-3 expression. Treatment with 7 candidate herbs resulted in significantly enhanced expression levels of phosphorylated cAMP response element binding protein. Number of proliferated cells indicated by BrdU labeling was increased by treatment with 10 candidate herbs. Schisandra chinensis was the most effective herb against cell death and proliferation of progenitor cells and Rehmannia glutinosa in neuroprotection in the hippocampus of a KA mice model. In a KA mice model, we confirmed improved spatial and short memory by treatment with the 3 most effective candidate herbs and these recovered functions were involved in a higher number of newly formed neurons from progenitor cells in the hippocampus. These established herbs and their combinations identified by text-mining technique and evaluation for effectiveness may have value in further experimental and clinical applications for cognitive enhancement in the elderly. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Biblio-MetReS for user-friendly mining of genes and biological processes in scientific documents.

PubMed

Usie, Anabel; Karathia, Hiren; Teixidó, Ivan; Alves, Rui; Solsona, Francesc

2014-01-01

One way to initiate the reconstruction of molecular circuits is by using automated text-mining techniques. Developing more efficient methods for such reconstruction is a topic of active research, and those methods are typically included by bioinformaticians in pipelines used to mine and curate large literature datasets. Nevertheless, experimental biologists have a limited number of available user-friendly tools that use text-mining for network reconstruction and require no programming skills to use. One of these tools is Biblio-MetReS. Originally, this tool permitted an on-the-fly analysis of documents contained in a number of web-based literature databases to identify co-occurrence of proteins/genes. This approach ensured results that were always up-to-date with the latest live version of the databases. However, this 'up-to-dateness' came at the cost of large execution times. Here we report an evolution of the application Biblio-MetReS that permits constructing co-occurrence networks for genes, GO processes, Pathways, or any combination of the three types of entities and graphically represent those entities. We show that the performance of Biblio-MetReS in identifying gene co-occurrence is as least as good as that of other comparable applications (STRING and iHOP). In addition, we also show that the identification of GO processes is on par to that reported in the latest BioCreAtIvE challenge. Finally, we also report the implementation of a new strategy that combines on-the-fly analysis of new documents with preprocessed information from documents that were encountered in previous analyses. This combination simultaneously decreases program run time and maintains 'up-to-dateness' of the results. http://metres.udl.cat/index.php/downloads, metres.cmb@gmail.com.

Two modelling approaches to water-quality simulation in a flooded iron-ore mine (Saizerais, Lorraine, France): a semi-distributed chemical reactor model and a physically based distributed reactive transport pipe network model.

PubMed

Hamm, V; Collon-Drouaillet, P; Fabriol, R

2008-02-19

The flooding of abandoned mines in the Lorraine Iron Basin (LIB) over the past 25 years has degraded the quality of the groundwater tapped for drinking water. High concentrations of dissolved sulphate have made the water unsuitable for human consumption. This problematic issue has led to the development of numerical tools to support water-resource management in mining contexts. Here we examine two modelling approaches using different numerical tools that we tested on the Saizerais flooded iron-ore mine (Lorraine, France). A first approach considers the Saizerais Mine as a network of two chemical reactors (NCR). The second approach is based on a physically distributed pipe network model (PNM) built with EPANET 2 software. This approach considers the mine as a network of pipes defined by their geometric and chemical parameters. Each reactor in the NCR model includes a detailed chemical model built to simulate quality evolution in the flooded mine water. However, in order to obtain a robust PNM, we simplified the detailed chemical model into a specific sulphate dissolution-precipitation model that is included as sulphate source/sink in both a NCR model and a pipe network model. Both the NCR model and the PNM, based on different numerical techniques, give good post-calibration agreement between the simulated and measured sulphate concentrations in the drinking-water well and overflow drift. The NCR model incorporating the detailed chemical model is useful when a detailed chemical behaviour at the overflow is needed. The PNM incorporating the simplified sulphate dissolution-precipitation model provides better information of the physics controlling the effect of flow and low flow zones, and the time of solid sulphate removal whereas the NCR model will underestimate clean-up time due to the complete mixing assumption. In conclusion, the detailed NCR model will give a first assessment of chemical processes at overflow, and in a second time, the PNM model will provide more detailed information on flow and chemical behaviour (dissolved sulphate concentrations, remaining mass of solid sulphate) in the network. Nevertheless, both modelling methods require hydrological and chemical parameters (recharge flow rate, outflows, volume of mine voids, mass of solids, kinetic constants of the dissolution-precipitation reactions), which are commonly not available for a mine and therefore call for calibration data.

Automated detection of follow-up appointments using text mining of discharge records.

PubMed

Ruud, Kari L; Johnson, Matthew G; Liesinger, Juliette T; Grafft, Carrie A; Naessens, James M

2010-06-01

To determine whether text mining can accurately detect specific follow-up appointment criteria in free-text hospital discharge records. Cross-sectional study. Mayo Clinic Rochester hospitals. Inpatients discharged from general medicine services in 2006 (n = 6481). Textual hospital dismissal summaries were manually reviewed to determine whether the records contained specific follow-up appointment arrangement elements: date, time and either physician or location for an appointment. The data set was evaluated for the same criteria using SAS Text Miner software. The two assessments were compared to determine the accuracy of text mining for detecting records containing follow-up appointment arrangements. Agreement of text-mined appointment findings with gold standard (manual abstraction) including sensitivity, specificity, positive predictive and negative predictive values (PPV and NPV). About 55.2% (3576) of discharge records contained all criteria for follow-up appointment arrangements according to the manual review, 3.2% (113) of which were missed through text mining. Text mining incorrectly identified 3.7% (107) follow-up appointments that were not considered valid through manual review. Therefore, the text mining analysis concurred with the manual review in 96.6% of the appointment findings. Overall sensitivity and specificity were 96.8 and 96.3%, respectively; and PPV and NPV were 97.0 and 96.1%, respectively. of individual appointment criteria resulted in accuracy rates of 93.5% for date, 97.4% for time, 97.5% for physician and 82.9% for location. Text mining of unstructured hospital dismissal summaries can accurately detect documentation of follow-up appointment arrangement elements, thus saving considerable resources for performance assessment and quality-related research.

Computational systems biology analysis of biomarkers in lung cancer; unravelling genomic regions which frequently encode biomarkers, enriched pathways, and new candidates.

PubMed

Alanazi, Ibrahim O; AlYahya, Sami A; Ebrahimie, Esmaeil; Mohammadi-Dehcheshmeh, Manijeh

2018-06-15

Exponentially growing scientific knowledge in scientific publications has resulted in the emergence of a new interdisciplinary science of literature mining. In text mining, the machine reads the published literature and transfers the discovered knowledge to mathematical-like formulas. In an integrative approach in this study, we used text mining in combination with network discovery, pathway analysis, and enrichment analysis of genomic regions for better understanding of biomarkers in lung cancer. Particular attention was paid to non-coding biomarkers. In total, 60 MicroRNA biomarkers were reported for lung cancer, including some prognostic biomarkers. MIR21, MIR155, MALAT1, and MIR31 were the top non-coding RNA biomarkers of lung cancer. Text mining identified 447 proteins which have been studied as biomarkers in lung cancer. EGFR (receptor), TP53 (transcription factor), KRAS, CDKN2A, ENO2, KRT19, RASSF1, GRP (ligand), SHOX2 (transcription factor), and ERBB2 (receptor) were the most studied proteins. Within small molecules, thymosin-a1, oestrogen, and 8-OHdG have received more attention. We found some chromosomal bands, such as 7q32.2, 18q12.1, 6p12, 11p15.5, and 3p21.3 that are highly involved in deriving lung cancer biomarkers. Copyright © 2018 Elsevier B.V. All rights reserved.

Finding novel relationships with integrated gene-gene association network analysis of Synechocystis sp. PCC 6803 using species-independent text-mining

PubMed Central

Kreula, Sanna M.; Kaewphan, Suwisa; Ginter, Filip

2018-01-01

The increasing move towards open access full-text scientific literature enhances our ability to utilize advanced text-mining methods to construct information-rich networks that no human will be able to grasp simply from ‘reading the literature’. The utility of text-mining for well-studied species is obvious though the utility for less studied species, or those with no prior track-record at all, is not clear. Here we present a concept for how advanced text-mining can be used to create information-rich networks even for less well studied species and apply it to generate an open-access gene-gene association network resource for Synechocystis sp. PCC 6803, a representative model organism for cyanobacteria and first case-study for the methodology. By merging the text-mining network with networks generated from species-specific experimental data, network integration was used to enhance the accuracy of predicting novel interactions that are biologically relevant. A rule-based algorithm (filter) was constructed in order to automate the search for novel candidate genes with a high degree of likely association to known target genes by (1) ignoring established relationships from the existing literature, as they are already ‘known’, and (2) demanding multiple independent evidences for every novel and potentially relevant relationship. Using selected case studies, we demonstrate the utility of the network resource and filter to (i) discover novel candidate associations between different genes or proteins in the network, and (ii) rapidly evaluate the potential role of any one particular gene or protein. The full network is provided as an open-source resource. PMID:29844966

Cinnamon gulch revisited: Another look at separating natural and mining-impacted contributions to instream metal load

USGS Publications Warehouse

Runkel, Robert L.; Verplanck, Philip; Kimball, Briant; Walton-Day, Katie

2018-01-01

Baseline, premining data for streams draining abandoned mine lands is virtually non existent, and indirect methods for estimating premining conditions are needed to establish realistic, cost effective cleanup goals. One such indirect method is the proximal analog approach, in which premining conditions are estimated using data from nearby mineralized areas that are unaffected by mining. In this paper, we combine the proximal analog approach with a quantitative mass balance framework using data from a spatially-detailed synoptic sampling campaign. The combined approach is applied to Cinnamon Gulch, a headwater stream with numerous draining adits. Synoptic sampling results indicate that three of the top five metal sources are affected by mining activities, and stream segments draining these sources account for a large percentage of overall metal loading within the study reach. These initial calculations overestimate the effects of mining, as the affected stream segments were likely acidic and metal rich prior to mining. Premining loads and concentrations were therefore determined through a replacement approach in which the chemistry of each mining-affected stream segment is revised based on proximal analog concentrations. The revised loading profiles indicate that 15–17% of the Al, Cd, Cu, Mn, Ni, and Zn loads are attributable to mining, whereas the mining contribution for Pb is 40%. Premining concentrations of Al, Cd, Cu, Mn, and Zn are estimated to be in excess of aquatic life standards over the length of the study reach.

Discovering Knowledge from Noisy Databases Using Genetic Programming.

ERIC Educational Resources Information Center

Wong, Man Leung; Leung, Kwong Sak; Cheng, Jack C. Y.

2000-01-01

Presents a framework that combines Genetic Programming and Inductive Logic Programming, two approaches in data mining, to induce knowledge from noisy databases. The framework is based on a formalism of logic grammars and is implemented as a data mining system called LOGENPRO (Logic Grammar-based Genetic Programming System). (Contains 34…

Using a Search Engine-Based Mutually Reinforcing Approach to Assess the Semantic Relatedness of Biomedical Terms

PubMed Central

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

Computer-Aided Analysis of Patents for Product Technology Maturity Forecasting

NASA Astrophysics Data System (ADS)

Liang, Yanhong; Gan, Dequan; Guo, Yingchun; Zhang, Peng

Product technology maturity foresting is vital for any enterprises to hold the chance for innovation and keep competitive for a long term. The Theory of Invention Problem Solving (TRIZ) is acknowledged both as a systematic methodology for innovation and a powerful tool for technology forecasting. Based on TRIZ, the state -of-the-art on the technology maturity of product and the limits of application are discussed. With the application of text mining and patent analysis technologies, this paper proposes a computer-aided approach for product technology maturity forecasting. It can overcome the shortcomings of the current methods.

OSCAR4: a flexible architecture for chemical text-mining.

PubMed

Jessop, David M; Adams, Sam E; Willighagen, Egon L; Hawizy, Lezan; Murray-Rust, Peter

2011-10-14

The Open-Source Chemistry Analysis Routines (OSCAR) software, a toolkit for the recognition of named entities and data in chemistry publications, has been developed since 2002. Recent work has resulted in the separation of the core OSCAR functionality and its release as the OSCAR4 library. This library features a modular API (based on reduction of surface coupling) that permits client programmers to easily incorporate it into external applications. OSCAR4 offers a domain-independent architecture upon which chemistry specific text-mining tools can be built, and its development and usage are discussed.

PathText: a text mining integrator for biological pathway visualizations

PubMed Central

Kemper, Brian; Matsuzaki, Takuya; Matsuoka, Yukiko; Tsuruoka, Yoshimasa; Kitano, Hiroaki; Ananiadou, Sophia; Tsujii, Jun'ichi

2010-01-01

Motivation: Metabolic and signaling pathways are an increasingly important part of organizing knowledge in systems biology. They serve to integrate collective interpretations of facts scattered throughout literature. Biologists construct a pathway by reading a large number of articles and interpreting them as a consistent network, but most of the models constructed currently lack direct links to those articles. Biologists who want to check the original articles have to spend substantial amounts of time to collect relevant articles and identify the sections relevant to the pathway. Furthermore, with the scientific literature expanding by several thousand papers per week, keeping a model relevant requires a continuous curation effort. In this article, we present a system designed to integrate a pathway visualizer, text mining systems and annotation tools into a seamless environment. This will enable biologists to freely move between parts of a pathway and relevant sections of articles, as well as identify relevant papers from large text bases. The system, PathText, is developed by Systems Biology Institute, Okinawa Institute of Science and Technology, National Centre for Text Mining (University of Manchester) and the University of Tokyo, and is being used by groups of biologists from these locations. Contact: brian@monrovian.com. PMID:20529930

Detection of interaction articles and experimental methods in biomedical literature.

PubMed

Schneider, Gerold; Clematide, Simon; Rinaldi, Fabio

2011-10-03

This article describes the approaches taken by the OntoGene group at the University of Zurich in dealing with two tasks of the BioCreative III competition: classification of articles which contain curatable protein-protein interactions (PPI-ACT) and extraction of experimental methods (PPI-IMT). Two main achievements are described in this paper: (a) a system for document classification which crucially relies on the results of an advanced pipeline of natural language processing tools; (b) a system which is capable of detecting all experimental methods mentioned in scientific literature, and listing them with a competitive ranking (AUC iP/R > 0.5). The results of the BioCreative III shared evaluation clearly demonstrate that significant progress has been achieved in the domain of biomedical text mining in the past few years. Our own contribution, together with the results of other participants, provides evidence that natural language processing techniques have become by now an integral part of advanced text mining approaches.

Text mining facilitates database curation - extraction of mutation-disease associations from Bio-medical literature.

PubMed

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.

Literature evidence in open targets - a target validation platform.

PubMed

Kafkas, Şenay; Dunham, Ian; McEntyre, Johanna

2017-06-06

We present the Europe PMC literature component of Open Targets - a target validation platform that integrates various evidence to aid drug target identification and validation. The component identifies target-disease associations in documents and ranks the documents based on their confidence from the Europe PMC literature database, by using rules utilising expert-provided heuristic information. The confidence score of a given document represents how valuable the document is in the scope of target validation for a given target-disease association by taking into account the credibility of the association based on the properties of the text. The component serves the platform regularly with the up-to-date data since December, 2015. Currently, there are a total number of 1168365 distinct target-disease associations text mined from >26 million PubMed abstracts and >1.2 million Open Access full text articles. Our comparative analyses on the current available evidence data in the platform revealed that 850179 of these associations are exclusively identified by literature mining. This component helps the platform's users by providing the most relevant literature hits for a given target and disease. The text mining evidence along with the other types of evidence can be explored visually through https://www.targetvalidation.org and all the evidence data is available for download in json format from https://www.targetvalidation.org/downloads/data .

Text mining resources for the life sciences.

PubMed

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.

Text mining resources for the life sciences

PubMed Central

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

Mining and beneficiation: A review of possible lunar applications

NASA Technical Reports Server (NTRS)

Chamberlain, Peter G.

1991-01-01

Successful exploration of Mars and outer space may require base stations strategically located on the Moon. Such bases must develop a certain self-sufficiency, particularly in the critical life support materials, fuel components, and construction materials. Technology is reviewed for the first steps in lunar resource recovery-mining and beneficiation. The topic is covered in three main categories: site selection; mining; and beneficiation. It will also include (in less detail) in-situ processes. The text described mining technology ranging from simple diggings and hauling vehicles (the strawman) to more specialized technology including underground excavation methods. The section of beneficiation emphasizes dry separation techniques and methods of sorting the ore by particle size. In-situ processes, chemical and thermal, are identified to stimulate further thinking by future researchers.

How Readability and Topic Incidence Relate to Performance on Mathematics Story Problems in Computer-Based Curricula

ERIC Educational Resources Information Center

Walkington, Candace; Clinton, Virginia; Ritter, Steven N.; Nathan, Mitchell J.

2015-01-01

Solving mathematics story problems requires text comprehension skills. However, previous studies have found few connections between traditional measures of text readability and performance on story problems. We hypothesized that recently developed measures of readability and topic incidence measured by text-mining tools may illuminate associations…

ParaBTM: A Parallel Processing Framework for Biomedical Text Mining on Supercomputers.

PubMed

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.

Using a Data Mining Approach to Develop a Student Engagement-Based Institutional Typology. IR Applications, Volume 18, February 8, 2009

ERIC Educational Resources Information Center

Luan, Jing; Zhao, Chun-Mei; Hayek, John C.

2009-01-01

Data mining provides both systematic and systemic ways to detect patterns of student engagement among students at hundreds of institutions. Using traditional statistical techniques alone, the task would be significantly difficult--if not impossible--considering the size and complexity in both data and analytical approaches necessary for this…

[Application of text mining approach to pre-education prior to clinical practice].

PubMed

Koinuma, Masayoshi; Koike, Katsuya; Nakamura, Hitoshi

2008-06-01

We developed a new survey analysis technique to understand students' actual aims for effective pretraining prior to clinical practice. We asked third-year undergraduate students to write fixed-style complete and free sentences on "preparation of drug dispensing." Then, we converted their sentence data in to text style and performed Japanese-language morphologic analysis on the data using language analysis software. We classified key words, which were created on the basis of the word class information of the Japanese language morphologic analysis, into categories based on causes and characteristics. In addition to this, we classified the characteristics into six categories consisting of those concepts including "knowledge," "skill and attitude," "image," etc. with the KJ method technique. The results showed that the awareness of students of "preparation of drug dispensing" tended to be approximately three-fold more frequent in "skill and attitude," "risk," etc. than in "knowledge." Regarding the characteristics in the category of the "image," words like "hard," "challenging," "responsibility," "life," etc. frequently occurred. The results of corresponding analysis showed that the characteristics of the words "knowledge" and "skills and attitude" were independent. As the result of developing a cause-and-effect diagram, it was demonstrated that the phase "hanging tough" described most of the various factors. We thus could understand students' actual feelings by applying text-mining as a new survey analysis technique.

An overview of the BioCreative 2012 Workshop Track III: interactive text mining task

PubMed Central

Arighi, Cecilia N.; Carterette, Ben; Cohen, K. Bretonnel; Krallinger, Martin; Wilbur, W. John; Fey, Petra; Dodson, Robert; Cooper, Laurel; Van Slyke, Ceri E.; Dahdul, Wasila; Mabee, Paula; Li, Donghui; Harris, Bethany; Gillespie, Marc; Jimenez, Silvia; Roberts, Phoebe; Matthews, Lisa; Becker, Kevin; Drabkin, Harold; Bello, Susan; Licata, Luana; Chatr-aryamontri, Andrew; Schaeffer, Mary L.; Park, Julie; Haendel, Melissa; Van Auken, Kimberly; Li, Yuling; Chan, Juancarlos; Muller, Hans-Michael; Cui, Hong; Balhoff, James P.; Chi-Yang Wu, Johnny; Lu, Zhiyong; Wei, Chih-Hsuan; Tudor, Catalina O.; Raja, Kalpana; Subramani, Suresh; Natarajan, Jeyakumar; Cejuela, Juan Miguel; Dubey, Pratibha; Wu, Cathy

2013-01-01

In many databases, biocuration primarily involves literature curation, which usually involves retrieving relevant articles, extracting information that will translate into annotations and identifying new incoming literature. As the volume of biological literature increases, the use of text mining to assist in biocuration becomes increasingly relevant. A number of groups have developed tools for text mining from a computer science/linguistics perspective, and there are many initiatives to curate some aspect of biology from the literature. Some biocuration efforts already make use of a text mining tool, but there have not been many broad-based systematic efforts to study which aspects of a text mining tool contribute to its usefulness for a curation task. Here, we report on an effort to bring together text mining tool developers and database biocurators to test the utility and usability of tools. Six text mining systems presenting diverse biocuration tasks participated in a formal evaluation, and appropriate biocurators were recruited for testing. The performance results from this evaluation indicate that some of the systems were able to improve efficiency of curation by speeding up the curation task significantly (∼1.7- to 2.5-fold) over manual curation. In addition, some of the systems were able to improve annotation accuracy when compared with the performance on the manually curated set. In terms of inter-annotator agreement, the factors that contributed to significant differences for some of the systems included the expertise of the biocurator on the given curation task, the inherent difficulty of the curation and attention to annotation guidelines. After the task, annotators were asked to complete a survey to help identify strengths and weaknesses of the various systems. The analysis of this survey highlights how important task completion is to the biocurators’ overall experience of a system, regardless of the system’s high score on design, learnability and usability. In addition, strategies to refine the annotation guidelines and systems documentation, to adapt the tools to the needs and query types the end user might have and to evaluate performance in terms of efficiency, user interface, result export and traditional evaluation metrics have been analyzed during this task. This analysis will help to plan for a more intense study in BioCreative IV. PMID:23327936

An overview of the BioCreative 2012 Workshop Track III: interactive text mining task.

PubMed

Arighi, Cecilia N; Carterette, Ben; Cohen, K Bretonnel; Krallinger, Martin; Wilbur, W John; Fey, Petra; Dodson, Robert; Cooper, Laurel; Van Slyke, Ceri E; Dahdul, Wasila; Mabee, Paula; Li, Donghui; Harris, Bethany; Gillespie, Marc; Jimenez, Silvia; Roberts, Phoebe; Matthews, Lisa; Becker, Kevin; Drabkin, Harold; Bello, Susan; Licata, Luana; Chatr-aryamontri, Andrew; Schaeffer, Mary L; Park, Julie; Haendel, Melissa; Van Auken, Kimberly; Li, Yuling; Chan, Juancarlos; Muller, Hans-Michael; Cui, Hong; Balhoff, James P; Chi-Yang Wu, Johnny; Lu, Zhiyong; Wei, Chih-Hsuan; Tudor, Catalina O; Raja, Kalpana; Subramani, Suresh; Natarajan, Jeyakumar; Cejuela, Juan Miguel; Dubey, Pratibha; Wu, Cathy

2013-01-01

In many databases, biocuration primarily involves literature curation, which usually involves retrieving relevant articles, extracting information that will translate into annotations and identifying new incoming literature. As the volume of biological literature increases, the use of text mining to assist in biocuration becomes increasingly relevant. A number of groups have developed tools for text mining from a computer science/linguistics perspective, and there are many initiatives to curate some aspect of biology from the literature. Some biocuration efforts already make use of a text mining tool, but there have not been many broad-based systematic efforts to study which aspects of a text mining tool contribute to its usefulness for a curation task. Here, we report on an effort to bring together text mining tool developers and database biocurators to test the utility and usability of tools. Six text mining systems presenting diverse biocuration tasks participated in a formal evaluation, and appropriate biocurators were recruited for testing. The performance results from this evaluation indicate that some of the systems were able to improve efficiency of curation by speeding up the curation task significantly (∼1.7- to 2.5-fold) over manual curation. In addition, some of the systems were able to improve annotation accuracy when compared with the performance on the manually curated set. In terms of inter-annotator agreement, the factors that contributed to significant differences for some of the systems included the expertise of the biocurator on the given curation task, the inherent difficulty of the curation and attention to annotation guidelines. After the task, annotators were asked to complete a survey to help identify strengths and weaknesses of the various systems. The analysis of this survey highlights how important task completion is to the biocurators' overall experience of a system, regardless of the system's high score on design, learnability and usability. In addition, strategies to refine the annotation guidelines and systems documentation, to adapt the tools to the needs and query types the end user might have and to evaluate performance in terms of efficiency, user interface, result export and traditional evaluation metrics have been analyzed during this task. This analysis will help to plan for a more intense study in BioCreative IV.

ChemicalTagger: A tool for semantic text-mining in chemistry.

PubMed

Hawizy, Lezan; Jessop, David M; Adams, Nico; Murray-Rust, Peter

2011-05-16

The primary method for scientific communication is in the form of published scientific articles and theses which use natural language combined with domain-specific terminology. As such, they contain free owing unstructured text. Given the usefulness of data extraction from unstructured literature, we aim to show how this can be achieved for the discipline of chemistry. The highly formulaic style of writing most chemists adopt make their contributions well suited to high-throughput Natural Language Processing (NLP) approaches. We have developed the ChemicalTagger parser as a medium-depth, phrase-based semantic NLP tool for the language of chemical experiments. Tagging is based on a modular architecture and uses a combination of OSCAR, domain-specific regex and English taggers to identify parts-of-speech. The ANTLR grammar is used to structure this into tree-based phrases. Using a metric that allows for overlapping annotations, we achieved machine-annotator agreements of 88.9% for phrase recognition and 91.9% for phrase-type identification (Action names). It is possible parse to chemical experimental text using rule-based techniques in conjunction with a formal grammar parser. ChemicalTagger has been deployed for over 10,000 patents and has identified solvents from their linguistic context with >99.5% precision.

Elevated rates of gold mining in the Amazon revealed through high-resolution monitoring.

PubMed

Asner, Gregory P; Llactayo, William; Tupayachi, Raul; Luna, Ernesto Ráez

2013-11-12

Gold mining has rapidly increased in western Amazonia, but the rates and ecological impacts of mining remain poorly known and potentially underestimated. We combined field surveys, airborne mapping, and high-resolution satellite imaging to assess road- and river-based gold mining in the Madre de Dios region of the Peruvian Amazon from 1999 to 2012. In this period, the geographic extent of gold mining increased 400%. The average annual rate of forest loss as a result of gold mining tripled in 2008 following the global economic recession, closely associated with increased gold prices. Small clandestine operations now comprise more than half of all gold mining activities throughout the region. These rates of gold mining are far higher than previous estimates that were based on traditional satellite mapping techniques. Our results prove that gold mining is growing more rapidly than previously thought, and that high-resolution monitoring approaches are required to accurately quantify human impacts on tropical forests.

Elevated rates of gold mining in the Amazon revealed through high-resolution monitoring

PubMed Central

Asner, Gregory P.; Llactayo, William; Tupayachi, Raul; Luna, Ernesto Ráez

2013-01-01

Gold mining has rapidly increased in western Amazonia, but the rates and ecological impacts of mining remain poorly known and potentially underestimated. We combined field surveys, airborne mapping, and high-resolution satellite imaging to assess road- and river-based gold mining in the Madre de Dios region of the Peruvian Amazon from 1999 to 2012. In this period, the geographic extent of gold mining increased 400%. The average annual rate of forest loss as a result of gold mining tripled in 2008 following the global economic recession, closely associated with increased gold prices. Small clandestine operations now comprise more than half of all gold mining activities throughout the region. These rates of gold mining are far higher than previous estimates that were based on traditional satellite mapping techniques. Our results prove that gold mining is growing more rapidly than previously thought, and that high-resolution monitoring approaches are required to accurately quantify human impacts on tropical forests. PMID:24167281

Writers Identification Based on Multiple Windows Features Mining

NASA Astrophysics Data System (ADS)

Fadhil, Murad Saadi; Alkawaz, Mohammed Hazim; Rehman, Amjad; Saba, Tanzila

2016-03-01

Now a days, writer identification is at high demand to identify the original writer of the script at high accuracy. The one of the main challenge in writer identification is how to extract the discriminative features of different authors' scripts to classify precisely. In this paper, the adaptive division method on the offline Latin script has been implemented using several variant window sizes. Fragments of binarized text a set of features are extracted and classified into clusters in the form of groups or classes. Finally, the proposed approach in this paper has been tested on various parameters in terms of text division and window sizes. It is observed that selection of the right window size yields a well positioned window division. The proposed approach is tested on IAM standard dataset (IAM, Institut für Informatik und angewandte Mathematik, University of Bern, Bern, Switzerland) that is a constraint free script database. Finally, achieved results are compared with several techniques reported in the literature.

Text mining-based in silico drug discovery in oral mucositis caused by high-dose cancer therapy.

PubMed

Kirk, Jon; Shah, Nirav; Noll, Braxton; Stevens, Craig B; Lawler, Marshall; Mougeot, Farah B; Mougeot, Jean-Luc C

2018-08-01

Oral mucositis (OM) is a major dose-limiting side effect of chemotherapy and radiation used in cancer treatment. Due to the complex nature of OM, currently available drug-based treatments are of limited efficacy. Our objectives were (i) to determine genes and molecular pathways associated with OM and wound healing using computational tools and publicly available data and (ii) to identify drugs formulated for topical use targeting the relevant OM molecular pathways. OM and wound healing-associated genes were determined by text mining, and the intersection of the two gene sets was selected for gene ontology analysis using the GeneCodis program. Protein interaction network analysis was performed using STRING-db. Enriched gene sets belonging to the identified pathways were queried against the Drug-Gene Interaction database to find drug candidates for topical use in OM. Our analysis identified 447 genes common to both the "OM" and "wound healing" text mining concepts. Gene enrichment analysis yielded 20 genes representing six pathways and targetable by a total of 32 drugs which could possibly be formulated for topical application. A manual search on ClinicalTrials.gov confirmed no relevant pathway/drug candidate had been overlooked. Twenty-five of the 32 drugs can directly affect the PTGS2 (COX-2) pathway, the pathway that has been targeted in previous clinical trials with limited success. Drug discovery using in silico text mining and pathway analysis tools can facilitate the identification of existing drugs that have the potential of topical administration to improve OM treatment.

Theoretical approaches to creation of robotic coal mines based on the synthesis of simulation technologies

NASA Astrophysics Data System (ADS)

Fryanov, V. N.; Pavlova, L. D.; Temlyantsev, M. V.

2017-09-01

Methodological approaches to theoretical substantiation of the structure and parameters of robotic coal mines are outlined. The results of mathematical and numerical modeling revealed the features of manifestation of geomechanical and gas dynamic processes in the conditions of robotic mines. Technological solutions for the design and manufacture of technical means for robotic mine are adopted using the method of economic and mathematical modeling and in accordance with the current regulatory documents. For a comparative performance evaluation of technological schemes of traditional and robotic mines, methods of cognitive modeling and matrix search for subsystem elements in the synthesis of a complex geotechnological system are applied. It is substantiated that the process of technical re-equipment of a traditional mine with a phased transition to a robotic mine will reduce unit costs by almost 1.5 times with a significant social effect due to a reduction in the number of personnel engaged in hazardous work.

Text-mining and information-retrieval services for molecular biology

PubMed Central

Krallinger, Martin; Valencia, Alfonso

2005-01-01

Text-mining in molecular biology - defined as the automatic extraction of information about genes, proteins and their functional relationships from text documents - has emerged as a hybrid discipline on the edges of the fields of information science, bioinformatics and computational linguistics. A range of text-mining applications have been developed recently that will improve access to knowledge for biologists and database annotators. PMID:15998455

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.

Disambiguating the species of biomedical named entities using natural language parsers

PubMed Central

Wang, Xinglong; Tsujii, Jun'ichi; Ananiadou, Sophia

2010-01-01

Motivation: Text mining technologies have been shown to reduce the laborious work involved in organizing the vast amount of information hidden in the literature. One challenge in text mining is linking ambiguous word forms to unambiguous biological concepts. This article reports on a comprehensive study on resolving the ambiguity in mentions of biomedical named entities with respect to model organisms and presents an array of approaches, with focus on methods utilizing natural language parsers. Results: We build a corpus for organism disambiguation where every occurrence of protein/gene entity is manually tagged with a species ID, and evaluate a number of methods on it. Promising results are obtained by training a machine learning model on syntactic parse trees, which is then used to decide whether an entity belongs to the model organism denoted by a neighbouring species-indicating word (e.g. yeast). The parser-based approaches are also compared with a supervised classification method and results indicate that the former are a more favorable choice when domain portability is of concern. The best overall performance is obtained by combining the strengths of syntactic features and supervised classification. Availability: The corpus and demo are available at http://www.nactem.ac.uk/deca_details/start.cgi, and the software is freely available as U-Compare components (Kano et al., 2009): NaCTeM Species Word Detector and NaCTeM Species Disambiguator. U-Compare is available at http://-compare.org/ Contact: xinglong.wang@manchester.ac.uk PMID:20053840

Knowledge discovery through games and game theory

NASA Astrophysics Data System (ADS)

Smith, James F., III; Rhyne, Robert D.

2001-03-01

A fuzzy logic based expert system has been developed that automatically allocates electronic attack (EA) resources in real-time over many dissimilar platforms. The platforms can be very general, e.g., ships, planes, robots, land based facilities, etc. Potential foes the platforms deal with can also be general. The initial version of the algorithm was optimized using a genetic algorithm employing fitness functions constructed based on expertise. A new approach is being explored that involves embedding the resource manager in a electronic game environment. The game allows a human expert to play against the resource manager in a simulated battlespace with each of the defending platforms being exclusively directed by the fuzzy resource manager and the attacking platforms being controlled by the human expert or operating autonomously under their own logic. This approach automates the data mining problem. The game automatically creates a database reflecting the domain expert's knowledge, it calls a data mining function, a genetic algorithm, for data mining of the database as required. The game allows easy evaluation of the information mined in the second step. The measure of effectiveness (MOE) for re-optimization is discussed. The mined information is extremely valuable as shown through demanding scenarios.

What Online Communities Can Tell Us About Electronic Cigarettes and Hookah Use: A Study Using Text Mining and Visualization Techniques.

PubMed

Chen, Annie T; Zhu, Shu-Hong; Conway, Mike

2015-09-29

The rise in popularity of electronic cigarettes (e-cigarettes) and hookah over recent years has been accompanied by some confusion and uncertainty regarding the development of an appropriate regulatory response towards these emerging products. Mining online discussion content can lead to insights into people's experiences, which can in turn further our knowledge of how to address potential health implications. In this work, we take a novel approach to understanding the use and appeal of these emerging products by applying text mining techniques to compare consumer experiences across discussion forums. This study examined content from the websites Vapor Talk, Hookah Forum, and Reddit to understand people's experiences with different tobacco products. Our investigation involves three parts. First, we identified contextual factors that inform our understanding of tobacco use behaviors, such as setting, time, social relationships, and sensory experience, and compared the forums to identify the ones where content on these factors is most common. Second, we compared how the tobacco use experience differs with combustible cigarettes and e-cigarettes. Third, we investigated differences between e-cigarette and hookah use. In the first part of our study, we employed a lexicon-based extraction approach to estimate prevalence of contextual factors, and then we generated a heat map based on these estimates to compare the forums. In the second and third parts of the study, we employed a text mining technique called topic modeling to identify important topics and then developed a visualization, Topic Bars, to compare topic coverage across forums. In the first part of the study, we identified two forums, Vapor Talk Health & Safety and the Stopsmoking subreddit, where discussion concerning contextual factors was particularly common. The second part showed that the discussion in Vapor Talk Health & Safety focused on symptoms and comparisons of combustible cigarettes and e-cigarettes, and the Stopsmoking subreddit focused on psychological aspects of quitting. Last, we examined the discussion content on Vapor Talk and Hookah Forum. Prominent topics included equipment, technique, experiential elements of use, and the buying and selling of equipment. This study has three main contributions. Discussion forums differ in the extent to which their content may help us understand behaviors with potential health implications. Identifying dimensions of interest and using a heat map visualization to compare across forums can be helpful for identifying forums with the greatest density of health information. Additionally, our work has shown that the quitting experience can potentially be very different depending on whether or not e-cigarettes are used. Finally, e-cigarette and hookah forums are similar in that members represent a "hobbyist culture" that actively engages in information exchange. These differences have important implications for both tobacco regulation and smoking cessation intervention design.

An overview of the biocreative 2012 workshop track III: Interactive text mining task

USDA-ARS?s Scientific Manuscript database

An important question is how to make use of text mining to enhance the biocuration workflow. A number of groups have developed tools for text mining from a computer science/linguistics perspective and there are many initiatives to curate some aspect of biology from the literature. In some cases the ...

Using Data Mining to Detect Health Care Fraud and Abuse: A Review of Literature

PubMed Central

Joudaki, Hossein; Rashidian, Arash; Minaei-Bidgoli, Behrouz; Mahmoodi, Mahmood; Geraili, Bijan; Nasiri, Mahdi; Arab, Mohammad

2015-01-01

Inappropriate payments by insurance organizations or third party payers occur because of errors, abuse and fraud. The scale of this problem is large enough to make it a priority issue for health systems. Traditional methods of detecting health care fraud and abuse are time-consuming and inefficient. Combining automated methods and statistical knowledge lead to the emergence of a new interdisciplinary branch of science that is named Knowledge Discovery from Databases (KDD). Data mining is a core of the KDD process. Data mining can help third-party payers such as health insurance organizations to extract useful information from thousands of claims and identify a smaller subset of the claims or claimants for further assessment. We reviewed studies that performed data mining techniques for detecting health care fraud and abuse, using supervised and unsupervised data mining approaches. Most available studies have focused on algorithmic data mining without an emphasis on or application to fraud detection efforts in the context of health service provision or health insurance policy. More studies are needed to connect sound and evidence-based diagnosis and treatment approaches toward fraudulent or abusive behaviors. Ultimately, based on available studies, we recommend seven general steps to data mining of health care claims. PMID:25560347

Text Mining in Cancer Gene and Pathway Prioritization

PubMed Central

Luo, Yuan; Riedlinger, Gregory; Szolovits, Peter

2014-01-01

Prioritization of cancer implicated genes has received growing attention as an effective way to reduce wet lab cost by computational analysis that ranks candidate genes according to the likelihood that experimental verifications will succeed. A multitude of gene prioritization tools have been developed, each integrating different data sources covering gene sequences, differential expressions, function annotations, gene regulations, protein domains, protein interactions, and pathways. This review places existing gene prioritization tools against the backdrop of an integrative Omic hierarchy view toward cancer and focuses on the analysis of their text mining components. We explain the relatively slow progress of text mining in gene prioritization, identify several challenges to current text mining methods, and highlight a few directions where more effective text mining algorithms may improve the overall prioritization task and where prioritizing the pathways may be more desirable than prioritizing only genes. PMID:25392685

Text mining in cancer gene and pathway prioritization.

PubMed

Luo, Yuan; Riedlinger, Gregory; Szolovits, Peter

2014-01-01

Prioritization of cancer implicated genes has received growing attention as an effective way to reduce wet lab cost by computational analysis that ranks candidate genes according to the likelihood that experimental verifications will succeed. A multitude of gene prioritization tools have been developed, each integrating different data sources covering gene sequences, differential expressions, function annotations, gene regulations, protein domains, protein interactions, and pathways. This review places existing gene prioritization tools against the backdrop of an integrative Omic hierarchy view toward cancer and focuses on the analysis of their text mining components. We explain the relatively slow progress of text mining in gene prioritization, identify several challenges to current text mining methods, and highlight a few directions where more effective text mining algorithms may improve the overall prioritization task and where prioritizing the pathways may be more desirable than prioritizing only genes.

Supporting the annotation of chronic obstructive pulmonary disease (COPD) phenotypes with text mining workflows.

PubMed

Fu, Xiao; Batista-Navarro, Riza; Rak, Rafal; Ananiadou, Sophia

2015-01-01

Chronic obstructive pulmonary disease (COPD) is a life-threatening lung disorder whose recent prevalence has led to an increasing burden on public healthcare. Phenotypic information in electronic clinical records is essential in providing suitable personalised treatment to patients with COPD. However, as phenotypes are often "hidden" within free text in clinical records, clinicians could benefit from text mining systems that facilitate their prompt recognition. This paper reports on a semi-automatic methodology for producing a corpus that can ultimately support the development of text mining tools that, in turn, will expedite the process of identifying groups of COPD patients. A corpus of 30 full-text papers was formed based on selection criteria informed by the expertise of COPD specialists. We developed an annotation scheme that is aimed at producing fine-grained, expressive and computable COPD annotations without burdening our curators with a highly complicated task. This was implemented in the Argo platform by means of a semi-automatic annotation workflow that integrates several text mining tools, including a graphical user interface for marking up documents. When evaluated using gold standard (i.e., manually validated) annotations, the semi-automatic workflow was shown to obtain a micro-averaged F-score of 45.70% (with relaxed matching). Utilising the gold standard data to train new concept recognisers, we demonstrated that our corpus, although still a work in progress, can foster the development of significantly better performing COPD phenotype extractors. We describe in this work the means by which we aim to eventually support the process of COPD phenotype curation, i.e., by the application of various text mining tools integrated into an annotation workflow. Although the corpus being described is still under development, our results thus far are encouraging and show great potential in stimulating the development of further automatic COPD phenotype extractors.

A Tools-Based Approach to Teaching Data Mining Methods

ERIC Educational Resources Information Center

Jafar, Musa J.

2010-01-01

Data mining is an emerging field of study in Information Systems programs. Although the course content has been streamlined, the underlying technology is still in a state of flux. The purpose of this paper is to describe how we utilized Microsoft Excel's data mining add-ins as a front-end to Microsoft's Cloud Computing and SQL Server 2008 Business…

PLAN2L: a web tool for integrated text mining and literature-based bioentity relation extraction.

PubMed

Krallinger, Martin; Rodriguez-Penagos, Carlos; Tendulkar, Ashish; Valencia, Alfonso

2009-07-01

There is an increasing interest in using literature mining techniques to complement information extracted from annotation databases or generated by bioinformatics applications. Here we present PLAN2L, a web-based online search system that integrates text mining and information extraction techniques to access systematically information useful for analyzing genetic, cellular and molecular aspects of the plant model organism Arabidopsis thaliana. Our system facilitates a more efficient retrieval of information relevant to heterogeneous biological topics, from implications in biological relationships at the level of protein interactions and gene regulation, to sub-cellular locations of gene products and associations to cellular and developmental processes, i.e. cell cycle, flowering, root, leaf and seed development. Beyond single entities, also predefined pairs of entities can be provided as queries for which literature-derived relations together with textual evidences are returned. PLAN2L does not require registration and is freely accessible at http://zope.bioinfo.cnio.es/plan2l.

Research on the factors influencing the price of commercial housing based on support vector machine (SVM)

NASA Astrophysics Data System (ADS)

Xiaoyang, Zhong; Hong, Ren; Jingxin, Gao

2018-03-01

With the gradual maturity of the real estate market in China, urban housing prices are also better able to reflect changes in market demand and the commodity property of commercial housing has become more and more obvious. Many scholars in our country have made a lot of research on the factors that affect the price of commercial housing in the city and the number of related research papers increased rapidly. These scholars’ research results provide valuable wealth to solve the problem of urban housing price changes in our country. However, due to the huge amount of literature, the vast amount of information is submerged in the library and cannot be fully utilized. Text mining technology has been widely concerned and developed in the field of Humanities and Social Sciences in recent years. But through the text mining technology to obtain the influence factors on the price of urban commercial housing is still relatively rare. In this paper, the research results of the existing scholars were excavated by text mining algorithm based on support vector machine in order to further make full use of the current research results and to provide a reference for stabilizing housing prices.

Optimizing research in symptomatic uterine fibroids with development of a computable phenotype for use with electronic health records.

PubMed

Hoffman, Sarah R; Vines, Anissa I; Halladay, Jacqueline R; Pfaff, Emily; Schiff, Lauren; Westreich, Daniel; Sundaresan, Aditi; Johnson, La-Shell; Nicholson, Wanda K

2018-06-01

Women with symptomatic uterine fibroids can report a myriad of symptoms, including pain, bleeding, infertility, and psychosocial sequelae. Optimizing fibroid research requires the ability to enroll populations of women with image-confirmed symptomatic uterine fibroids. Our objective was to develop an electronic health record-based algorithm to identify women with symptomatic uterine fibroids for a comparative effectiveness study of medical or surgical treatments on quality-of-life measures. Using an iterative process and text-mining techniques, an effective computable phenotype algorithm, composed of demographics, and clinical and laboratory characteristics, was developed with reasonable performance. Such algorithms provide a feasible, efficient way to identify populations of women with symptomatic uterine fibroids for the conduct of large traditional or pragmatic trials and observational comparative effectiveness studies. Symptomatic uterine fibroids, due to menorrhagia, pelvic pain, bulk symptoms, or infertility, are a source of substantial morbidity for reproductive-age women. Comparing Treatment Options for Uterine Fibroids is a multisite registry study to compare the effectiveness of hormonal or surgical fibroid treatments on women's perceptions of their quality of life. Electronic health record-based algorithms are able to identify large numbers of women with fibroids, but additional work is needed to develop electronic health record algorithms that can identify women with symptomatic fibroids to optimize fibroid research. We sought to develop an efficient electronic health record-based algorithm that can identify women with symptomatic uterine fibroids in a large health care system for recruitment into large-scale observational and interventional research in fibroid management. We developed and assessed the accuracy of 3 algorithms to identify patients with symptomatic fibroids using an iterative approach. The data source was the Carolina Data Warehouse for Health, a repository for the health system's electronic health record data. In addition to International Classification of Diseases, Ninth Revision diagnosis and procedure codes and clinical characteristics, text data-mining software was used to derive information from imaging reports to confirm the presence of uterine fibroids. Results of each algorithm were compared with expert manual review to calculate the positive predictive values for each algorithm. Algorithm 1 was composed of the following criteria: (1) age 18-54 years; (2) either ≥1 International Classification of Diseases, Ninth Revision diagnosis codes for uterine fibroids or mention of fibroids using text-mined key words in imaging records or documents; and (3) no International Classification of Diseases, Ninth Revision or Current Procedural Terminology codes for hysterectomy and no reported history of hysterectomy. The positive predictive value was 47% (95% confidence interval 39-56%). Algorithm 2 required ≥2 International Classification of Diseases, Ninth Revision diagnosis codes for fibroids and positive text-mined key words and had a positive predictive value of 65% (95% confidence interval 50-79%). In algorithm 3, further refinements included ≥2 International Classification of Diseases, Ninth Revision diagnosis codes for fibroids on separate outpatient visit dates, the exclusion of women who had a positive pregnancy test within 3 months of their fibroid-related visit, and exclusion of incidentally detected fibroids during prenatal or emergency department visits. Algorithm 3 achieved a positive predictive value of 76% (95% confidence interval 71-81%). An electronic health record-based algorithm is capable of identifying cases of symptomatic uterine fibroids with moderate positive predictive value and may be an efficient approach for large-scale study recruitment. Copyright © 2018 Elsevier Inc. All rights reserved.

Database citation in full text biomedical articles.

PubMed

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.

Database Citation in Full Text Biomedical Articles

PubMed Central

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

Generating a Spanish Affective Dictionary with Supervised Learning Techniques

ERIC Educational Resources Information Center

Bermudez-Gonzalez, Daniel; Miranda-Jiménez, Sabino; García-Moreno, Raúl-Ulises; Calderón-Nepamuceno, Dora

2016-01-01

Nowadays, machine learning techniques are being used in several Natural Language Processing (NLP) tasks such as Opinion Mining (OM). OM is used to analyse and determine the affective orientation of texts. Usually, OM approaches use affective dictionaries in order to conduct sentiment analysis. These lexicons are labeled manually with affective…

Discovering functional modules by topic modeling RNA-Seq based toxicogenomic data.

PubMed

Yu, Ke; Gong, Binsheng; Lee, Mikyung; Liu, Zhichao; Xu, Joshua; Perkins, Roger; Tong, Weida

2014-09-15

Toxicogenomics (TGx) endeavors to elucidate the underlying molecular mechanisms through exploring gene expression profiles in response to toxic substances. Recently, RNA-Seq is increasingly regarded as a more powerful alternative to microarrays in TGx studies. However, realizing RNA-Seq's full potential requires novel approaches to extracting information from the complex TGx data. Considering read counts as the number of times a word occurs in a document, gene expression profiles from RNA-Seq are analogous to a word by document matrix used in text mining. Topic modeling aiming at to discover the latent structures in text corpora would be helpful to explore RNA-Seq based TGx data. In this study, topic modeling was applied on a typical RNA-Seq based TGx data set to discover hidden functional modules. The RNA-Seq based gene expression profiles were transformed into "documents", on which latent Dirichlet allocation (LDA) was used to build a topic model. We found samples treated by the compounds with the same modes of actions (MoAs) could be clustered based on topic similarities. The topic most relevant to each cluster was identified as a "marker" topic, which was interpreted by gene enrichment analysis with MoAs then confirmed by compound and pathways associations mined from literature. To further validate the "marker" topics, we tested topic transferability from RNA-Seq to microarrays. The RNA-Seq based gene expression profile of a topic specifically associated with peroxisome proliferator-activated receptors (PPAR) signaling pathway was used to query samples with similar expression profiles in two different microarray data sets, yielding accuracy of about 85%. This proof-of-concept study demonstrates the applicability of topic modeling to discover functional modules in RNA-Seq data and suggests a valuable computational tool for leveraging information within TGx data in RNA-Seq era.

Public reactions to e-cigarette regulations on Twitter: a text mining analysis.

PubMed

Lazard, Allison J; Wilcox, Gary B; Tuttle, Hannah M; Glowacki, Elizabeth M; Pikowski, Jessica

2017-12-01

In May 2016, the Food and Drug Administration (FDA) issued a final rule that deemed e-cigarettes to be within their regulatory authority as a tobacco product. News and opinions about the regulation were shared on social media platforms, such as Twitter, which can play an important role in shaping the public's attitudes. We analysed information shared on Twitter for insights into initial public reactions. A text mining approach was used to uncover important topics among reactions to the e-cigarette regulations on Twitter. SAS Text Miner V.12.1 software was used for descriptive text mining to uncover the primary topics from tweets collected from May 1 to May 17 2016 using NUVI software to gather the data. A total of nine topics were generated. These topics reveal initial reactions to whether the FDA's e-cigarette regulations will benefit or harm public health, how the regulations will impact the emerging e-cigarette market and efforts to share the news. The topics were dominated by negative or mixed reactions. In the days following the FDA's announcement of the new deeming regulations, the public reaction on Twitter was largely negative. Public health advocates should consider using social media outlets to better communicate the policy's intentions, reach and potential impact for public good to create a more balanced conversation. © 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.

OSCAR4: a flexible architecture for chemical text-mining

PubMed Central

2011-01-01

The Open-Source Chemistry Analysis Routines (OSCAR) software, a toolkit for the recognition of named entities and data in chemistry publications, has been developed since 2002. Recent work has resulted in the separation of the core OSCAR functionality and its release as the OSCAR4 library. This library features a modular API (based on reduction of surface coupling) that permits client programmers to easily incorporate it into external applications. OSCAR4 offers a domain-independent architecture upon which chemistry specific text-mining tools can be built, and its development and usage are discussed. PMID:21999457

A structural informatics approach to mine kinase knowledge bases.

PubMed

Brooijmans, Natasja; Mobilio, Dominick; Walker, Gary; Nilakantan, Ramaswamy; Denny, Rajiah A; Feyfant, Eric; Diller, David; Bikker, Jack; Humblet, Christine

2010-03-01

In this paper, we describe a combination of structural informatics approaches developed to mine data extracted from existing structure knowledge bases (Protein Data Bank and the GVK database) with a focus on kinase ATP-binding site data. In contrast to existing systems that retrieve and analyze protein structures, our techniques are centered on a database of ligand-bound geometries in relation to residues lining the binding site and transparent access to ligand-based SAR data. We illustrate the systems in the context of the Abelson kinase and related inhibitor structures. 2009 Elsevier Ltd. All rights reserved.

Naive Bayes as opinion classifier to evaluate students satisfaction based on student sentiment in Twitter Social Media

NASA Astrophysics Data System (ADS)

Candra Permana, Fahmi; Rosmansyah, Yusep; Setiawan Abdullah, Atje

2017-10-01

Students activity on social media can provide implicit knowledge and new perspectives for an educational system. Sentiment analysis is a part of text mining that can help to analyze and classify the opinion data. This research uses text mining and naive Bayes method as opinion classifier, to be used as an alternative methods in the process of evaluating studentss satisfaction for educational institution. Based on test results, this system can determine the opinion classification in Bahasa Indonesia using naive Bayes as opinion classifier with accuracy level of 84% correct, and the comparison between the existing system and the proposed system to evaluate students satisfaction in learning process, there is only a difference of 16.49%.

Linguistic feature analysis for protein interaction extraction

PubMed Central

2009-01-01

Background The rapid growth of the amount of publicly available reports on biomedical experimental results has recently caused a boost of text mining approaches for protein interaction extraction. Most approaches rely implicitly or explicitly on linguistic, i.e., lexical and syntactic, data extracted from text. However, only few attempts have been made to evaluate the contribution of the different feature types. In this work, we contribute to this evaluation by studying the relative importance of deep syntactic features, i.e., grammatical relations, shallow syntactic features (part-of-speech information) and lexical features. For this purpose, we use a recently proposed approach that uses support vector machines with structured kernels. Results Our results reveal that the contribution of the different feature types varies for the different data sets on which the experiments were conducted. The smaller the training corpus compared to the test data, the more important the role of grammatical relations becomes. Moreover, deep syntactic information based classifiers prove to be more robust on heterogeneous texts where no or only limited common vocabulary is shared. Conclusion Our findings suggest that grammatical relations play an important role in the interaction extraction task. Moreover, the net advantage of adding lexical and shallow syntactic features is small related to the number of added features. This implies that efficient classifiers can be built by using only a small fraction of the features that are typically being used in recent approaches. PMID:19909518

tmBioC: improving interoperability of text-mining tools with BioC.

PubMed

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.

Sampling and monitoring for the mine life cycle

USGS Publications Warehouse

McLemore, Virginia T.; Smith, Kathleen S.; Russell, Carol C.

2014-01-01

Sampling and Monitoring for the Mine Life Cycle provides an overview of sampling for environmental purposes and monitoring of environmentally relevant variables at mining sites. It focuses on environmental sampling and monitoring of surface water, and also considers groundwater, process water streams, rock, soil, and other media including air and biological organisms. The handbook includes an appendix of technical summaries written by subject-matter experts that describe field measurements, collection methods, and analytical techniques and procedures relevant to environmental sampling and monitoring.The sixth of a series of handbooks on technologies for management of metal mine and metallurgical process drainage, this handbook supplements and enhances current literature and provides an awareness of the critical components and complexities involved in environmental sampling and monitoring at the mine site. It differs from most information sources by providing an approach to address all types of mining influenced water and other sampling media throughout the mine life cycle.Sampling and Monitoring for the Mine Life Cycle is organized into a main text and six appendices that are an integral part of the handbook. Sidebars and illustrations are included to provide additional detail about important concepts, to present examples and brief case studies, and to suggest resources for further information. Extensive references are included.

MapReduce in the Cloud: A Use Case Study for Efficient Co-Occurrence Processing of MEDLINE Annotations with MeSH.

PubMed

Kreuzthaler, Markus; Miñarro-Giménez, Jose Antonio; Schulz, Stefan

2016-01-01

Big data resources are difficult to process without a scaled hardware environment that is specifically adapted to the problem. The emergence of flexible cloud-based virtualization techniques promises solutions to this problem. This paper demonstrates how a billion of lines can be processed in a reasonable amount of time in a cloud-based environment. Our use case addresses the accumulation of concept co-occurrence data in MEDLINE annotation as a series of MapReduce jobs, which can be scaled and executed in the cloud. Besides showing an efficient way solving this problem, we generated an additional resource for the scientific community to be used for advanced text mining approaches.

Comparative Analysis of Document level Text Classification Algorithms using R

NASA Astrophysics Data System (ADS)

Syamala, Maganti; Nalini, N. J., Dr; Maguluri, Lakshamanaphaneendra; Ragupathy, R., Dr.

2017-08-01

From the past few decades there has been tremendous volumes of data available in Internet either in structured or unstructured form. Also, there is an exponential growth of information on Internet, so there is an emergent need of text classifiers. Text mining is an interdisciplinary field which draws attention on information retrieval, data mining, machine learning, statistics and computational linguistics. And to handle this situation, a wide range of supervised learning algorithms has been introduced. Among all these K-Nearest Neighbor(KNN) is efficient and simplest classifier in text classification family. But KNN suffers from imbalanced class distribution and noisy term features. So, to cope up with this challenge we use document based centroid dimensionality reduction(CentroidDR) using R Programming. By combining these two text classification techniques, KNN and Centroid classifiers, we propose a scalable and effective flat classifier, called MCenKNN which works well substantially better than CenKNN.

Terminologies for text-mining; an experiment in the lipoprotein metabolism domain

PubMed Central

Alexopoulou, Dimitra; Wächter, Thomas; Pickersgill, Laura; Eyre, Cecilia; Schroeder, Michael

2008-01-01

Background The engineering of ontologies, especially with a view to a text-mining use, is still a new research field. There does not yet exist a well-defined theory and technology for ontology construction. Many of the ontology design steps remain manual and are based on personal experience and intuition. However, there exist a few efforts on automatic construction of ontologies in the form of extracted lists of terms and relations between them. Results We share experience acquired during the manual development of a lipoprotein metabolism ontology (LMO) to be used for text-mining. We compare the manually created ontology terms with the automatically derived terminology from four different automatic term recognition (ATR) methods. The top 50 predicted terms contain up to 89% relevant terms. For the top 1000 terms the best method still generates 51% relevant terms. In a corpus of 3066 documents 53% of LMO terms are contained and 38% can be generated with one of the methods. Conclusions Given high precision, automatic methods can help decrease development time and provide significant support for the identification of domain-specific vocabulary. The coverage of the domain vocabulary depends strongly on the underlying documents. Ontology development for text mining should be performed in a semi-automatic way; taking ATR results as input and following the guidelines we described. Availability The TFIDF term recognition is available as Web Service, described at PMID:18460175

Effective application of improved profit-mining algorithm for the interday trading model.

PubMed

Hsieh, Yu-Lung; Yang, Don-Lin; Wu, Jungpin

2014-01-01

Many real world applications of association rule mining from large databases help users make better decisions. However, they do not work well in financial markets at this time. In addition to a high profit, an investor also looks for a low risk trading with a better rate of winning. The traditional approach of using minimum confidence and support thresholds needs to be changed. Based on an interday model of trading, we proposed effective profit-mining algorithms which provide investors with profit rules including information about profit, risk, and winning rate. Since profit-mining in the financial market is still in its infant stage, it is important to detail the inner working of mining algorithms and illustrate the best way to apply them. In this paper we go into details of our improved profit-mining algorithm and showcase effective applications with experiments using real world trading data. The results show that our approach is practical and effective with good performance for various datasets.

Effective Application of Improved Profit-Mining Algorithm for the Interday Trading Model

PubMed Central

Wu, Jungpin

2014-01-01

Many real world applications of association rule mining from large databases help users make better decisions. However, they do not work well in financial markets at this time. In addition to a high profit, an investor also looks for a low risk trading with a better rate of winning. The traditional approach of using minimum confidence and support thresholds needs to be changed. Based on an interday model of trading, we proposed effective profit-mining algorithms which provide investors with profit rules including information about profit, risk, and winning rate. Since profit-mining in the financial market is still in its infant stage, it is important to detail the inner working of mining algorithms and illustrate the best way to apply them. In this paper we go into details of our improved profit-mining algorithm and showcase effective applications with experiments using real world trading data. The results show that our approach is practical and effective with good performance for various datasets. PMID:24688442

Text Mining of the Classical Medical Literature for Medicines That Show Potential in Diabetic Nephropathy

PubMed Central

Zhang, Lei; Li, Yin; Guo, Xinfeng; May, Brian H.; Xue, Charlie C. L.; Yang, Lihong; Liu, Xusheng

2014-01-01

Objectives. To apply modern text-mining methods to identify candidate herbs and formulae for the treatment of diabetic nephropathy. Methods. The method we developed includes three steps: (1) identification of candidate ancient terms; (2) systemic search and assessment of medical records written in classical Chinese; (3) preliminary evaluation of the effect and safety of candidates. Results. Ancient terms Xia Xiao, Shen Xiao, and Xiao Shen were determined as the most likely to correspond with diabetic nephropathy and used in text mining. A total of 80 Chinese formulae for treating conditions congruent with diabetic nephropathy recorded in medical books from Tang Dynasty to Qing Dynasty were collected. Sao si tang (also called Reeling Silk Decoction) was chosen to show the process of preliminary evaluation of the candidates. It had promising potential for development as new agent for the treatment of diabetic nephropathy. However, further investigations about the safety to patients with renal insufficiency are still needed. Conclusions. The methods developed in this study offer a targeted approach to identifying traditional herbs and/or formulae as candidates for further investigation in the search for new drugs for modern disease. However, more effort is still required to improve our techniques, especially with regard to compound formulae. PMID:24744808

A preliminary approach to creating an overview of lactoferrin multi-functionality utilizing a text mining method.

PubMed

Shimazaki, Kei-ichi; Kushida, Tatsuya

2010-06-01

Lactoferrin is a multi-functional metal-binding glycoprotein that exhibits many biological functions of interest to many researchers from the fields of clinical medicine, dentistry, pharmacology, veterinary medicine, nutrition and milk science. To date, a number of academic reports concerning the biological activities of lactoferrin have been published and are easily accessible through public data repositories. However, as the literature is expanding daily, this presents challenges in understanding the larger picture of lactoferrin function and mechanisms. In order to overcome the "analysis paralysis" associated with lactoferrin information, we attempted to apply a text mining method to the accumulated lactoferrin literature. To this end, we used the information extraction system GENPAC (provided by Nalapro Technologies Inc., Tokyo). This information extraction system uses natural language processing and text mining technology. This system analyzes the sentences and titles from abstracts stored in the PubMed database, and can automatically extract binary relations that consist of interactions between genes/proteins, chemicals and diseases/functions. We expect that such information visualization analysis will be useful in determining novel relationships among a multitude of lactoferrin functions and mechanisms. We have demonstrated the utilization of this method to find pathways of lactoferrin participation in neovascularization, Helicobacter pylori attack on gastric mucosa, atopic dermatitis and lipid metabolism.

Semantic Annotation of Complex Text Structures in Problem Reports

NASA Technical Reports Server (NTRS)

Malin, Jane T.; Throop, David R.; Fleming, Land D.

2011-01-01

Text analysis is important for effective information retrieval from databases where the critical information is embedded in text fields. Aerospace safety depends on effective retrieval of relevant and related problem reports for the purpose of trend analysis. The complex text syntax in problem descriptions has limited statistical text mining of problem reports. The presentation describes an intelligent tagging approach that applies syntactic and then semantic analysis to overcome this problem. The tags identify types of problems and equipment that are embedded in the text descriptions. The power of these tags is illustrated in a faceted searching and browsing interface for problem report trending that combines automatically generated tags with database code fields and temporal information.

A fuzzy hill-climbing algorithm for the development of a compact associative classifier

NASA Astrophysics Data System (ADS)

Mitra, Soumyaroop; Lam, Sarah S.

2012-02-01

Classification, a data mining technique, has widespread applications including medical diagnosis, targeted marketing, and others. Knowledge discovery from databases in the form of association rules is one of the important data mining tasks. An integrated approach, classification based on association rules, has drawn the attention of the data mining community over the last decade. While attention has been mainly focused on increasing classifier accuracies, not much efforts have been devoted towards building interpretable and less complex models. This paper discusses the development of a compact associative classification model using a hill-climbing approach and fuzzy sets. The proposed methodology builds the rule-base by selecting rules which contribute towards increasing training accuracy, thus balancing classification accuracy with the number of classification association rules. The results indicated that the proposed associative classification model can achieve competitive accuracies on benchmark datasets with continuous attributes and lend better interpretability, when compared with other rule-based systems.

Recognition of chemical entities: combining dictionary-based and grammar-based approaches.

PubMed

Akhondi, Saber A; Hettne, Kristina M; van der Horst, Eelke; van Mulligen, Erik M; Kors, Jan A

2015-01-01

The past decade has seen an upsurge in the number of publications in chemistry. The ever-swelling volume of available documents makes it increasingly hard to extract relevant new information from such unstructured texts. The BioCreative CHEMDNER challenge invites the development of systems for the automatic recognition of chemicals in text (CEM task) and for ranking the recognized compounds at the document level (CDI task). We investigated an ensemble approach where dictionary-based named entity recognition is used along with grammar-based recognizers to extract compounds from text. We assessed the performance of ten different commercial and publicly available lexical resources using an open source indexing system (Peregrine), in combination with three different chemical compound recognizers and a set of regular expressions to recognize chemical database identifiers. The effect of different stop-word lists, case-sensitivity matching, and use of chunking information was also investigated. We focused on lexical resources that provide chemical structure information. To rank the different compounds found in a text, we used a term confidence score based on the normalized ratio of the term frequencies in chemical and non-chemical journals. The use of stop-word lists greatly improved the performance of the dictionary-based recognition, but there was no additional benefit from using chunking information. A combination of ChEBI and HMDB as lexical resources, the LeadMine tool for grammar-based recognition, and the regular expressions, outperformed any of the individual systems. On the test set, the F-scores were 77.8% (recall 71.2%, precision 85.8%) for the CEM task and 77.6% (recall 71.7%, precision 84.6%) for the CDI task. Missed terms were mainly due to tokenization issues, poor recognition of formulas, and term conjunctions. We developed an ensemble system that combines dictionary-based and grammar-based approaches for chemical named entity recognition, outperforming any of the individual systems that we considered. The system is able to provide structure information for most of the compounds that are found. Improved tokenization and better recognition of specific entity types is likely to further improve system performance.

Recognition of chemical entities: combining dictionary-based and grammar-based approaches

PubMed Central

2015-01-01

Background The past decade has seen an upsurge in the number of publications in chemistry. The ever-swelling volume of available documents makes it increasingly hard to extract relevant new information from such unstructured texts. The BioCreative CHEMDNER challenge invites the development of systems for the automatic recognition of chemicals in text (CEM task) and for ranking the recognized compounds at the document level (CDI task). We investigated an ensemble approach where dictionary-based named entity recognition is used along with grammar-based recognizers to extract compounds from text. We assessed the performance of ten different commercial and publicly available lexical resources using an open source indexing system (Peregrine), in combination with three different chemical compound recognizers and a set of regular expressions to recognize chemical database identifiers. The effect of different stop-word lists, case-sensitivity matching, and use of chunking information was also investigated. We focused on lexical resources that provide chemical structure information. To rank the different compounds found in a text, we used a term confidence score based on the normalized ratio of the term frequencies in chemical and non-chemical journals. Results The use of stop-word lists greatly improved the performance of the dictionary-based recognition, but there was no additional benefit from using chunking information. A combination of ChEBI and HMDB as lexical resources, the LeadMine tool for grammar-based recognition, and the regular expressions, outperformed any of the individual systems. On the test set, the F-scores were 77.8% (recall 71.2%, precision 85.8%) for the CEM task and 77.6% (recall 71.7%, precision 84.6%) for the CDI task. Missed terms were mainly due to tokenization issues, poor recognition of formulas, and term conjunctions. Conclusions We developed an ensemble system that combines dictionary-based and grammar-based approaches for chemical named entity recognition, outperforming any of the individual systems that we considered. The system is able to provide structure information for most of the compounds that are found. Improved tokenization and better recognition of specific entity types is likely to further improve system performance. PMID:25810767

Overview of the gene ontology task at BioCreative IV.

PubMed

Mao, Yuqing; Van Auken, Kimberly; Li, Donghui; Arighi, Cecilia N; McQuilton, Peter; Hayman, G Thomas; Tweedie, Susan; Schaeffer, Mary L; Laulederkind, Stanley J F; Wang, Shur-Jen; Gobeill, Julien; Ruch, Patrick; Luu, Anh Tuan; Kim, Jung-Jae; Chiang, Jung-Hsien; Chen, Yu-De; Yang, Chia-Jung; Liu, Hongfang; Zhu, Dongqing; Li, Yanpeng; Yu, Hong; Emadzadeh, Ehsan; Gonzalez, Graciela; Chen, Jian-Ming; Dai, Hong-Jie; Lu, Zhiyong

2014-01-01

Gene ontology (GO) annotation is a common task among model organism databases (MODs) for capturing gene function data from journal articles. It is a time-consuming and labor-intensive task, and is thus often considered as one of the bottlenecks in literature curation. There is a growing need for semiautomated or fully automated GO curation techniques that will help database curators to rapidly and accurately identify gene function information in full-length articles. Despite multiple attempts in the past, few studies have proven to be useful with regard to assisting real-world GO curation. The shortage of sentence-level training data and opportunities for interaction between text-mining developers and GO curators has limited the advances in algorithm development and corresponding use in practical circumstances. To this end, we organized a text-mining challenge task for literature-based GO annotation in BioCreative IV. More specifically, we developed two subtasks: (i) to automatically locate text passages that contain GO-relevant information (a text retrieval task) and (ii) to automatically identify relevant GO terms for the genes in a given article (a concept-recognition task). With the support from five MODs, we provided teams with >4000 unique text passages that served as the basis for each GO annotation in our task data. Such evidence text information has long been recognized as critical for text-mining algorithm development but was never made available because of the high cost of curation. In total, seven teams participated in the challenge task. From the team results, we conclude that the state of the art in automatically mining GO terms from literature has improved over the past decade while much progress is still needed for computer-assisted GO curation. Future work should focus on addressing remaining technical challenges for improved performance of automatic GO concept recognition and incorporating practical benefits of text-mining tools into real-world GO annotation. http://www.biocreative.org/tasks/biocreative-iv/track-4-GO/. Published by Oxford University Press 2014. This work is written by US Government employees and is in the public domain in the US.

Multi-Filter String Matching and Human-Centric Entity Matching for Information Extraction

ERIC Educational Resources Information Center

Sun, Chong

2012-01-01

More and more information is being generated in text documents, such as Web pages, emails and blogs. To effectively manage this unstructured information, one broadly used approach includes locating relevant content in documents, extracting structured information and integrating the extracted information for querying, mining or further analysis. In…

Analysis of mesenchymal stem cell differentiation in vitro using classification association rule mining.

PubMed

Wang, Weiqi; Wang, Yanbo Justin; Bañares-Alcántara, René; Coenen, Frans; Cui, Zhanfeng

2009-12-01

In this paper, data mining is used to analyze the data on the differentiation of mammalian Mesenchymal Stem Cells (MSCs), aiming at discovering known and hidden rules governing MSC differentiation, following the establishment of a web-based public database containing experimental data on the MSC proliferation and differentiation. To this effect, a web-based public interactive database comprising the key parameters which influence the fate and destiny of mammalian MSCs has been constructed and analyzed using Classification Association Rule Mining (CARM) as a data-mining technique. The results show that the proposed approach is technically feasible and performs well with respect to the accuracy of (classification) prediction. Key rules mined from the constructed MSC database are consistent with experimental observations, indicating the validity of the method developed and the first step in the application of data mining to the study of MSCs.

Risk Assessment in Underground Coalmines Using Fuzzy Logic in the Presence of Uncertainty

NASA Astrophysics Data System (ADS)

Tripathy, Debi Prasad; Ala, Charan Kumar

2018-04-01

Fatal accidents are occurring every year as regular events in Indian coal mining industry. To increase the safety conditions, it has become a prerequisite to performing a risk assessment of various operations in mines. However, due to uncertain accident data, it is hard to conduct a risk assessment in mines. The object of this study is to present a method to assess safety risks in underground coalmines. The assessment of safety risks is based on the fuzzy reasoning approach. Mamdani fuzzy logic model is developed in the fuzzy logic toolbox of MATLAB. A case study is used to demonstrate the applicability of the developed model. The summary of risk evaluation in case study mine indicated that mine fire has the highest risk level among all the hazard factors. This study could help the mine management to prepare safety measures based on the risk rankings obtained.

A semantic model for multimodal data mining in healthcare information systems.

PubMed

Iakovidis, Dimitris; Smailis, Christos

2012-01-01

Electronic health records (EHRs) are representative examples of multimodal/multisource data collections; including measurements, images and free texts. The diversity of such information sources and the increasing amounts of medical data produced by healthcare institutes annually, pose significant challenges in data mining. In this paper we present a novel semantic model that describes knowledge extracted from the lowest-level of a data mining process, where information is represented by multiple features i.e. measurements or numerical descriptors extracted from measurements, images, texts or other medical data, forming multidimensional feature spaces. Knowledge collected by manual annotation or extracted by unsupervised data mining from one or more feature spaces is modeled through generalized qualitative spatial semantics. This model enables a unified representation of knowledge across multimodal data repositories. It contributes to bridging the semantic gap, by enabling direct links between low-level features and higher-level concepts e.g. describing body parts, anatomies and pathological findings. The proposed model has been developed in web ontology language based on description logics (OWL-DL) and can be applied to a variety of data mining tasks in medical informatics. It utility is demonstrated for automatic annotation of medical data.

Localisation of an Unknown Number of Land Mines Using a Network of Vapour Detectors

PubMed Central

Chhadé, Hiba Haj; Abdallah, Fahed; Mougharbel, Imad; Gning, Amadou; Julier, Simon; Mihaylova, Lyudmila

2014-01-01

We consider the problem of localising an unknown number of land mines using concentration information provided by a wireless sensor network. A number of vapour sensors/detectors, deployed in the region of interest, are able to detect the concentration of the explosive vapours, emanating from buried land mines. The collected data is communicated to a fusion centre. Using a model for the transport of the explosive chemicals in the air, we determine the unknown number of sources using a Principal Component Analysis (PCA)-based technique. We also formulate the inverse problem of determining the positions and emission rates of the land mines using concentration measurements provided by the wireless sensor network. We present a solution for this problem based on a probabilistic Bayesian technique using a Markov chain Monte Carlo sampling scheme, and we compare it to the least squares optimisation approach. Experiments conducted on simulated data show the effectiveness of the proposed approach. PMID:25384008

Contextual Text Mining

ERIC Educational Resources Information Center

Mei, Qiaozhu

2009-01-01

With the dramatic growth of text information, there is an increasing need for powerful text mining systems that can automatically discover useful knowledge from text. Text is generally associated with all kinds of contextual information. Those contexts can be explicit, such as the time and the location where a blog article is written, and the…

Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features.

PubMed

Nikfarjam, Azadeh; Sarker, Abeed; O'Connor, Karen; Ginn, Rachel; Gonzalez, Graciela

2015-05-01

Social media is becoming increasingly popular as a platform for sharing personal health-related information. This information can be utilized for public health monitoring tasks, particularly for pharmacovigilance, via the use of natural language processing (NLP) techniques. However, the language in social media is highly informal, and user-expressed medical concepts are often nontechnical, descriptive, and challenging to extract. There has been limited progress in addressing these challenges, and thus far, advanced machine learning-based NLP techniques have been underutilized. Our objective is to design a machine learning-based approach to extract mentions of adverse drug reactions (ADRs) from highly informal text in social media. We introduce ADRMine, a machine learning-based concept extraction system that uses conditional random fields (CRFs). ADRMine utilizes a variety of features, including a novel feature for modeling words' semantic similarities. The similarities are modeled by clustering words based on unsupervised, pretrained word representation vectors (embeddings) generated from unlabeled user posts in social media using a deep learning technique. ADRMine outperforms several strong baseline systems in the ADR extraction task by achieving an F-measure of 0.82. Feature analysis demonstrates that the proposed word cluster features significantly improve extraction performance. It is possible to extract complex medical concepts, with relatively high performance, from informal, user-generated content. Our approach is particularly scalable, suitable for social media mining, as it relies on large volumes of unlabeled data, thus diminishing the need for large, annotated training data sets. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

Chapter 3: Selecting materials for mine soil construction when establishing forests on Appalachian mined lands

Treesearch

Jeff Skousen; Carl Zipper; Jim Burger; Christopher Barton; Patrick. Angel

2017-01-01

The Forestry Reclamation Approach (FRA), a method for reclaiming coal-mined land to forest (Chapter 2, this volume), is based on research, knowledge, and experience of forest soil scientists and reclamation practitioners. Step 1 of the FRA is to create a suitable rooting medium for good tree growth that is no less than 4 feet deep and consists of topsoil, weathered...

Spectral methods to detect surface mines

NASA Astrophysics Data System (ADS)

Winter, Edwin M.; Schatten Silvious, Miranda

2008-04-01

Over the past five years, advances have been made in the spectral detection of surface mines under minefield detection programs at the U. S. Army RDECOM CERDEC Night Vision and Electronic Sensors Directorate (NVESD). The problem of detecting surface land mines ranges from the relatively simple, the detection of large anti-vehicle mines on bare soil, to the very difficult, the detection of anti-personnel mines in thick vegetation. While spatial and spectral approaches can be applied to the detection of surface mines, spatial-only detection requires many pixels-on-target such that the mine is actually imaged and shape-based features can be exploited. This method is unreliable in vegetated areas because only part of the mine may be exposed, while spectral detection is possible without the mine being resolved. At NVESD, hyperspectral and multi-spectral sensors throughout the reflection and thermal spectral regimes have been applied to the mine detection problem. Data has been collected on mines in forest and desert regions and algorithms have been developed both to detect the mines as anomalies and to detect the mines based on their spectral signature. In addition to the detection of individual mines, algorithms have been developed to exploit the similarities of mines in a minefield to improve their detection probability. In this paper, the types of spectral data collected over the past five years will be summarized along with the advances in algorithm development.

Science for watershed decisions on abandoned mine lands; review of preliminary results, Denver, Colorado, February 4-5, 1998

USGS Publications Warehouse

Nimick, David A.; Von Guerard, Paul

1998-01-01

From the Preface: There are thousands of abandoned or inactive mines on or adjacent to public lands administered by the U.S. Forest Service, Bureau of Land Management, and National Park Service. Mine wastes from many of these abandoned mines adversely affect resources on public lands. In 1995, an interdepartmental work group within the Federal government developed a strategy to address remediation of the many abandoned mines on public lands. This strategy is based on using a watershed approach to address the abandoned mine lands (AML) problem. The USGS, working closely with the Federal land-management agencies (FLMAs), is key for the success of this watershed approach. In support of this watershed approach, the USGS developed an AML Initiative with pilot studies in the Boulder River in Montana and the Animas River in Colorado. The goal of these studies is to design and implement a reliable strategy that will supply the scientific information to the FLMAs so that land managers can develop efficient and cost-effective remediation of AML. The symposium 'Science for Watershed Decisions on Abandoned Mine Lands: Review of Preliminary Results' held in Denver, Colorado, on February 4-5, 1998, provided the FLMAs a first look at the techniques, data, and interpretations being generated by the USGS pilot studies. This multidisciplined effort already is proving very valuable to land managers in making science-based AML cleanup decisions and will continue to be of increasing value as additional and more complete information is obtained. Ongoing interaction between scientists and land managers is essential to insure the efficient continuation and success of AML cleanup efforts.

Chemical Topic Modeling: Exploring Molecular Data Sets Using a Common Text-Mining Approach.

PubMed

Schneider, Nadine; Fechner, Nikolas; Landrum, Gregory A; Stiefl, Nikolaus

2017-08-28

Big data is one of the key transformative factors which increasingly influences all aspects of modern life. Although this transformation brings vast opportunities it also generates novel challenges, not the least of which is organizing and searching this data deluge. The field of medicinal chemistry is not different: more and more data are being generated, for instance, by technologies such as DNA encoded libraries, peptide libraries, text mining of large literature corpora, and new in silico enumeration methods. Handling those huge sets of molecules effectively is quite challenging and requires compromises that often come at the expense of the interpretability of the results. In order to find an intuitive and meaningful approach to organizing large molecular data sets, we adopted a probabilistic framework called "topic modeling" from the text-mining field. Here we present the first chemistry-related implementation of this method, which allows large molecule sets to be assigned to "chemical topics" and investigating the relationships between those. In this first study, we thoroughly evaluate this novel method in different experiments and discuss both its disadvantages and advantages. We show very promising results in reproducing human-assigned concepts using the approach to identify and retrieve chemical series from sets of molecules. We have also created an intuitive visualization of the chemical topics output by the algorithm. This is a huge benefit compared to other unsupervised machine-learning methods, like clustering, which are commonly used to group sets of molecules. Finally, we applied the new method to the 1.6 million molecules of the ChEMBL22 data set to test its robustness and efficiency. In about 1 h we built a 100-topic model of this large data set in which we could identify interesting topics like "proteins", "DNA", or "steroids". Along with this publication we provide our data sets and an open-source implementation of the new method (CheTo) which will be part of an upcoming version of the open-source cheminformatics toolkit RDKit.

A novel web informatics approach for automated surveillance of cancer mortality trends✩

PubMed Central

Tourassi, Georgia; Yoon, Hong-Jun; Xu, Songhua

2016-01-01

Cancer surveillance data are collected every year in the United States via the National Program of Cancer Registries (NPCR) and the Surveillance, Epidemiology and End Results (SEER) Program of the National Cancer Institute (NCI). General trends are closely monitored to measure the nation's progress against cancer. The objective of this study was to apply a novel web informatics approach for enabling fully automated monitoring of cancer mortality trends. The approach involves automated collection and text mining of online obituaries to derive the age distribution, geospatial, and temporal trends of cancer deaths in the US. Using breast and lung cancer as examples, we mined 23,850 cancer-related and 413,024 general online obituaries spanning the timeframe 2008–2012. There was high correlation between the web-derived mortality trends and the official surveillance statistics reported by NCI with respect to the age distribution (ρ = 0.981 for breast; ρ = 0.994 for lung), the geospatial distribution (ρ = 0.939 for breast; ρ = 0.881 for lung), and the annual rates of cancer deaths (ρ = 0.661 for breast; ρ = 0.839 for lung). Additional experiments investigated the effect of sample size on the consistency of the web-based findings. Overall, our study findings support web informatics as a promising, cost-effective way to dynamically monitor spatiotemporal cancer mortality trends. PMID:27044930

A novel web informatics approach for automated surveillance of cancer mortality trends

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tourassi, Georgia; Yoon, Hong -Jun; Xu, Songhua

Cancer surveillance data are collected every year in the United States via the National Program of Cancer Registries (NPCR) and the Surveillance, Epidemiology and End Results (SEER) Program of the National Cancer Institute (NCI). General trends are closely monitored to measure the nation’s progress against cancer. The objective of this study was to apply a novel web informatics approach for enabling fully automated monitoring of cancer mortality trends. The approach involves automated collection and text mining of online obituaries to derive the age distribution, geospatial, and temporal trends of cancer deaths in the US. Using breast and lung cancer asmore » examples, we mined 23,850 cancer-related and 413,024 general online obituaries spanning the timeframe 2008–2012. There was high correlation between the web-derived mortality trends and the official surveillance statistics reported by NCI with respect to the age distribution (ρ = 0.981 for breast; ρ = 0.994 for lung), the geospatial distribution (ρ = 0.939 for breast; ρ = 0.881 for lung), and the annual rates of cancer deaths (ρ = 0.661 for breast; ρ = 0.839 for lung). Additional experiments investigated the effect of sample size on the consistency of the web-based findings. Altogether, our study findings support web informatics as a promising, cost-effective way to dynamically monitor spatiotemporal cancer mortality trends.« less

A novel web informatics approach for automated surveillance of cancer mortality trends

DOE PAGES

Tourassi, Georgia; Yoon, Hong -Jun; Xu, Songhua

2016-04-01

Cancer surveillance data are collected every year in the United States via the National Program of Cancer Registries (NPCR) and the Surveillance, Epidemiology and End Results (SEER) Program of the National Cancer Institute (NCI). General trends are closely monitored to measure the nation’s progress against cancer. The objective of this study was to apply a novel web informatics approach for enabling fully automated monitoring of cancer mortality trends. The approach involves automated collection and text mining of online obituaries to derive the age distribution, geospatial, and temporal trends of cancer deaths in the US. Using breast and lung cancer asmore » examples, we mined 23,850 cancer-related and 413,024 general online obituaries spanning the timeframe 2008–2012. There was high correlation between the web-derived mortality trends and the official surveillance statistics reported by NCI with respect to the age distribution (ρ = 0.981 for breast; ρ = 0.994 for lung), the geospatial distribution (ρ = 0.939 for breast; ρ = 0.881 for lung), and the annual rates of cancer deaths (ρ = 0.661 for breast; ρ = 0.839 for lung). Additional experiments investigated the effect of sample size on the consistency of the web-based findings. Altogether, our study findings support web informatics as a promising, cost-effective way to dynamically monitor spatiotemporal cancer mortality trends.« less

Restoring Forests and Associated Ecosystem Services on Appalachian Coal Surface Mines

NASA Astrophysics Data System (ADS)

Zipper, Carl E.; Burger, James A.; Skousen, Jeffrey G.; Angel, Patrick N.; Barton, Christopher D.; Davis, Victor; Franklin, Jennifer A.

2011-05-01

Surface coal mining in Appalachia has caused extensive replacement of forest with non-forested land cover, much of which is unmanaged and unproductive. Although forested ecosystems are valued by society for both marketable products and ecosystem services, forests have not been restored on most Appalachian mined lands because traditional reclamation practices, encouraged by regulatory policies, created conditions poorly suited for reforestation. Reclamation scientists have studied productive forests growing on older mine sites, established forest vegetation experimentally on recent mines, and identified mine reclamation practices that encourage forest vegetation re-establishment. Based on these findings, they developed a Forestry Reclamation Approach (FRA) that can be employed by coal mining firms to restore forest vegetation. Scientists and mine regulators, working collaboratively, have communicated the FRA to the coal industry and to regulatory enforcement personnel. Today, the FRA is used routinely by many coal mining firms, and thousands of mined hectares have been reclaimed to restore productive mine soils and planted with native forest trees. Reclamation of coal mines using the FRA is expected to restore these lands' capabilities to provide forest-based ecosystem services, such as wood production, atmospheric carbon sequestration, wildlife habitat, watershed protection, and water quality protection to a greater extent than conventional reclamation practices.

Text feature extraction based on deep learning: a review.

PubMed

Liang, Hong; Sun, Xiao; Sun, Yunlei; Gao, Yuan

2017-01-01

Selection of text feature item is a basic and important matter for text mining and information retrieval. Traditional methods of feature extraction require handcrafted features. To hand-design, an effective feature is a lengthy process, but aiming at new applications, deep learning enables to acquire new effective feature representation from training data. As a new feature extraction method, deep learning has made achievements in text mining. The major difference between deep learning and conventional methods is that deep learning automatically learns features from big data, instead of adopting handcrafted features, which mainly depends on priori knowledge of designers and is highly impossible to take the advantage of big data. Deep learning can automatically learn feature representation from big data, including millions of parameters. This thesis outlines the common methods used in text feature extraction first, and then expands frequently used deep learning methods in text feature extraction and its applications, and forecasts the application of deep learning in feature extraction.

Unsupervised text mining for assessing and augmenting GWAS results.

PubMed

Ailem, Melissa; Role, François; Nadif, Mohamed; Demenais, Florence

2016-04-01

Text mining can assist in the analysis and interpretation of large-scale biomedical data, helping biologists to quickly and cheaply gain confirmation of hypothesized relationships between biological entities. We set this question in the context of genome-wide association studies (GWAS), an actively emerging field that contributed to identify many genes associated with multifactorial diseases. These studies allow to identify groups of genes associated with the same phenotype, but provide no information about the relationships between these genes. Therefore, our objective is to leverage unsupervised text mining techniques using text-based cosine similarity comparisons and clustering applied to candidate and random gene vectors, in order to augment the GWAS results. We propose a generic framework which we used to characterize the relationships between 10 genes reported associated with asthma by a previous GWAS. The results of this experiment showed that the similarities between these 10 genes were significantly stronger than would be expected by chance (one-sided p-value<0.01). The clustering of observed and randomly selected gene also allowed to generate hypotheses about potential functional relationships between these genes and thus contributed to the discovery of new candidate genes for asthma. Copyright © 2016 Elsevier Inc. All rights reserved.

U-Compare: share and compare text mining tools with UIMA.

PubMed

Kano, Yoshinobu; Baumgartner, William A; McCrohon, Luke; Ananiadou, Sophia; Cohen, K Bretonnel; Hunter, Lawrence; Tsujii, Jun'ichi

2009-08-01

Due to the increasing number of text mining resources (tools and corpora) available to biologists, interoperability issues between these resources are becoming significant obstacles to using them effectively. UIMA, the Unstructured Information Management Architecture, is an open framework designed to aid in the construction of more interoperable tools. U-Compare is built on top of the UIMA framework, and provides both a concrete framework for out-of-the-box text mining and a sophisticated evaluation platform allowing users to run specific tools on any target text, generating both detailed statistics and instance-based visualizations of outputs. U-Compare is a joint project, providing the world's largest, and still growing, collection of UIMA-compatible resources. These resources, originally developed by different groups for a variety of domains, include many famous tools and corpora. U-Compare can be launched straight from the web, without needing to be manually installed. All U-Compare components are provided ready-to-use and can be combined easily via a drag-and-drop interface without any programming. External UIMA components can also simply be mixed with U-Compare components, without distinguishing between locally and remotely deployed resources. http://u-compare.org/

GNormPlus: An Integrative Approach for Tagging Genes, Gene Families, and Protein Domains

PubMed Central

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

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.

Multi-label literature classification based on the Gene Ontology graph.

PubMed

Jin, Bo; Muller, Brian; Zhai, Chengxiang; Lu, Xinghua

2008-12-08

The Gene Ontology is a controlled vocabulary for representing knowledge related to genes and proteins in a computable form. The current effort of manually annotating proteins with the Gene Ontology is outpaced by the rate of accumulation of biomedical knowledge in literature, which urges the development of text mining approaches to facilitate the process by automatically extracting the Gene Ontology annotation from literature. The task is usually cast as a text classification problem, and contemporary methods are confronted with unbalanced training data and the difficulties associated with multi-label classification. In this research, we investigated the methods of enhancing automatic multi-label classification of biomedical literature by utilizing the structure of the Gene Ontology graph. We have studied three graph-based multi-label classification algorithms, including a novel stochastic algorithm and two top-down hierarchical classification methods for multi-label literature classification. We systematically evaluated and compared these graph-based classification algorithms to a conventional flat multi-label algorithm. The results indicate that, through utilizing the information from the structure of the Gene Ontology graph, the graph-based multi-label classification methods can significantly improve predictions of the Gene Ontology terms implied by the analyzed text. Furthermore, the graph-based multi-label classifiers are capable of suggesting Gene Ontology annotations (to curators) that are closely related to the true annotations even if they fail to predict the true ones directly. A software package implementing the studied algorithms is available for the research community. Through utilizing the information from the structure of the Gene Ontology graph, the graph-based multi-label classification methods have better potential than the conventional flat multi-label classification approach to facilitate protein annotation based on the literature.

Evolutionary Data Mining Approach to Creating Digital Logic

DTIC Science & Technology

2010-01-01

To deal with this problem a genetic program (GP) based data mining ( DM ) procedure has been invented (Smith 2005). A genetic program is an algorithm...that can operate on the variables. When a GP was used as a DM function in the past to automatically create fuzzy decision trees, the Report...rules represents an approach to the determining the effect of linguistic imprecision, i.e., the inability of experts to provide crisp rules. The

Documenting research with transgender and gender diverse people: protocol for an evidence map and thematic analysis.

PubMed

Marshall, Zack; Welch, Vivian; Thomas, James; Brunger, Fern; Swab, Michelle; Shemilt, Ian; Kaposy, Chris

2017-02-20

There is limited information about how transgender, gender diverse, and Two-Spirit (trans) people have been represented and studied by researchers. The objectives of this study are to (1) map and describe trans research in the social sciences, sciences, humanities, health, education, and business, (2) identify evidence gaps and opportunities for more responsible research with trans people, (3) assess the use of text mining for study identification, and (4) increase access to trans research for key stakeholders through the creation of a web-based evidence map. Study design was informed by community consultations and pilot searches. Eligibility criteria were established to include all original research of any design, including trans people or their health information, and published in English in peer-reviewed journals. A complex electronic search strategy based on relevant concepts in 15 databases was developed to obtain a broad range of results linked to transgender, gender diverse, and Two-Spirit individuals and communities. Searches conducted in early 2015 resulted in 25,242 references after removal of duplicates. Based on the number of references, resources, and an objective to capture upwards of 90% of the existing literature, this study is a good candidate for text mining using Latent Dirichlet Allocation to improve efficiency of the screening process. The following information will be collected for evidence mapping: study topic, study design, methods and data sources, recruitment strategies, sample size, sample demographics, researcher name and affiliation, country where research was conducted, funding source, and year of publication. The proposed research incorporates an extensive search strategy, text mining, and evidence map; it therefore has the potential to build on knowledge in several fields. Review results will increase awareness of existing trans research, identify evidence gaps, and inform strategic research prioritization. Publishing the map online will improve access to research for key stakeholders including community members, policy makers, and healthcare providers. This study will also contribute to knowledge in the area of text mining for study identification by providing an example of how semi-automation performs for screening on title and abstract and on full text.

Annotating images by mining image search results.

PubMed

Wang, Xin-Jing; Zhang, Lei; Li, Xirong; Ma, Wei-Ying

2008-11-01

Although it has been studied for years by the computer vision and machine learning communities, image annotation is still far from practical. In this paper, we propose a novel attempt at model-free image annotation, which is a data-driven approach that annotates images by mining their search results. Some 2.4 million images with their surrounding text are collected from a few photo forums to support this approach. The entire process is formulated in a divide-and-conquer framework where a query keyword is provided along with the uncaptioned image to improve both the effectiveness and efficiency. This is helpful when the collected data set is not dense everywhere. In this sense, our approach contains three steps: 1) the search process to discover visually and semantically similar search results, 2) the mining process to identify salient terms from textual descriptions of the search results, and 3) the annotation rejection process to filter out noisy terms yielded by Step 2. To ensure real-time annotation, two key techniques are leveraged-one is to map the high-dimensional image visual features into hash codes, the other is to implement it as a distributed system, of which the search and mining processes are provided as Web services. As a typical result, the entire process finishes in less than 1 second. Since no training data set is required, our approach enables annotating with unlimited vocabulary and is highly scalable and robust to outliers. Experimental results on both real Web images and a benchmark image data set show the effectiveness and efficiency of the proposed algorithm. It is also worth noting that, although the entire approach is illustrated within the divide-and conquer framework, a query keyword is not crucial to our current implementation. We provide experimental results to prove this.

Improving the Method of Roof Fall Susceptibility Assessment based on Fuzzy Approach

NASA Astrophysics Data System (ADS)

Ghasemi, Ebrahim; Ataei, Mohammad; Shahriar, Kourosh

2017-03-01

Retreat mining is always accompanied by a great amount of accidents and most of them are due to roof fall. Therefore, development of methodologies to evaluate the roof fall susceptibility (RFS) seems essential. Ghasemi et al. (2012) proposed a systematic methodology to assess the roof fall risk during retreat mining based on risk assessment classic approach. The main defect of this method is ignorance of subjective uncertainties due to linguistic input value of some factors, low resolution, fixed weighting, sharp class boundaries, etc. To remove this defection and improve the mentioned method, in this paper, a novel methodology is presented to assess the RFS using fuzzy approach. The application of fuzzy approach provides an effective tool to handle the subjective uncertainties. Furthermore, fuzzy analytical hierarchy process (AHP) is used to structure and prioritize various risk factors and sub-factors during development of this method. This methodology is applied to identify the susceptibility of roof fall occurrence in main panel of Tabas Central Mine (TCM), Iran. The results indicate that this methodology is effective and efficient in assessing RFS.

ChemicalTagger: A tool for semantic text-mining in chemistry

PubMed Central

2011-01-01

Background The primary method for scientific communication is in the form of published scientific articles and theses which use natural language combined with domain-specific terminology. As such, they contain free owing unstructured text. Given the usefulness of data extraction from unstructured literature, we aim to show how this can be achieved for the discipline of chemistry. The highly formulaic style of writing most chemists adopt make their contributions well suited to high-throughput Natural Language Processing (NLP) approaches. Results We have developed the ChemicalTagger parser as a medium-depth, phrase-based semantic NLP tool for the language of chemical experiments. Tagging is based on a modular architecture and uses a combination of OSCAR, domain-specific regex and English taggers to identify parts-of-speech. The ANTLR grammar is used to structure this into tree-based phrases. Using a metric that allows for overlapping annotations, we achieved machine-annotator agreements of 88.9% for phrase recognition and 91.9% for phrase-type identification (Action names). Conclusions It is possible parse to chemical experimental text using rule-based techniques in conjunction with a formal grammar parser. ChemicalTagger has been deployed for over 10,000 patents and has identified solvents from their linguistic context with >99.5% precision. PMID:21575201

Benefits of off-campus education for students in the health sciences: a text-mining analysis.

PubMed

Nakagawa, Kazumasa; Asakawa, Yasuyoshi; Yamada, Keiko; Ushikubo, Mitsuko; Yoshida, Tohru; Yamaguchi, Haruyasu

2012-08-28

In Japan, few community-based approaches have been adopted in health-care professional education, and the appropriate content for such approaches has not been clarified. In establishing community-based education for health-care professionals, clarification of its learning effects is required. A community-based educational program was started in 2009 in the health sciences course at Gunma University, and one of the main elements in this program is conducting classes outside school. The purpose of this study was to investigate using text-analysis methods how the off-campus program affects students. In all, 116 self-assessment worksheets submitted by students after participating in the off-campus classes were decomposed into words. The extracted words were carefully selected from the perspective of contained meaning or content. With the selected terms, the relations to each word were analyzed by means of cluster analysis. Cluster analysis was used to select and divide 32 extracted words into four clusters: cluster 1-"actually/direct," "learn/watch/hear," "how," "experience/participation," "local residents," "atmosphere in community-based clinical care settings," "favorable," "communication/conversation," and "study"; cluster 2-"work of staff member" and "role"; cluster 3-"interaction/communication," "understanding," "feel," "significant/important/necessity," and "think"; and cluster 4-"community," "confusing," "enjoyable," "proactive," "knowledge," "academic knowledge," and "class." The students who participated in the program achieved different types of learning through the off-campus classes. They also had a positive impression of the community-based experience and interaction with the local residents, which is considered a favorable outcome. Off-campus programs could be a useful educational approach for students in health sciences.

Application of text mining in the biomedical domain.

PubMed

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.

Integrating geological uncertainty in long-term open pit mine production planning by ant colony optimization

NASA Astrophysics Data System (ADS)

Gilani, Seyed-Omid; Sattarvand, Javad

2016-02-01

Meeting production targets in terms of ore quantity and quality is critical for a successful mining operation. In-situ grade uncertainty causes both deviations from production targets and general financial deficits. A new stochastic optimization algorithm based on ant colony optimization (ACO) approach is developed herein to integrate geological uncertainty described through a series of the simulated ore bodies. Two different strategies were developed based on a single predefined probability value (Prob) and multiple probability values (Pro bnt) , respectively in order to improve the initial solutions that created by deterministic ACO procedure. Application at the Sungun copper mine in the northwest of Iran demonstrate the abilities of the stochastic approach to create a single schedule and control the risk of deviating from production targets over time and also increase the project value. A comparison between two strategies and traditional approach illustrates that the multiple probability strategy is able to produce better schedules, however, the single predefined probability is more practical in projects requiring of high flexibility degree.

The BEL information extraction workflow (BELIEF): evaluation in the BioCreative V BEL and IAT track

PubMed Central

Madan, Sumit; Hodapp, Sven; Senger, Philipp; Ansari, Sam; Szostak, Justyna; Hoeng, Julia; Peitsch, Manuel; Fluck, Juliane

2016-01-01

Network-based approaches have become extremely important in systems biology to achieve a better understanding of biological mechanisms. For network representation, the Biological Expression Language (BEL) is well designed to collate findings from the scientific literature into biological network models. To facilitate encoding and biocuration of such findings in BEL, a BEL Information Extraction Workflow (BELIEF) was developed. BELIEF provides a web-based curation interface, the BELIEF Dashboard, that incorporates text mining techniques to support the biocurator in the generation of BEL networks. The underlying UIMA-based text mining pipeline (BELIEF Pipeline) uses several named entity recognition processes and relationship extraction methods to detect concepts and BEL relationships in literature. The BELIEF Dashboard allows easy curation of the automatically generated BEL statements and their context annotations. Resulting BEL statements and their context annotations can be syntactically and semantically verified to ensure consistency in the BEL network. In summary, the workflow supports experts in different stages of systems biology network building. Based on the BioCreative V BEL track evaluation, we show that the BELIEF Pipeline automatically extracts relationships with an F-score of 36.4% and fully correct statements can be obtained with an F-score of 30.8%. Participation in the BioCreative V Interactive task (IAT) track with BELIEF revealed a systems usability scale (SUS) of 67. Considering the complexity of the task for new users—learning BEL, working with a completely new interface, and performing complex curation—a score so close to the overall SUS average highlights the usability of BELIEF. Database URL: BELIEF is available at http://www.scaiview.com/belief/ PMID:27694210

The BEL information extraction workflow (BELIEF): evaluation in the BioCreative V BEL and IAT track.

PubMed

Madan, Sumit; Hodapp, Sven; Senger, Philipp; Ansari, Sam; Szostak, Justyna; Hoeng, Julia; Peitsch, Manuel; Fluck, Juliane

2016-01-01

Network-based approaches have become extremely important in systems biology to achieve a better understanding of biological mechanisms. For network representation, the Biological Expression Language (BEL) is well designed to collate findings from the scientific literature into biological network models. To facilitate encoding and biocuration of such findings in BEL, a BEL Information Extraction Workflow (BELIEF) was developed. BELIEF provides a web-based curation interface, the BELIEF Dashboard, that incorporates text mining techniques to support the biocurator in the generation of BEL networks. The underlying UIMA-based text mining pipeline (BELIEF Pipeline) uses several named entity recognition processes and relationship extraction methods to detect concepts and BEL relationships in literature. The BELIEF Dashboard allows easy curation of the automatically generated BEL statements and their context annotations. Resulting BEL statements and their context annotations can be syntactically and semantically verified to ensure consistency in the BEL network. In summary, the workflow supports experts in different stages of systems biology network building. Based on the BioCreative V BEL track evaluation, we show that the BELIEF Pipeline automatically extracts relationships with an F-score of 36.4% and fully correct statements can be obtained with an F-score of 30.8%. Participation in the BioCreative V Interactive task (IAT) track with BELIEF revealed a systems usability scale (SUS) of 67. Considering the complexity of the task for new users-learning BEL, working with a completely new interface, and performing complex curation-a score so close to the overall SUS average highlights the usability of BELIEF.Database URL: BELIEF is available at http://www.scaiview.com/belief/. © The Author(s) 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Text Mining of UU-ITE Implementation in Indonesia

NASA Astrophysics Data System (ADS)

Hakim, Lukmanul; Kusumasari, Tien F.; Lubis, Muharman

2018-04-01

At present, social media and networks act as one of the main platforms for sharing information, idea, thought and opinions. Many people share their knowledge and express their views on the specific topics or current hot issues that interest them. The social media texts have rich information about the complaints, comments, recommendation and suggestion as the automatic reaction or respond to government initiative or policy in order to overcome certain issues.This study examines the sentiment from netizensas part of citizen who has vocal sound about the implementation of UU ITE as the first cyberlaw in Indonesia as a means to identify the current tendency of citizen perception. To perform text mining techniques, this study used Twitter Rest API while R programming was utilized for the purpose of classification analysis based on hierarchical cluster.

Biomedical data mining in clinical routine: expanding the impact of hospital information systems.

PubMed

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.

Integrated approach of environmental impact and risk assessment of Rosia Montana Mining Area, Romania.

PubMed

Stefănescu, Lucrina; Robu, Brînduşa Mihaela; Ozunu, Alexandru

2013-11-01

The environmental impact assessment of mining sites represents nowadays a large interest topic in Romania. Historical pollution in the Rosia Montana mining area of Romania caused extensive damage to environmental media. This paper has two goals: to investigate the environmental pollution induced by mining activities in the Rosia Montana area and to quantify the environmental impacts and associated risks by means of an integrated approach. Thus, a new method was developed and applied for quantifying the impact of mining activities, taking account of the quality of environmental media in the mining area, and used as case study in the present paper. The associated risks are a function of the environmental impacts and the probability of their occurrence. The results show that the environmental impacts and quantified risks, based on quality indicators to characterize the environmental quality, are of a higher order, and thus measures for pollution remediation and control need to be considered in the investigated area. The conclusion drawn is that an integrated approach for the assessment of environmental impact and associated risks is a valuable and more objective method, and is an important tool that can be applied in the decision-making process for national authorities in the prioritization of emergency action.

The Islamic State Battle Plan: Press Release Natural Language Processing

DTIC Science & Technology

2016-06-01

Processing, text mining , corpus, generalized linear model, cascade, R Shiny, leaflet, data visualization 15. NUMBER OF PAGES 83 16. PRICE CODE...Terrorism and Responses to Terrorism TDM Term Document Matrix TF Term Frequency TF-IDF Term Frequency-Inverse Document Frequency tm text mining (R...package=leaflet. Feinerer I, Hornik K (2015) Text Mining Package “tm,” Version 0.6-2. (Jul 3) https://cran.r-project.org/web/packages/tm/tm.pdf

The ACODEA Framework: Developing Segmentation and Classification Schemes for Fully Automatic Analysis of Online Discussions

ERIC Educational Resources Information Center

Mu, Jin; Stegmann, Karsten; Mayfield, Elijah; Rose, Carolyn; Fischer, Frank

2012-01-01

Research related to online discussions frequently faces the problem of analyzing huge corpora. Natural Language Processing (NLP) technologies may allow automating this analysis. However, the state-of-the-art in machine learning and text mining approaches yields models that do not transfer well between corpora related to different topics. Also,…

Enhancing a Core Journal Collection for Digital Libraries

ERIC Educational Resources Information Center

Kovacevic, Ana; Devedzic, Vladan; Pocajt, Viktor

2010-01-01

Purpose: This paper aims to address the problem of enhancing the selection of titles offered by a digital library, by analysing the differences in these titles when they are cited by local authors in their publications and when they are listed in the digital library offer. Design/methodology/approach: Text mining techniques were used to identify…

Cataloging the biomedical world of pain through semi-automated curation of molecular interactions

PubMed Central

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

Cataloging the biomedical world of pain through semi-automated curation of molecular interactions.

PubMed

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: •••

What Online Communities Can Tell Us About Electronic Cigarettes and Hookah Use: A Study Using Text Mining and Visualization Techniques

PubMed Central

Zhu, Shu-Hong; Conway, Mike

2015-01-01

Background The rise in popularity of electronic cigarettes (e-cigarettes) and hookah over recent years has been accompanied by some confusion and uncertainty regarding the development of an appropriate regulatory response towards these emerging products. Mining online discussion content can lead to insights into people’s experiences, which can in turn further our knowledge of how to address potential health implications. In this work, we take a novel approach to understanding the use and appeal of these emerging products by applying text mining techniques to compare consumer experiences across discussion forums. Objective This study examined content from the websites Vapor Talk, Hookah Forum, and Reddit to understand people’s experiences with different tobacco products. Our investigation involves three parts. First, we identified contextual factors that inform our understanding of tobacco use behaviors, such as setting, time, social relationships, and sensory experience, and compared the forums to identify the ones where content on these factors is most common. Second, we compared how the tobacco use experience differs with combustible cigarettes and e-cigarettes. Third, we investigated differences between e-cigarette and hookah use. Methods In the first part of our study, we employed a lexicon-based extraction approach to estimate prevalence of contextual factors, and then we generated a heat map based on these estimates to compare the forums. In the second and third parts of the study, we employed a text mining technique called topic modeling to identify important topics and then developed a visualization, Topic Bars, to compare topic coverage across forums. Results In the first part of the study, we identified two forums, Vapor Talk Health & Safety and the Stopsmoking subreddit, where discussion concerning contextual factors was particularly common. The second part showed that the discussion in Vapor Talk Health & Safety focused on symptoms and comparisons of combustible cigarettes and e-cigarettes, and the Stopsmoking subreddit focused on psychological aspects of quitting. Last, we examined the discussion content on Vapor Talk and Hookah Forum. Prominent topics included equipment, technique, experiential elements of use, and the buying and selling of equipment. Conclusions This study has three main contributions. Discussion forums differ in the extent to which their content may help us understand behaviors with potential health implications. Identifying dimensions of interest and using a heat map visualization to compare across forums can be helpful for identifying forums with the greatest density of health information. Additionally, our work has shown that the quitting experience can potentially be very different depending on whether or not e-cigarettes are used. Finally, e-cigarette and hookah forums are similar in that members represent a “hobbyist culture” that actively engages in information exchange. These differences have important implications for both tobacco regulation and smoking cessation intervention design. PMID:26420469

Combined mining: discovering informative knowledge in complex data.

PubMed

Cao, Longbing; Zhang, Huaifeng; Zhao, Yanchang; Luo, Dan; Zhang, Chengqi

2011-06-01

Enterprise data mining applications often involve complex data such as multiple large heterogeneous data sources, user preferences, and business impact. In such situations, a single method or one-step mining is often limited in discovering informative knowledge. It would also be very time and space consuming, if not impossible, to join relevant large data sources for mining patterns consisting of multiple aspects of information. It is crucial to develop effective approaches for mining patterns combining necessary information from multiple relevant business lines, catering for real business settings and decision-making actions rather than just providing a single line of patterns. The recent years have seen increasing efforts on mining more informative patterns, e.g., integrating frequent pattern mining with classifications to generate frequent pattern-based classifiers. Rather than presenting a specific algorithm, this paper builds on our existing works and proposes combined mining as a general approach to mining for informative patterns combining components from either multiple data sets or multiple features or by multiple methods on demand. We summarize general frameworks, paradigms, and basic processes for multifeature combined mining, multisource combined mining, and multimethod combined mining. Novel types of combined patterns, such as incremental cluster patterns, can result from such frameworks, which cannot be directly produced by the existing methods. A set of real-world case studies has been conducted to test the frameworks, with some of them briefed in this paper. They identify combined patterns for informing government debt prevention and improving government service objectives, which show the flexibility and instantiation capability of combined mining in discovering informative knowledge in complex data.

caTIES: a grid based system for coding and retrieval of surgical pathology reports and tissue specimens in support of translational research.

PubMed

Crowley, Rebecca S; Castine, Melissa; Mitchell, Kevin; Chavan, Girish; McSherry, Tara; Feldman, Michael

2010-01-01

The authors report on the development of the Cancer Tissue Information Extraction System (caTIES)--an application that supports collaborative tissue banking and text mining by leveraging existing natural language processing methods and algorithms, grid communication and security frameworks, and query visualization methods. The system fills an important need for text-derived clinical data in translational research such as tissue-banking and clinical trials. The design of caTIES addresses three critical issues for informatics support of translational research: (1) federation of research data sources derived from clinical systems; (2) expressive graphical interfaces for concept-based text mining; and (3) regulatory and security model for supporting multi-center collaborative research. Implementation of the system at several Cancer Centers across the country is creating a potential network of caTIES repositories that could provide millions of de-identified clinical reports to users. The system provides an end-to-end application of medical natural language processing to support multi-institutional translational research programs.

Automatically classifying sentences in full-text biomedical articles into Introduction, Methods, Results and Discussion.

PubMed

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/.

Design risk assessment for burst-prone mines: Application in a Canadian mine

NASA Astrophysics Data System (ADS)

Cheung, David J.

A proactive stance towards improving the effectiveness and consistency of risk assessments has been adopted recently by mining companies and industry. The next 10-20 years forecasts that ore deposits accessible using shallow mining techniques will diminish. The industry continues to strive for success in "deeper" mining projects in order to keep up with the continuing demand for raw materials. Although the returns are quite profitable, many projects have been sidelined due to high uncertainty and technical risk in the mining of the mineral deposit. Several hardrock mines have faced rockbursting and seismicity problems. Within those reported, mines in countries like South Africa, Australia and Canada have documented cases of severe rockburst conditions attributed to the mining depth. Severe rockburst conditions known as "burst-prone" can be effectively managed with design. Adopting a more robust design can ameliorate the exposure of workers and equipment to adverse conditions and minimize the economic consequences, which can hinder the bottom line of an operation. This thesis presents a methodology created for assessing the design risk in burst-prone mines. The methodology includes an evaluation of relative risk ratings for scenarios with options of risk reduction through several design principles. With rockbursts being a hazard of seismic events, the methodology is based on research in the area of mining seismicity factoring in rockmass failure mechanisms, which results from a combination of mining induced stress, geological structures, rockmass properties and mining influences. The methodology was applied to case studies at Craig Mine of Xstrata Nickel in Sudbury, Ontario, which is known to contain seismically active fault zones. A customized risk assessment was created and applied to rockburst case studies, evaluating the seismic vulnerability and consequence for each case. Application of the methodology to Craig Mine demonstrates that changes in the design can reduce both exposure risk (personnel and equipment), and economical risk (revenue and costs). Fatal and catastrophic consequences can be averted through robust planning and design. Two customized approaches were developed to conduct risk assessment of case studies at Craig Mine. Firstly, the Brownfield Approach utilizes the seismic database to determine the seismic hazard from a rating system that evaluates frequency-magnitude, event size, and event-blast relation. Secondly, the Greenfield Approach utilizes the seismic database, focusing on larger magnitude events, rocktype, and geological structure. The customized Greenfield Approach can also be applied in the evaluation of design risk in deep mines with the same setting and condition as Craig Mine. Other mines with different settings and conditions can apply the principles in the methodology to evaluate design alternatives and risk reduction strategies for burst-prone mines.

Biogeochemical behaviour and bioremediation of uranium in waters of abandoned mines.

PubMed

Mkandawire, Martin

2013-11-01

The discharges of uranium and associated radionuclides as well as heavy metals and metalloids from waste and tailing dumps in abandoned uranium mining and processing sites pose contamination risks to surface and groundwater. Although many more are being planned for nuclear energy purposes, most of the abandoned uranium mines are a legacy of uranium production that fuelled arms race during the cold war of the last century. Since the end of cold war, there have been efforts to rehabilitate the mining sites, initially, using classical remediation techniques based on high chemical and civil engineering. Recently, bioremediation technology has been sought as alternatives to the classical approach due to reasons, which include: (a) high demand of sites requiring remediation; (b) the economic implication of running and maintaining the facilities due to high energy and work force demand; and (c) the pattern and characteristics of contaminant discharges in most of the former uranium mining and processing sites prevents the use of classical methods. This review discusses risks of uranium contamination from abandoned uranium mines from the biogeochemical point of view and the potential and limitation of uranium bioremediation technique as alternative to classical approach in abandoned uranium mining and processing sites.

On the unsupervised analysis of domain-specific Chinese texts

PubMed Central

Deng, Ke; Bol, Peter K.; Li, Kate J.; Liu, Jun S.

2016-01-01

With the growing availability of digitized text data both publicly and privately, there is a great need for effective computational tools to automatically extract information from texts. Because the Chinese language differs most significantly from alphabet-based languages in not specifying word boundaries, most existing Chinese text-mining methods require a prespecified vocabulary and/or a large relevant training corpus, which may not be available in some applications. We introduce an unsupervised method, top-down word discovery and segmentation (TopWORDS), for simultaneously discovering and segmenting words and phrases from large volumes of unstructured Chinese texts, and propose ways to order discovered words and conduct higher-level context analyses. TopWORDS is particularly useful for mining online and domain-specific texts where the underlying vocabulary is unknown or the texts of interest differ significantly from available training corpora. When outputs from TopWORDS are fed into context analysis tools such as topic modeling, word embedding, and association pattern finding, the results are as good as or better than that from using outputs of a supervised segmentation method. PMID:27185919

Development and application of biotechnologies in the metal mining industry.

PubMed

Johnson, D Barrie

2013-11-01

Metal mining faces a number of significant economic and environmental challenges in the twenty-first century for which established and emerging biotechnologies may, at least in part, provide the answers. Bioprocessing of mineral ores and concentrates is already used in variously engineered formats to extract base (e.g., copper, cobalt, and nickel) and precious (gold and silver) metals in mines throughout the world, though it remains a niche technology. However, current projections of an increasing future need to use low-grade primary metal ores, to reprocess mine wastes, and to develop in situ leaching technologies to extract metals from deep-buried ore bodies, all of which are economically more amenable to bioprocessing than conventional approaches (e.g., pyrometallurgy), would suggest that biomining will become more extensively utilized in the future. Recent research has also shown that bioleaching could be used to process a far wider range of metal ores (e.g., oxidized ores) than has previously been the case. Biotechnologies are also being developed to control mine-related pollution, including securing mine wastes (rocks and tailings) by using "ecological engineering" approaches, and also to remediate and recover metals from waste waters, such as acid mine drainage. This article reviews the current status of biotechnologies within the mining sector and considers how these may be developed and applied in future years.

Using MSD prevention for cultural change in mining: Queensland Government/Anglo Coal Industry partnership.

PubMed

Tilbury, Trudy; Sanderson, Liz

2012-01-01

Queensland Mining has a strong focus on safety performance, but risk management of health, including Musculoskeletal Disorders (MSDs) continues to have a lower priority. The reliance on individual screening of workers and lower level approaches such as manual handling training is part of the coal mining 'culture'. Initiatives such as the New South Wales and Queensland Mining joint project to develop good practice guidance for mining has allowed for a more consistent message on participatory ergonomics and prevention of MSD. An evidence based practice approach, including the introduction of participatory ergonomics and safe design principles, was proposed to Anglo American Coal operations in Queensland. The project consisted of a skills analysis of current health personnel, design of a facilitated participatory ergonomics training program, site visits to identify good practice and champions, and a graduated mentoring program for health personnel. Early results demonstrate a number of sites are benefiting from site taskforces with a focus on positive performance outcomes.

Establishing Reliable miRNA-Cancer Association Network Based on Text-Mining Method

PubMed Central

Yang, Zhaowan; Fang, Ming; Zhang, Libin; Zhou, Yanhong

2014-01-01

Associating microRNAs (miRNAs) with cancers is an important step of understanding the mechanisms of cancer pathogenesis and finding novel biomarkers for cancer therapies. In this study, we constructed a miRNA-cancer association network (miCancerna) based on more than 1,000 miRNA-cancer associations detected from millions of abstracts with the text-mining method, including 226 miRNA families and 20 common cancers. We further prioritized cancer-related miRNAs at the network level with the random-walk algorithm, achieving a relatively higher performance than previous miRNA disease networks. Finally, we examined the top 5 candidate miRNAs for each kind of cancer and found that 71% of them are confirmed experimentally. miCancerna would be an alternative resource for the cancer-related miRNA identification. PMID:24895499

Multiagent data warehousing and multiagent data mining for cerebrum/cerebellum modeling

NASA Astrophysics Data System (ADS)

Zhang, Wen-Ran

2002-03-01

An algorithm named Neighbor-Miner is outlined for multiagent data warehousing and multiagent data mining. The algorithm is defined in an evolving dynamic environment with autonomous or semiautonomous agents. Instead of mining frequent itemsets from customer transactions, the new algorithm discovers new agents and mining agent associations in first-order logic from agent attributes and actions. While the Apriori algorithm uses frequency as a priory threshold, the new algorithm uses agent similarity as priory knowledge. The concept of agent similarity leads to the notions of agent cuboid, orthogonal multiagent data warehousing (MADWH), and multiagent data mining (MADM). Based on agent similarities and action similarities, Neighbor-Miner is proposed and illustrated in a MADWH/MADM approach to cerebrum/cerebellum modeling. It is shown that (1) semiautonomous neurofuzzy agents can be identified for uniped locomotion and gymnastic training based on attribute relevance analysis; (2) new agents can be discovered and agent cuboids can be dynamically constructed in an orthogonal MADWH, which resembles an evolving cerebrum/cerebellum system; and (3) dynamic motion laws can be discovered as association rules in first order logic. Although examples in legged robot gymnastics are used to illustrate the basic ideas, the new approach is generally suitable for a broad category of data mining tasks where knowledge can be discovered collectively by a set of agents from a geographically or geometrically distributed but relevant environment, especially in scientific and engineering data environments.

Text mining of cancer-related information: review of current status and future directions.

PubMed

Spasić, Irena; Livsey, Jacqueline; Keane, John A; Nenadić, Goran

2014-09-01

This paper reviews the research literature on text mining (TM) with the aim to find out (1) which cancer domains have been the subject of TM efforts, (2) which knowledge resources can support TM of cancer-related information and (3) to what extent systems that rely on knowledge and computational methods can convert text data into useful clinical information. These questions were used to determine the current state of the art in this particular strand of TM and suggest future directions in TM development to support cancer research. A review of the research on TM of cancer-related information was carried out. A literature search was conducted on the Medline database as well as IEEE Xplore and ACM digital libraries to address the interdisciplinary nature of such research. The search results were supplemented with the literature identified through Google Scholar. A range of studies have proven the feasibility of TM for extracting structured information from clinical narratives such as those found in pathology or radiology reports. In this article, we provide a critical overview of the current state of the art for TM related to cancer. The review highlighted a strong bias towards symbolic methods, e.g. named entity recognition (NER) based on dictionary lookup and information extraction (IE) relying on pattern matching. The F-measure of NER ranges between 80% and 90%, while that of IE for simple tasks is in the high 90s. To further improve the performance, TM approaches need to deal effectively with idiosyncrasies of the clinical sublanguage such as non-standard abbreviations as well as a high degree of spelling and grammatical errors. This requires a shift from rule-based methods to machine learning following the success of similar trends in biological applications of TM. Machine learning approaches require large training datasets, but clinical narratives are not readily available for TM research due to privacy and confidentiality concerns. This issue remains the main bottleneck for progress in this area. In addition, there is a need for a comprehensive cancer ontology that would enable semantic representation of textual information found in narrative reports. Copyright © 2014 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Text Mining for Precision Medicine: Bringing structure to EHRs and biomedical literature to understand genes and health

PubMed Central

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

Text Mining for Precision Medicine: Bringing Structure to EHRs and Biomedical Literature to Understand Genes and Health.

PubMed

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.

Open Pit Mine 3d Mapping by Tls and Digital Photogrammetry: 3d Model Update Thanks to a Slam Based Approach

NASA Astrophysics Data System (ADS)

Vassena, G.; Clerici, A.

2018-05-01

The state of the art of 3D surveying technologies, if correctly applied, allows to obtain 3D coloured models of large open pit mines using different technologies as terrestrial laser scanner (TLS), with images, combined with UAV based digital photogrammetry. GNSS and/or total station are also currently used to geo reference the model. The University of Brescia has been realised a project to map in 3D an open pit mine located in Botticino, a famous location of marble extraction close to Brescia in North Italy. Terrestrial Laser Scanner 3D point clouds combined with RGB images and digital photogrammetry from UAV have been used to map a large part of the cave. By rigorous and well know procedures a 3D point cloud and mesh model have been obtained using an easy and rigorous approach. After the description of the combined mapping process, the paper describes the innovative process proposed for the daily/weekly update of the model itself. To realize this task a SLAM technology approach is described, using an innovative approach based on an innovative instrument capable to run an automatic localization process and real time on the field change detection analysis.

Decision Tree based Prediction and Rule Induction for Groundwater Trichloroethene (TCE) Pollution Vulnerability

NASA Astrophysics Data System (ADS)

Park, J.; Yoo, K.

2013-12-01

For groundwater resource conservation, it is important to accurately assess groundwater pollution sensitivity or vulnerability. In this work, we attempted to use data mining approach to assess groundwater pollution vulnerability in a TCE (trichloroethylene) contaminated Korean industrial site. The conventional DRASTIC method failed to describe TCE sensitivity data with a poor correlation with hydrogeological properties. Among the different data mining methods such as Artificial Neural Network (ANN), Multiple Logistic Regression (MLR), Case Base Reasoning (CBR), and Decision Tree (DT), the accuracy and consistency of Decision Tree (DT) was the best. According to the following tree analyses with the optimal DT model, the failure of the conventional DRASTIC method in fitting with TCE sensitivity data may be due to the use of inaccurate weight values of hydrogeological parameters for the study site. These findings provide a proof of concept that DT based data mining approach can be used in predicting and rule induction of groundwater TCE sensitivity without pre-existing information on weights of hydrogeological properties.

Content-Based Indexing and Teaching Focus Mining for Lecture Videos

ERIC Educational Resources Information Center

Lin, Yu-Tzu; Yen, Bai-Jang; Chang, Chia-Hu; Lee, Greg C.; Lin, Yu-Chih

2010-01-01

Purpose: The purpose of this paper is to propose an indexing and teaching focus mining system for lecture videos recorded in an unconstrained environment. Design/methodology/approach: By applying the proposed algorithms in this paper, the slide structure can be reconstructed by extracting slide images from the video. Instead of applying…

Explosion and/or fire risk assessment methodology: a common approach, structured for underground coalmine environments / Metoda szacowania ryzyka wybuchu i pożarów: podejście ogólne, dostosowane do środowiska kopalni podziemnej

NASA Astrophysics Data System (ADS)

Cioca, Ionel-Lucian; Moraru, Roland Iosif

2012-10-01

In order to meet statutory requirements concerning the workers health and safety, it is necessary for mine managers within Valea Jiului coal basin in Romania to address the potential for underground fires and explosions and their impact on the workforce and the mine ventilation systems. Highlighting the need for a unified and systematic approach of the specific risks, the authors are developing a general framework for fire/explosion risk assessment in gassy mines, based on the quantification of the likelihood of occurrence and gravity of the consequences of such undesired events and employing Root-Cause analysis method. It is emphasized that even a small fire should be regarded as being a major hazard from the point of view of explosion initiation, should a combustible atmosphere arise. The developed methodology, for the assessment of underground fire and explosion risks, is based on the known underground explosion hazards, fire engineering principles and fire test criteria for potentially combustible materials employed in mines.

A New Data Representation Based on Training Data Characteristics to Extract Drug Name Entity in Medical Text

PubMed Central

Basaruddin, T.

2016-01-01

One essential task in information extraction from the medical corpus is drug name recognition. Compared with text sources come from other domains, the medical text mining poses more challenges, for example, more unstructured text, the fast growing of new terms addition, a wide range of name variation for the same drug, the lack of labeled dataset sources and external knowledge, and the multiple token representations for a single drug name. Although many approaches have been proposed to overwhelm the task, some problems remained with poor F-score performance (less than 0.75). This paper presents a new treatment in data representation techniques to overcome some of those challenges. We propose three data representation techniques based on the characteristics of word distribution and word similarities as a result of word embedding training. The first technique is evaluated with the standard NN model, that is, MLP. The second technique involves two deep network classifiers, that is, DBN and SAE. The third technique represents the sentence as a sequence that is evaluated with a recurrent NN model, that is, LSTM. In extracting the drug name entities, the third technique gives the best F-score performance compared to the state of the art, with its average F-score being 0.8645. PMID:27843447

Study of acid mine drainage management with evaluating climate and rainfall in East Pit 3 West Banko coal mine

NASA Astrophysics Data System (ADS)

Rochyani, Neny

2017-11-01

Acid mine drainage is a major problem for the mining environment. The main factor that formed acid mine drainage is the volume of rainfall. Therefore, it is important to know clearly the main climate pattern of rainfall and season on the management of acid mine drainage. This study focuses on the effects of rainfall on acid mine water management. Based on daily rainfall data, monthly and seasonal patterns by using Gumbel approach is known the amount of rainfall that occurred in East Pit 3 West Banko area. The data also obtained the highest maximum daily rainfall on 165 mm/day and the lowest at 76.4 mm/day, where it is known that the rainfall conditions during the period 2007 - 2016 is from November to April so the use of lime is also slightly, While the low rainfall is from May to October and the use of lime will be more and more. Based on calculation of lime requirement for each return period, it can be seen the total of lime and financial requirement for treatment of each return period.

A system for classifying disease comorbidity status from medical discharge summaries using automated hotspot and negated concept detection.

PubMed

Ambert, Kyle H; Cohen, Aaron M

2009-01-01

OBJECTIVE Free-text clinical reports serve as an important part of patient care management and clinical documentation of patient disease and treatment status. Free-text notes are commonplace in medical practice, but remain an under-used source of information for clinical and epidemiological research, as well as personalized medicine. The authors explore the challenges associated with automatically extracting information from clinical reports using their submission to the Integrating Informatics with Biology and the Bedside (i2b2) 2008 Natural Language Processing Obesity Challenge Task. DESIGN A text mining system for classifying patient comorbidity status, based on the information contained in clinical reports. The approach of the authors incorporates a variety of automated techniques, including hot-spot filtering, negated concept identification, zero-vector filtering, weighting by inverse class-frequency, and error-correcting of output codes with linear support vector machines. MEASUREMENTS Performance was evaluated in terms of the macroaveraged F1 measure. RESULTS The automated system performed well against manual expert rule-based systems, finishing fifth in the Challenge's intuitive task, and 13(th) in the textual task. CONCLUSIONS The system demonstrates that effective comorbidity status classification by an automated system is possible.

Using Text Mining to Uncover Students' Technology-Related Problems in Live Video Streaming

ERIC Educational Resources Information Center

Abdous, M'hammed; He, Wu

2011-01-01

Because of their capacity to sift through large amounts of data, text mining and data mining are enabling higher education institutions to reveal valuable patterns in students' learning behaviours without having to resort to traditional survey methods. In an effort to uncover live video streaming (LVS) students' technology related-problems and to…

The Feasibility of Using Large-Scale Text Mining to Detect Adverse Childhood Experiences in a VA-Treated Population.

PubMed

Hammond, Kenric W; Ben-Ari, Alon Y; Laundry, Ryan J; Boyko, Edward J; Samore, Matthew H

2015-12-01

Free text in electronic health records resists large-scale analysis. Text records facts of interest not found in encoded data, and text mining enables their retrieval and quantification. The U.S. Department of Veterans Affairs (VA) clinical data repository affords an opportunity to apply text-mining methodology to study clinical questions in large populations. To assess the feasibility of text mining, investigation of the relationship between exposure to adverse childhood experiences (ACEs) and recorded diagnoses was conducted among all VA-treated Gulf war veterans, utilizing all progress notes recorded from 2000-2011. Text processing extracted ACE exposures recorded among 44.7 million clinical notes belonging to 243,973 veterans. The relationship of ACE exposure to adult illnesses was analyzed using logistic regression. Bias considerations were assessed. ACE score was strongly associated with suicide attempts and serious mental disorders (ORs = 1.84 to 1.97), and less so with behaviorally mediated and somatic conditions (ORs = 1.02 to 1.36) per unit. Bias adjustments did not remove persistent associations between ACE score and most illnesses. Text mining to detect ACE exposure in a large population was feasible. Analysis of the relationship between ACE score and adult health conditions yielded patterns of association consistent with prior research. Copyright © 2015 International Society for Traumatic Stress Studies.

Online discourse on fibromyalgia: text-mining to identify clinical distinction and patient concerns.

PubMed

Park, Jungsik; Ryu, Young Uk

2014-10-07

The purpose of this study was to evaluate the possibility of using text-mining to identify clinical distinctions and patient concerns in online memoires posted by patients with fibromyalgia (FM). A total of 399 memoirs were collected from an FM group website. The unstructured data of memoirs associated with FM were collected through a crawling process and converted into structured data with a concordance, parts of speech tagging, and word frequency. We also conducted a lexical analysis and phrase pattern identification. After examining the data, a set of FM-related keywords were obtained and phrase net relationships were set through a web-based visualization tool. The clinical distinction of FM was verified. Pain is the biggest issue to the FM patients. The pains were affecting body parts including 'muscles,' 'leg,' 'neck,' 'back,' 'joints,' and 'shoulders' with accompanying symptoms such as 'spasms,' 'stiffness,' and 'aching,' and were described as 'sever,' 'chronic,' and 'constant.' This study also demonstrated that it was possible to understand the interests and concerns of FM patients through text-mining. FM patients wanted to escape from the pain and symptoms, so they were interested in medical treatment and help. Also, they seemed to have interest in their work and occupation, and hope to continue to live life through the relationships with the people around them. This research shows the potential for extracting keywords to confirm the clinical distinction of a certain disease, and text-mining can help objectively understand the concerns of patients by generalizing their large number of subjective illness experiences. However, it is believed that there are limitations to the processes and methods for organizing and classifying large amounts of text, so these limits have to be considered when analyzing the results. The development of research methodology to overcome these limitations is greatly needed.

Efficient chemical-disease identification and relationship extraction using Wikipedia to improve recall

PubMed Central

Lowe, Daniel M.; O’Boyle, Noel M.; Sayle, Roger A.

2016-01-01

Awareness of the adverse effects of chemicals is important in biomedical research and healthcare. Text mining can allow timely and low-cost extraction of this knowledge from the biomedical literature. We extended our text mining solution, LeadMine, to identify diseases and chemical-induced disease relationships (CIDs). LeadMine is a dictionary/grammar-based entity recognizer and was used to recognize and normalize both chemicals and diseases to Medical Subject Headings (MeSH) IDs. The disease lexicon was obtained from three sources: MeSH, the Disease Ontology and Wikipedia. The Wikipedia dictionary was derived from pages with a disease/symptom box, or those where the page title appeared in the lexicon. Composite entities (e.g. heart and lung disease) were detected and mapped to their composite MeSH IDs. For CIDs, we developed a simple pattern-based system to find relationships within the same sentence. Our system was evaluated in the BioCreative V Chemical–Disease Relation task and achieved very good results for both disease concept ID recognition (F1-score: 86.12%) and CIDs (F1-score: 52.20%) on the test set. As our system was over an order of magnitude faster than other solutions evaluated on the task, we were able to apply the same system to the entirety of MEDLINE allowing us to extract a collection of over 250 000 distinct CIDs. PMID:27060160

Text mining and its potential applications in systems biology.

PubMed

Ananiadou, Sophia; Kell, Douglas B; Tsujii, Jun-ichi

2006-12-01

With biomedical literature increasing at a rate of several thousand papers per week, it is impossible to keep abreast of all developments; therefore, automated means to manage the information overload are required. Text mining techniques, which involve the processes of information retrieval, information extraction and data mining, provide a means of solving this. By adding meaning to text, these techniques produce a more structured analysis of textual knowledge than simple word searches, and can provide powerful tools for the production and analysis of systems biology models.

A watershed-scale approach to tracing metal contamination in the environment

USGS Publications Warehouse

Church, Stanley E

1996-01-01

IntroductionPublic policy during the 1800's encouraged mining in the western United States. Mining on Federal lands played an important role in the growing economy creating national wealth from our abundant and diverse mineral resource base. The common industrial practice from the early days of mining through about 1970 in the U.S. was for mine operators to dispose of the mine wastes and mill tailings in the nearest stream reach or lake. As a result of this contamination, many stream reaches below old mines, mills, and mining districts and some major rivers and lakes no longer support aquatic life. Riparian habitats within these affected watersheds have also been impacted. Often, the water from these affected stream reaches is generally not suitable for drinking, creating a public health hazard. The recent Department of Interior Abandoned Mine Lands (AML) Initiative is an effort on the part of the Federal Government to address the adverse environmental impact of these past mining practices on Federal lands. The AML Initiative has adopted a watershed approach to determine those sites that contribute the majority of the contaminants in the watershed. By remediating the largest sources of contamination within the watershed, the impact of metal contamination in the environment within the watershed as a whole is reduced rather than focusing largely on those sites for which principal responsible parties can be found.The scope of the problem of metal contamination in the environment from past mining practices in the coterminous U.S. is addressed in a recent report by Ferderer (1996). Using the USGS1:2,000,000-scale hydrologic drainage basin boundaries and the USGS Minerals Availability System (MAS) data base, he plotted the distribution of 48,000 past-producing metal mines on maps showing the boundaries of lands administered by the various Federal Land Management Agencies (FLMA). Census analysis of these data provided an initial screening tool for prioritization of watersheds in the western U.S. A different approach to the scope of the abandoned mine problem (Church et al., 1996a) is shown by the water quality data collected by the States under the Clean Water Act, section 305(b). These data document the stream reaches affected by metals from naturally occurring sources as well as from mining, or mineral resource extraction. Permitted discharges from active industrial and mine sites are not covered in the 305(b) data base.Local citizens and state and federal agencies are all part of the collaborative decision process used to select the drainage basins chosen for the AML Initiative pilot studies. Data gathered by these three entities were brought to bear on the watershed selection process. The USGS prepared data available from Federal data bases in the form of interpretative GIS products. Maps of the states of Colorado (Plumlee et al., 1995) and a similar study of the state of Montana (USGS, unpublished data) were used to select the Animas watershed in southwestern Colorado and the Boulder watershed southwest of Helena Montana as the pilot study areas for the AML Initiative. Thus, the watersheds selected for study were public decisions made on the basis of available scientific data. The role of the U.S. Geological Survey in the Abandoned Mine Land Initiative is outlined in Buxton et al. (1997).The watershed approach to metals contamination in the environment has been studied in several drainage basins (Church et al., 1993, 1994, 1995, 1996b; Kimball et al., 1995). The underlying principles used to successfully discriminate between sources and to quantify the impact of these sources on the environment are the subject of this report.

Literature mining of protein-residue associations with graph rules learned through distant supervision.

PubMed

Ravikumar, Ke; Liu, Haibin; Cohn, Judith D; Wall, Michael E; Verspoor, Karin

2012-10-05

We propose a method for automatic extraction of protein-specific residue mentions from the biomedical literature. The method searches text for mentions of amino acids at specific sequence positions and attempts to correctly associate each mention with a protein also named in the text. The methods presented in this work will enable improved protein functional site extraction from articles, ultimately supporting protein function prediction. Our method made use of linguistic patterns for identifying the amino acid residue mentions in text. Further, we applied an automated graph-based method to learn syntactic patterns corresponding to protein-residue pairs mentioned in the text. We finally present an approach to automated construction of relevant training and test data using the distant supervision model. The performance of the method was assessed by extracting protein-residue relations from a new automatically generated test set of sentences containing high confidence examples found using distant supervision. It achieved a F-measure of 0.84 on automatically created silver corpus and 0.79 on a manually annotated gold data set for this task, outperforming previous methods. The primary contributions of this work are to (1) demonstrate the effectiveness of distant supervision for automatic creation of training data for protein-residue relation extraction, substantially reducing the effort and time involved in manual annotation of a data set and (2) show that the graph-based relation extraction approach we used generalizes well to the problem of protein-residue association extraction. This work paves the way towards effective extraction of protein functional residues from the literature.

PubMedMiner: Mining and Visualizing MeSH-based Associations in PubMed.

PubMed

Zhang, Yucan; Sarkar, Indra Neil; Chen, Elizabeth S

2014-01-01

The exponential growth of biomedical literature provides the opportunity to develop approaches for facilitating the identification of possible relationships between biomedical concepts. Indexing by Medical Subject Headings (MeSH) represent high-quality summaries of much of this literature that can be used to support hypothesis generation and knowledge discovery tasks using techniques such as association rule mining. Based on a survey of literature mining tools, a tool implemented using Ruby and R - PubMedMiner - was developed in this study for mining and visualizing MeSH-based associations for a set of MEDLINE articles. To demonstrate PubMedMiner's functionality, a case study was conducted that focused on identifying and comparing comorbidities for asthma in children and adults. Relative to the tools surveyed, the initial results suggest that PubMedMiner provides complementary functionality for summarizing and comparing topics as well as identifying potentially new knowledge.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shumway, R.H.; McQuarrie, A.D.

Robust statistical approaches to the problem of discriminating between regional earthquakes and explosions are developed. We compare linear discriminant analysis using descriptive features like amplitude and spectral ratios with signal discrimination techniques using the original signal waveforms and spectral approximations to the log likelihood function. Robust information theoretic techniques are proposed and all methods are applied to 8 earthquakes and 8 mining explosions in Scandinavia and to an event from Novaya Zemlya of unknown origin. It is noted that signal discrimination approaches based on discrimination information and Renyi entropy perform better in the test sample than conventional methods based onmore » spectral ratios involving the P and S phases. Two techniques for identifying the ripple-firing pattern for typical mining explosions are proposed and shown to work well on simulated data and on several Scandinavian earthquakes and explosions. We use both cepstral analysis in the frequency domain and a time domain method based on the autocorrelation and partial autocorrelation functions. The proposed approach strips off underlying smooth spectral and seasonal spectral components corresponding to the echo pattern induced by two simple ripple-fired models. For two mining explosions, a pattern is identified whereas for two earthquakes, no pattern is evident.« less

U-Compare: share and compare text mining tools with UIMA

PubMed Central

Kano, Yoshinobu; Baumgartner, William A.; McCrohon, Luke; Ananiadou, Sophia; Cohen, K. Bretonnel; Hunter, Lawrence; Tsujii, Jun'ichi

2009-01-01

Summary: Due to the increasing number of text mining resources (tools and corpora) available to biologists, interoperability issues between these resources are becoming significant obstacles to using them effectively. UIMA, the Unstructured Information Management Architecture, is an open framework designed to aid in the construction of more interoperable tools. U-Compare is built on top of the UIMA framework, and provides both a concrete framework for out-of-the-box text mining and a sophisticated evaluation platform allowing users to run specific tools on any target text, generating both detailed statistics and instance-based visualizations of outputs. U-Compare is a joint project, providing the world's largest, and still growing, collection of UIMA-compatible resources. These resources, originally developed by different groups for a variety of domains, include many famous tools and corpora. U-Compare can be launched straight from the web, without needing to be manually installed. All U-Compare components are provided ready-to-use and can be combined easily via a drag-and-drop interface without any programming. External UIMA components can also simply be mixed with U-Compare components, without distinguishing between locally and remotely deployed resources. Availability: http://u-compare.org/ Contact: kano@is.s.u-tokyo.ac.jp PMID:19414535

BioCreative V track 4: a shared task for the extraction of causal network information using the Biological Expression Language.

PubMed

Rinaldi, Fabio; Ellendorff, Tilia Renate; Madan, Sumit; Clematide, Simon; van der Lek, Adrian; Mevissen, Theo; Fluck, Juliane

2016-01-01

Automatic extraction of biological network information is one of the most desired and most complex tasks in biological and medical text mining. Track 4 at BioCreative V attempts to approach this complexity using fragments of large-scale manually curated biological networks, represented in Biological Expression Language (BEL), as training and test data. BEL is an advanced knowledge representation format which has been designed to be both human readable and machine processable. The specific goal of track 4 was to evaluate text mining systems capable of automatically constructing BEL statements from given evidence text, and of retrieving evidence text for given BEL statements. Given the complexity of the task, we designed an evaluation methodology which gives credit to partially correct statements. We identified various levels of information expressed by BEL statements, such as entities, functions, relations, and introduced an evaluation framework which rewards systems capable of delivering useful BEL fragments at each of these levels. The aim of this evaluation method is to help identify the characteristics of the systems which, if combined, would be most useful for achieving the overall goal of automatically constructing causal biological networks from text. © The Author(s) 2016. Published by Oxford University Press.

Prediction model for peninsular Indian summer monsoon rainfall using data mining and statistical approaches

NASA Astrophysics Data System (ADS)

Vathsala, H.; Koolagudi, Shashidhar G.

2017-01-01

In this paper we discuss a data mining application for predicting peninsular Indian summer monsoon rainfall, and propose an algorithm that combine data mining and statistical techniques. We select likely predictors based on association rules that have the highest confidence levels. We then cluster the selected predictors to reduce their dimensions and use cluster membership values for classification. We derive the predictors from local conditions in southern India, including mean sea level pressure, wind speed, and maximum and minimum temperatures. The global condition variables include southern oscillation and Indian Ocean dipole conditions. The algorithm predicts rainfall in five categories: Flood, Excess, Normal, Deficit and Drought. We use closed itemset mining, cluster membership calculations and a multilayer perceptron function in the algorithm to predict monsoon rainfall in peninsular India. Using Indian Institute of Tropical Meteorology data, we found the prediction accuracy of our proposed approach to be exceptionally good.

The ``battle of gold'' under the light of green economics: a case study from Greece

NASA Astrophysics Data System (ADS)

Damigos, D.; Kaliampakos, D.

2006-05-01

Mining firms stimulate local and national economies but this comes at a certain cost. In the light of increasing public concern, external costs of environmental degradation and social disruption are no longer of pure academic interest. The assessment of mining projects on the grounds of sustainable development is critical in order to decide whether the exploitation of mineral resources is socially desirable. In practice, few steps have been taken towards this end. In this paper, a case study is illustrated that provides the means for evaluating the social worthiness of mining projects. The analysis, which is the first of its kind in Greece, deals with a major problem of the mining industry: the gold debate on the grounds of green economics. The assessment is based on the social cost benefit approach. Well-established techniques (e.g. benefit transfer) and innovative approaches have been adopted to overcome various practical problems

OrganismTagger: detection, normalization and grounding of organism entities in biomedical documents.

PubMed

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.

Modelling the sensory space of varietal wines: Mining of large, unstructured text data and visualisation of style patterns.

PubMed

Valente, Carlo C; Bauer, Florian F; Venter, Fritz; Watson, Bruce; Nieuwoudt, Hélène H

2018-03-21

The increasingly large volumes of publicly available sensory descriptions of wine raises the question whether this source of data can be mined to extract meaningful domain-specific information about the sensory properties of wine. We introduce a novel application of formal concept lattices, in combination with traditional statistical tests, to visualise the sensory attributes of a big data set of some 7,000 Chenin blanc and Sauvignon blanc wines. Complexity was identified as an important driver of style in hereto uncharacterised Chenin blanc, and the sensory cues for specific styles were identified. This is the first study to apply these methods for the purpose of identifying styles within varietal wines. More generally, our interactive data visualisation and mining driven approach opens up new investigations towards better understanding of the complex field of sensory science.

A Control Chart Approach for Representing and Mining Data Streams with Shape Based Similarity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Omitaomu, Olufemi A

The mining of data streams for online condition monitoring is a challenging task in several domains including (electric) power grid system, intelligent manufacturing, and consumer science. Considering a power grid application in which thousands of sensors, called the phasor measurement units, are deployed on the power grid network to continuously collect streams of digital data for real-time situational awareness and system management. Depending on design, each sensor could stream between ten and sixty data samples per second. The myriad of sensory data captured could convey deeper insights about sequence of events in real-time and before major damages are done. However,more » the timely processing and analysis of these high-velocity and high-volume data streams is a challenge. Hence, a new data processing and transformation approach, based on the concept of control charts, for representing sequence of data streams from sensors is proposed. In addition, an application of the proposed approach for enhancing data mining tasks such as clustering using real-world power grid data streams is presented. The results indicate that the proposed approach is very efficient for data streams storage and manipulation.« less

New challenges for text mining: mapping between text and manually curated pathways

PubMed Central

Oda, Kanae; Kim, Jin-Dong; Ohta, Tomoko; Okanohara, Daisuke; Matsuzaki, Takuya; Tateisi, Yuka; Tsujii, Jun'ichi

2008-01-01

Background Associating literature with pathways poses new challenges to the Text Mining (TM) community. There are three main challenges to this task: (1) the identification of the mapping position of a specific entity or reaction in a given pathway, (2) the recognition of the causal relationships among multiple reactions, and (3) the formulation and implementation of required inferences based on biological domain knowledge. Results To address these challenges, we constructed new resources to link the text with a model pathway; they are: the GENIA pathway corpus with event annotation and NF-kB pathway. Through their detailed analysis, we address the untapped resource, ‘bio-inference,’ as well as the differences between text and pathway representation. Here, we show the precise comparisons of their representations and the nine classes of ‘bio-inference’ schemes observed in the pathway corpus. Conclusions We believe that the creation of such rich resources and their detailed analysis is the significant first step for accelerating the research of the automatic construction of pathway from text. PMID:18426550

VisualUrText: A Text Analytics Tool for Unstructured Textual Data

NASA Astrophysics Data System (ADS)

Zainol, Zuraini; Jaymes, Mohd T. H.; Nohuddin, Puteri N. E.

2018-05-01

The growing amount of unstructured text over Internet is tremendous. Text repositories come from Web 2.0, business intelligence and social networking applications. It is also believed that 80-90% of future growth data is available in the form of unstructured text databases that may potentially contain interesting patterns and trends. Text Mining is well known technique for discovering interesting patterns and trends which are non-trivial knowledge from massive unstructured text data. Text Mining covers multidisciplinary fields involving information retrieval (IR), text analysis, natural language processing (NLP), data mining, machine learning statistics and computational linguistics. This paper discusses the development of text analytics tool that is proficient in extracting, processing, analyzing the unstructured text data and visualizing cleaned text data into multiple forms such as Document Term Matrix (DTM), Frequency Graph, Network Analysis Graph, Word Cloud and Dendogram. This tool, VisualUrText, is developed to assist students and researchers for extracting interesting patterns and trends in document analyses.

Efficient discovery of risk patterns in medical data.

PubMed

Li, Jiuyong; Fu, Ada Wai-chee; Fahey, Paul

2009-01-01

This paper studies a problem of efficiently discovering risk patterns in medical data. Risk patterns are defined by a statistical metric, relative risk, which has been widely used in epidemiological research. To avoid fruitless search in the complete exploration of risk patterns, we define optimal risk pattern set to exclude superfluous patterns, i.e. complicated patterns with lower relative risk than their corresponding simpler form patterns. We prove that mining optimal risk pattern sets conforms an anti-monotone property that supports an efficient mining algorithm. We propose an efficient algorithm for mining optimal risk pattern sets based on this property. We also propose a hierarchical structure to present discovered patterns for the easy perusal by domain experts. The proposed approach is compared with two well-known rule discovery methods, decision tree and association rule mining approaches on benchmark data sets and applied to a real world application. The proposed method discovers more and better quality risk patterns than a decision tree approach. The decision tree method is not designed for such applications and is inadequate for pattern exploring. The proposed method does not discover a large number of uninteresting superfluous patterns as an association mining approach does. The proposed method is more efficient than an association rule mining method. A real world case study shows that the method reveals some interesting risk patterns to medical practitioners. The proposed method is an efficient approach to explore risk patterns. It quickly identifies cohorts of patients that are vulnerable to a risk outcome from a large data set. The proposed method is useful for exploratory study on large medical data to generate and refine hypotheses. The method is also useful for designing medical surveillance systems.

Large-Scale Overlays and Trends: Visually Mining, Panning and Zooming the Observable Universe.

PubMed

Luciani, Timothy Basil; Cherinka, Brian; Oliphant, Daniel; Myers, Sean; Wood-Vasey, W Michael; Labrinidis, Alexandros; Marai, G Elisabeta

2014-07-01

We introduce a web-based computing infrastructure to assist the visual integration, mining and interactive navigation of large-scale astronomy observations. Following an analysis of the application domain, we design a client-server architecture to fetch distributed image data and to partition local data into a spatial index structure that allows prefix-matching of spatial objects. In conjunction with hardware-accelerated pixel-based overlays and an online cross-registration pipeline, this approach allows the fetching, displaying, panning and zooming of gigabit panoramas of the sky in real time. To further facilitate the integration and mining of spatial and non-spatial data, we introduce interactive trend images-compact visual representations for identifying outlier objects and for studying trends within large collections of spatial objects of a given class. In a demonstration, images from three sky surveys (SDSS, FIRST and simulated LSST results) are cross-registered and integrated as overlays, allowing cross-spectrum analysis of astronomy observations. Trend images are interactively generated from catalog data and used to visually mine astronomy observations of similar type. The front-end of the infrastructure uses the web technologies WebGL and HTML5 to enable cross-platform, web-based functionality. Our approach attains interactive rendering framerates; its power and flexibility enables it to serve the needs of the astronomy community. Evaluation on three case studies, as well as feedback from domain experts emphasize the benefits of this visual approach to the observational astronomy field; and its potential benefits to large scale geospatial visualization in general.

Functional Genome Mining for Metabolites Encoded by Large Gene Clusters through Heterologous Expression of a Whole-Genome Bacterial Artificial Chromosome Library in Streptomyces spp.

PubMed Central

Xu, Min; Wang, Yemin; Zhao, Zhilong; Gao, Guixi; Huang, Sheng-Xiong; Kang, Qianjin; He, Xinyi; Lin, Shuangjun; Pang, Xiuhua; Deng, Zixin

2016-01-01

ABSTRACT Genome sequencing projects in the last decade revealed numerous cryptic biosynthetic pathways for unknown secondary metabolites in microbes, revitalizing drug discovery from microbial metabolites by approaches called genome mining. In this work, we developed a heterologous expression and functional screening approach for genome mining from genomic bacterial artificial chromosome (BAC) libraries in Streptomyces spp. We demonstrate mining from a strain of Streptomyces rochei, which is known to produce streptothricins and borrelidin, by expressing its BAC library in the surrogate host Streptomyces lividans SBT5, and screening for antimicrobial activity. In addition to the successful capture of the streptothricin and borrelidin biosynthetic gene clusters, we discovered two novel linear lipopeptides and their corresponding biosynthetic gene cluster, as well as a novel cryptic gene cluster for an unknown antibiotic from S. rochei. This high-throughput functional genome mining approach can be easily applied to other streptomycetes, and it is very suitable for the large-scale screening of genomic BAC libraries for bioactive natural products and the corresponding biosynthetic pathways. IMPORTANCE Microbial genomes encode numerous cryptic biosynthetic gene clusters for unknown small metabolites with potential biological activities. Several genome mining approaches have been developed to activate and bring these cryptic metabolites to biological tests for future drug discovery. Previous sequence-guided procedures relied on bioinformatic analysis to predict potentially interesting biosynthetic gene clusters. In this study, we describe an efficient approach based on heterologous expression and functional screening of a whole-genome library for the mining of bioactive metabolites from Streptomyces. The usefulness of this function-driven approach was demonstrated by the capture of four large biosynthetic gene clusters for metabolites of various chemical types, including streptothricins, borrelidin, two novel lipopeptides, and one unknown antibiotic from Streptomyces rochei Sal35. The transfer, expression, and screening of the library were all performed in a high-throughput way, so that this approach is scalable and adaptable to industrial automation for next-generation antibiotic discovery. PMID:27451447

A Framework of Knowledge Integration and Discovery for Supporting Pharmacogenomics Target Predication of Adverse Drug Events: A Case Study of Drug-Induced Long QT Syndrome.

PubMed

Jiang, Guoqian; Wang, Chen; Zhu, Qian; Chute, Christopher G

2013-01-01

Knowledge-driven text mining is becoming an important research area for identifying pharmacogenomics target genes. However, few of such studies have been focused on the pharmacogenomics targets of adverse drug events (ADEs). The objective of the present study is to build a framework of knowledge integration and discovery that aims to support pharmacogenomics target predication of ADEs. We integrate a semantically annotated literature corpus Semantic MEDLINE with a semantically coded ADE knowledgebase known as ADEpedia using a semantic web based framework. We developed a knowledge discovery approach combining a network analysis of a protein-protein interaction (PPI) network and a gene functional classification approach. We performed a case study of drug-induced long QT syndrome for demonstrating the usefulness of the framework in predicting potential pharmacogenomics targets of ADEs.

Document cards: a top trumps visualization for documents.

PubMed

Strobelt, Hendrik; Oelke, Daniela; Rohrdantz, Christian; Stoffel, Andreas; Keim, Daniel A; Deussen, Oliver

2009-01-01

Finding suitable, less space consuming views for a document's main content is crucial to provide convenient access to large document collections on display devices of different size. We present a novel compact visualization which represents the document's key semantic as a mixture of images and important key terms, similar to cards in a top trumps game. The key terms are extracted using an advanced text mining approach based on a fully automatic document structure extraction. The images and their captions are extracted using a graphical heuristic and the captions are used for a semi-semantic image weighting. Furthermore, we use the image color histogram for classification and show at least one representative from each non-empty image class. The approach is demonstrated for the IEEE InfoVis publications of a complete year. The method can easily be applied to other publication collections and sets of documents which contain images.

Identification and Progression of Heart Disease Risk Factors in Diabetic Patients from Longitudinal Electronic Health Records.

PubMed

Jonnagaddala, Jitendra; Liaw, Siaw-Teng; Ray, Pradeep; Kumar, Manish; Dai, Hong-Jie; Hsu, Chien-Yeh

2015-01-01

Heart disease is the leading cause of death worldwide. Therefore, assessing the risk of its occurrence is a crucial step in predicting serious cardiac events. Identifying heart disease risk factors and tracking their progression is a preliminary step in heart disease risk assessment. A large number of studies have reported the use of risk factor data collected prospectively. Electronic health record systems are a great resource of the required risk factor data. Unfortunately, most of the valuable information on risk factor data is buried in the form of unstructured clinical notes in electronic health records. In this study, we present an information extraction system to extract related information on heart disease risk factors from unstructured clinical notes using a hybrid approach. The hybrid approach employs both machine learning and rule-based clinical text mining techniques. The developed system achieved an overall microaveraged F-score of 0.8302.

Use of amendments to restore ecosystem function to metal mining impacted sites; Tools to evaluate efficacy

USDA-ARS?s Scientific Manuscript database

There is a long history of using residuals based soil amendments for restoration of disturbed sites. More recently, this approach has been tested for use on metal contaminated mining sites. For these sites, amendment mixtures are targeted to reduce metal availability in situ as well as restore eco...

BioCreative Workshops for DOE Genome Sciences: Text Mining for Metagenomics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, Cathy H.; Hirschman, Lynette

The objective of this project was to host BioCreative workshops to define and develop text mining tasks to meet the needs of the Genome Sciences community, focusing on metadata information extraction in metagenomics. Following the successful introduction of metagenomics at the BioCreative IV workshop, members of the metagenomics community and BioCreative communities continued discussion to identify candidate topics for a BioCreative metagenomics track for BioCreative V. Of particular interest was the capture of environmental and isolation source information from text. The outcome was to form a “community of interest” around work on the interactive EXTRACT system, which supported interactive taggingmore » of environmental and species data. This experiment is included in the BioCreative V virtual issue of Database. In addition, there was broad participation by members of the metagenomics community in the panels held at BioCreative V, leading to valuable exchanges between the text mining developers and members of the metagenomics research community. These exchanges are reflected in a number of the overview and perspective pieces also being captured in the BioCreative V virtual issue. Overall, this conversation has exposed the metagenomics researchers to the possibilities of text mining, and educated the text mining developers to the specific needs of the metagenomics community.« less

Simulating adverse event spontaneous reporting systems as preferential attachment networks: application to the Vaccine Adverse Event Reporting System.

PubMed

Scott, J; Botsis, T; Ball, R

2014-01-01

Spontaneous Reporting Systems [SRS] are critical tools in the post-licensure evaluation of medical product safety. Regulatory authorities use a variety of data mining techniques to detect potential safety signals in SRS databases. Assessing the performance of such signal detection procedures requires simulated SRS databases, but simulation strategies proposed to date each have limitations. We sought to develop a novel SRS simulation strategy based on plausible mechanisms for the growth of databases over time. We developed a simulation strategy based on the network principle of preferential attachment. We demonstrated how this strategy can be used to create simulations based on specific databases of interest, and provided an example of using such simulations to compare signal detection thresholds for a popular data mining algorithm. The preferential attachment simulations were generally structurally similar to our targeted SRS database, although they had fewer nodes of very high degree. The approach was able to generate signal-free SRS simulations, as well as mimicking specific known true signals. Explorations of different reporting thresholds for the FDA Vaccine Adverse Event Reporting System suggested that using proportional reporting ratio [PRR] > 3.0 may yield better signal detection operating characteristics than the more commonly used PRR > 2.0 threshold. The network analytic approach to SRS simulation based on the principle of preferential attachment provides an attractive framework for exploring the performance of safety signal detection algorithms. This approach is potentially more principled and versatile than existing simulation approaches. The utility of network-based SRS simulations needs to be further explored by evaluating other types of simulated signals with a broader range of data mining approaches, and comparing network-based simulations with other simulation strategies where applicable.

Improving Collaborative Learning in the Classroom: Text Mining Based Grouping and Representing

ERIC Educational Resources Information Center

Erkens, Melanie; Bodemer, Daniel; Hoppe, H. Ulrich

2016-01-01

Orchestrating collaborative learning in the classroom involves tasks such as forming learning groups with heterogeneous knowledge and making learners aware of the knowledge differences. However, gathering information on which the formation of appropriate groups and the creation of graphical knowledge representations can be based is very effortful…

TEES 2.2: Biomedical Event Extraction for Diverse Corpora

PubMed Central

2015-01-01

Background The Turku Event Extraction System (TEES) is a text mining program developed for the extraction of events, complex biomedical relationships, from scientific literature. Based on a graph-generation approach, the system detects events with the use of a rich feature set built via dependency parsing. The TEES system has achieved record performance in several of the shared tasks of its domain, and continues to be used in a variety of biomedical text mining tasks. Results The TEES system was quickly adapted to the BioNLP'13 Shared Task in order to provide a public baseline for derived systems. An automated approach was developed for learning the underlying annotation rules of event type, allowing immediate adaptation to the various subtasks, and leading to a first place in four out of eight tasks. The system for the automated learning of annotation rules is further enhanced in this paper to the point of requiring no manual adaptation to any of the BioNLP'13 tasks. Further, the scikit-learn machine learning library is integrated into the system, bringing a wide variety of machine learning methods usable with TEES in addition to the default SVM. A scikit-learn ensemble method is also used to analyze the importances of the features in the TEES feature sets. Conclusions The TEES system was introduced for the BioNLP'09 Shared Task and has since then demonstrated good performance in several other shared tasks. By applying the current TEES 2.2 system to multiple corpora from these past shared tasks an overarching analysis of the most promising methods and possible pitfalls in the evolving field of biomedical event extraction are presented. PMID:26551925

Discovery of novel biomarkers and phenotypes by semantic technologies

PubMed Central

2013-01-01

Background Biomarkers and target-specific phenotypes are important to targeted drug design and individualized medicine, thus constituting an important aspect of modern pharmaceutical research and development. More and more, the discovery of relevant biomarkers is aided by in silico techniques based on applying data mining and computational chemistry on large molecular databases. However, there is an even larger source of valuable information available that can potentially be tapped for such discoveries: repositories constituted by research documents. Results This paper reports on a pilot experiment to discover potential novel biomarkers and phenotypes for diabetes and obesity by self-organized text mining of about 120,000 PubMed abstracts, public clinical trial summaries, and internal Merck research documents. These documents were directly analyzed by the InfoCodex semantic engine, without prior human manipulations such as parsing. Recall and precision against established, but different benchmarks lie in ranges up to 30% and 50% respectively. Retrieval of known entities missed by other traditional approaches could be demonstrated. Finally, the InfoCodex semantic engine was shown to discover new diabetes and obesity biomarkers and phenotypes. Amongst these were many interesting candidates with a high potential, although noticeable noise (uninteresting or obvious terms) was generated. Conclusions The reported approach of employing autonomous self-organising semantic engines to aid biomarker discovery, supplemented by appropriate manual curation processes, shows promise and has potential to impact, conservatively, a faster alternative to vocabulary processes dependent on humans having to read and analyze all the texts. More optimistically, it could impact pharmaceutical research, for example to shorten time-to-market of novel drugs, or speed up early recognition of dead ends and adverse reactions. PMID:23402646

TEES 2.2: Biomedical Event Extraction for Diverse Corpora.

PubMed

Björne, Jari; Salakoski, Tapio

2015-01-01

The Turku Event Extraction System (TEES) is a text mining program developed for the extraction of events, complex biomedical relationships, from scientific literature. Based on a graph-generation approach, the system detects events with the use of a rich feature set built via dependency parsing. The TEES system has achieved record performance in several of the shared tasks of its domain, and continues to be used in a variety of biomedical text mining tasks. The TEES system was quickly adapted to the BioNLP'13 Shared Task in order to provide a public baseline for derived systems. An automated approach was developed for learning the underlying annotation rules of event type, allowing immediate adaptation to the various subtasks, and leading to a first place in four out of eight tasks. The system for the automated learning of annotation rules is further enhanced in this paper to the point of requiring no manual adaptation to any of the BioNLP'13 tasks. Further, the scikit-learn machine learning library is integrated into the system, bringing a wide variety of machine learning methods usable with TEES in addition to the default SVM. A scikit-learn ensemble method is also used to analyze the importances of the features in the TEES feature sets. The TEES system was introduced for the BioNLP'09 Shared Task and has since then demonstrated good performance in several other shared tasks. By applying the current TEES 2.2 system to multiple corpora from these past shared tasks an overarching analysis of the most promising methods and possible pitfalls in the evolving field of biomedical event extraction are presented.

Some Psychometric and Design Implications of Game-Based Learning Analytics

ERIC Educational Resources Information Center

Gibson, David; Clarke-Midura, Jody

2013-01-01

The rise of digital game and simulation-based learning applications has led to new approaches in educational measurement that take account of patterns in time, high resolution paths of action, and clusters of virtual performance artifacts. The new approaches, which depart from traditional statistical analyses, include data mining, machine…

Evaluating a Bilingual Text-Mining System with a Taxonomy of Key Words and Hierarchical Visualization for Understanding Learner-Generated Text

ERIC Educational Resources Information Center

Kong, Siu Cheung; Li, Ping; Song, Yanjie

2018-01-01

This study evaluated a bilingual text-mining system, which incorporated a bilingual taxonomy of key words and provided hierarchical visualization, for understanding learner-generated text in the learning management systems through automatic identification and counting of matching key words. A class of 27 in-service teachers studied a course…

Challenges in recovering resources from acid mine drainage

USGS Publications Warehouse

Nordstrom, D. Kirk; Bowell, Robert J.; Campbell, Kate M.; Alpers, Charles N.

2017-01-01

Metal recovery from mine waters and effluents is not a new approach but one that has occurred largely opportunistically over the last four millennia. Due to the need for low-cost resources and increasingly stringent environmental conditions, mine waters are being considered in a fresh light with a designed, deliberate approach to resource recovery often as part of a larger water treatment evaluation. Mine water chemistry is highly dependent on many factors including geology, ore deposit composition and mineralogy, mining methods, climate, site hydrology, and others. Mine waters are typically Ca-Mg-SO4±Al±Fe with a broad range in pH and metal content. The main issue in recovering components of these waters having potential economic value, such as base metals or rare earth elements, is the separation of these from more reactive metals such as Fe and Al. Broad categories of methods for separating and extracting substances from acidic mine drainage are chemical and biological. Chemical methods include solution, physicochemical, and electrochemical technologies. Advances in membrane techniques such as reverse osmosis have been substantial and the technique is both physical and chemical. Biological methods may be further divided into microbiological and macrobiological, but only the former is considered here as a recovery method, as the latter is typically used as a passive form of water treatment.

Mining FDA drug labels using an unsupervised learning technique--topic modeling.

PubMed

Bisgin, Halil; Liu, Zhichao; Fang, Hong; Xu, Xiaowei; Tong, Weida

2011-10-18

The Food and Drug Administration (FDA) approved drug labels contain a broad array of information, ranging from adverse drug reactions (ADRs) to drug efficacy, risk-benefit consideration, and more. However, the labeling language used to describe these information is free text often containing ambiguous semantic descriptions, which poses a great challenge in retrieving useful information from the labeling text in a consistent and accurate fashion for comparative analysis across drugs. Consequently, this task has largely relied on the manual reading of the full text by experts, which is time consuming and labor intensive. In this study, a novel text mining method with unsupervised learning in nature, called topic modeling, was applied to the drug labeling with a goal of discovering "topics" that group drugs with similar safety concerns and/or therapeutic uses together. A total of 794 FDA-approved drug labels were used in this study. First, the three labeling sections (i.e., Boxed Warning, Warnings and Precautions, Adverse Reactions) of each drug label were processed by the Medical Dictionary for Regulatory Activities (MedDRA) to convert the free text of each label to the standard ADR terms. Next, the topic modeling approach with latent Dirichlet allocation (LDA) was applied to generate 100 topics, each associated with a set of drugs grouped together based on the probability analysis. Lastly, the efficacy of the topic modeling was evaluated based on known information about the therapeutic uses and safety data of drugs. The results demonstrate that drugs grouped by topics are associated with the same safety concerns and/or therapeutic uses with statistical significance (P<0.05). The identified topics have distinct context that can be directly linked to specific adverse events (e.g., liver injury or kidney injury) or therapeutic application (e.g., antiinfectives for systemic use). We were also able to identify potential adverse events that might arise from specific medications via topics. The successful application of topic modeling on the FDA drug labeling demonstrates its potential utility as a hypothesis generation means to infer hidden relationships of concepts such as, in this study, drug safety and therapeutic use in the study of biomedical documents.

Effect of Temporal Relationships in Associative Rule Mining for Web Log Data

PubMed Central

Mohd Khairudin, Nazli; Mustapha, Aida

2014-01-01

The advent of web-based applications and services has created such diverse and voluminous web log data stored in web servers, proxy servers, client machines, or organizational databases. This paper attempts to investigate the effect of temporal attribute in relational rule mining for web log data. We incorporated the characteristics of time in the rule mining process and analysed the effect of various temporal parameters. The rules generated from temporal relational rule mining are then compared against the rules generated from the classical rule mining approach such as the Apriori and FP-Growth algorithms. The results showed that by incorporating the temporal attribute via time, the number of rules generated is subsequently smaller but is comparable in terms of quality. PMID:24587757

75 FR 51291 - National Science Board: Sunshine Act Meetings; Notice

Federal Register 2010, 2011, 2012, 2013, 2014

2010-08-19

...-Gathering Activities. [cir] COV Report Text-Mining. [cir] Design of Research Questions for External Input. [cir] SBE/CISE Text-Mining Projects. [cir] Using a Blog for Informal Input. Committee on Education and...

Prediction of Floor Water Inrush: The Application of GIS-Based AHP Vulnerable Index Method to Donghuantuo Coal Mine, China

NASA Astrophysics Data System (ADS)

Wu, Qiang; Liu, Yuanzhang; Liu, Donghai; Zhou, Wanfang

2011-09-01

Floor water inrush represents a geohazard that can pose significant threat to safe operations for instance in coal mines in China and elsewhere. Its occurrence is controlled by many factors, and the processes are often not amenable to mathematical expressions. To evaluate the water inrush risk, the paper proposes the vulnerability index approach by coupling the analytic hierarchy process (AHP) and geographic information system (GIS). The detailed procedures of using this innovative approach are shown in a case study in China (Donghuantuo Coal Mine). The powerful spatial data analysis functions of GIS was used to establish the thematic layer of each of the six factors that control the water inrush, and the contribution weights of each factor was determined with the AHP method. The established AHP evaluation model was used to determine the threshold value for each risk level with a histogram of the water inrush vulnerability index. As a result, the mine area was divided into five regions with different vulnerability levels which served as general guidelines for the mine operations. The prediction results were further corroborated with the actual mining data, and the evaluation result is satisfactory.

Integrating text mining, data mining, and network analysis for identifying genetic breast cancer trends.

PubMed

Jurca, Gabriela; Addam, Omar; Aksac, Alper; Gao, Shang; Özyer, Tansel; Demetrick, Douglas; Alhajj, Reda

2016-04-26

Breast cancer is a serious disease which affects many women and may lead to death. It has received considerable attention from the research community. Thus, biomedical researchers aim to find genetic biomarkers indicative of the disease. Novel biomarkers can be elucidated from the existing literature. However, the vast amount of scientific publications on breast cancer make this a daunting task. This paper presents a framework which investigates existing literature data for informative discoveries. It integrates text mining and social network analysis in order to identify new potential biomarkers for breast cancer. We utilized PubMed for the testing. We investigated gene-gene interactions, as well as novel interactions such as gene-year, gene-country, and abstract-country to find out how the discoveries varied over time and how overlapping/diverse are the discoveries and the interest of various research groups in different countries. Interesting trends have been identified and discussed, e.g., different genes are highlighted in relationship to different countries though the various genes were found to share functionality. Some text analysis based results have been validated against results from other tools that predict gene-gene relations and gene functions.

Research on Occupational Safety, Health Management and Risk Control Technology in Coal Mines.

PubMed

Zhou, Lu-Jie; Cao, Qing-Gui; Yu, Kai; Wang, Lin-Lin; Wang, Hai-Bin

2018-04-26

This paper studies the occupational safety and health management methods as well as risk control technology associated with the coal mining industry, including daily management of occupational safety and health, identification and assessment of risks, early warning and dynamic monitoring of risks, etc.; also, a B/S mode software (Geting Coal Mine, Jining, Shandong, China), i.e., Coal Mine Occupational Safety and Health Management and Risk Control System, is developed to attain the aforementioned objectives, namely promoting the coal mine occupational safety and health management based on early warning and dynamic monitoring of risks. Furthermore, the practical effectiveness and the associated pattern for applying this software package to coal mining is analyzed. The study indicates that the presently developed coal mine occupational safety and health management and risk control technology and the associated software can support the occupational safety and health management efforts in coal mines in a standardized and effective manner. It can also control the accident risks scientifically and effectively; its effective implementation can further improve the coal mine occupational safety and health management mechanism, and further enhance the risk management approaches. Besides, its implementation indicates that the occupational safety and health management and risk control technology has been established based on a benign cycle involving dynamic feedback and scientific development, which can provide a reliable assurance to the safe operation of coal mines.

Research on Occupational Safety, Health Management and Risk Control Technology in Coal Mines

PubMed Central

Zhou, Lu-jie; Cao, Qing-gui; Yu, Kai; Wang, Lin-lin; Wang, Hai-bin

2018-01-01

This paper studies the occupational safety and health management methods as well as risk control technology associated with the coal mining industry, including daily management of occupational safety and health, identification and assessment of risks, early warning and dynamic monitoring of risks, etc.; also, a B/S mode software (Geting Coal Mine, Jining, Shandong, China), i.e., Coal Mine Occupational Safety and Health Management and Risk Control System, is developed to attain the aforementioned objectives, namely promoting the coal mine occupational safety and health management based on early warning and dynamic monitoring of risks. Furthermore, the practical effectiveness and the associated pattern for applying this software package to coal mining is analyzed. The study indicates that the presently developed coal mine occupational safety and health management and risk control technology and the associated software can support the occupational safety and health management efforts in coal mines in a standardized and effective manner. It can also control the accident risks scientifically and effectively; its effective implementation can further improve the coal mine occupational safety and health management mechanism, and further enhance the risk management approaches. Besides, its implementation indicates that the occupational safety and health management and risk control technology has been established based on a benign cycle involving dynamic feedback and scientific development, which can provide a reliable assurance to the safe operation of coal mines. PMID:29701715

Promoter Sequences Prediction Using Relational Association Rule Mining

PubMed Central

Czibula, Gabriela; Bocicor, Maria-Iuliana; Czibula, Istvan Gergely

2012-01-01

In this paper we are approaching, from a computational perspective, the problem of promoter sequences prediction, an important problem within the field of bioinformatics. As the conditions for a DNA sequence to function as a promoter are not known, machine learning based classification models are still developed to approach the problem of promoter identification in the DNA. We are proposing a classification model based on relational association rules mining. Relational association rules are a particular type of association rules and describe numerical orderings between attributes that commonly occur over a data set. Our classifier is based on the discovery of relational association rules for predicting if a DNA sequence contains or not a promoter region. An experimental evaluation of the proposed model and comparison with similar existing approaches is provided. The obtained results show that our classifier overperforms the existing techniques for identifying promoter sequences, confirming the potential of our proposal. PMID:22563233

Assimilating Text-Mining & Bio-Informatics Tools to Analyze Cellulase structures

NASA Astrophysics Data System (ADS)

Satyasree, K. P. N. V., Dr; Lalitha Kumari, B., Dr; Jyotsna Devi, K. S. N. V.; Choudri, S. M. Roy; Pratap Joshi, K.

2017-08-01

Text-mining is one of the best potential way of automatically extracting information from the huge biological literature. To exploit its prospective, the knowledge encrypted in the text should be converted to some semantic representation such as entities and relations, which could be analyzed by machines. But large-scale practical systems for this purpose are rare. But text mining could be helpful for generating or validating predictions. Cellulases have abundant applications in various industries. Cellulose degrading enzymes are cellulases and the same producing bacteria - Bacillus subtilis & fungus Pseudomonas putida were isolated from top soil of Guntur Dt. A.P. India. Absolute cultures were conserved on potato dextrose agar medium for molecular studies. In this paper, we presented how well the text mining concepts can be used to analyze cellulase producing bacteria and fungi, their comparative structures are also studied with the aid of well-establised, high quality standard bioinformatic tools such as Bioedit, Swissport, Protparam, EMBOSSwin with which a complete data on Cellulases like structure, constituents of the enzyme has been obtained.

Mining the SDSS SkyServer SQL queries log

NASA Astrophysics Data System (ADS)

Hirota, Vitor M.; Santos, Rafael; Raddick, Jordan; Thakar, Ani

2016-05-01

SkyServer, the Internet portal for the Sloan Digital Sky Survey (SDSS) astronomic catalog, provides a set of tools that allows data access for astronomers and scientific education. One of SkyServer data access interfaces allows users to enter ad-hoc SQL statements to query the catalog. SkyServer also presents some template queries that can be used as basis for more complex queries. This interface has logged over 330 million queries submitted since 2001. It is expected that analysis of this data can be used to investigate usage patterns, identify potential new classes of queries, find similar queries, etc. and to shed some light on how users interact with the Sloan Digital Sky Survey data and how scientists have adopted the new paradigm of e-Science, which could in turn lead to enhancements on the user interfaces and experience in general. In this paper we review some approaches to SQL query mining, apply the traditional techniques used in the literature and present lessons learned, namely, that the general text mining approach for feature extraction and clustering does not seem to be adequate for this type of data, and, most importantly, we find that this type of analysis can result in very different queries being clustered together.

Text grouping in patent analysis using adaptive K-means clustering algorithm

NASA Astrophysics Data System (ADS)

Shanie, Tiara; Suprijadi, Jadi; Zulhanif

2017-03-01

Patents are one of the Intellectual Property. Analyzing patent is one requirement in knowing well the development of technology in each country and in the world now. This study uses the patent document coming from the Espacenet server about Green Tea. Patent documents related to the technology in the field of tea is still widespread, so it will be difficult for users to information retrieval (IR). Therefore, it is necessary efforts to categorize documents in a specific group of related terms contained therein. This study uses titles patent text data with the proposed Green Tea in Statistical Text Mining methods consists of two phases: data preparation and data analysis stage. The data preparation phase uses Text Mining methods and data analysis stage is done by statistics. Statistical analysis in this study using a cluster analysis algorithm, the Adaptive K-Means Clustering Algorithm. Results from this study showed that based on the maximum value Silhouette, generate 87 clusters associated fifteen terms therein that can be utilized in the process of information retrieval needs.

Internet of Things in Health Trends Through Bibliometrics and Text Mining.

PubMed

Konstantinidis, Stathis Th; Billis, Antonis; Wharrad, Heather; Bamidis, Panagiotis D

2017-01-01

Recently a new buzzword has slowly but surely emerged, namely the Internet of Things (IoT). The importance of IoT is identified worldwide both by organisations and governments and the scientific community with an incremental number of publications during the last few years. IoT in Health is one of the main pillars of this evolution, but limited research has been performed on future visions and trends. Thus, in this study we investigate the longitudinal trends of Internet of Things in Health through bibliometrics and use of text mining. Seven hundred seventy eight (778) articles were retrieved form The Web of Science database from 1998 to 2016. The publications are grouped into thirty (30) clusters based on abstract text analysis resulting into some eight (8) trends of IoT in Health. Research in this field is obviously obtaining a worldwide character with specific trends, which are worth delineating to be in favour of some areas.

Miners' Misconceptions of Flow Distribution Within Circuits as a Factor Influencing Underground Mining Accidents.

NASA Astrophysics Data System (ADS)

Passaro, Perry David

Misconceptions can be thought of as naive approaches to problem solving that are perceptually appealing but incorrect and inconsistent with scientific evidence (Piaget, 1929). One type of misconception involves flow distributions within circuits. This concept is important because miners' conceptual errors about flow distribution changes within complex circuits may be in part responsible for fatal mine disasters. Based on the theory that misconceptions of flow distribution changes within circuits were responsible for underground mine disasters involving mine ventilation circuits, a series of studies was undertaken with mining engineering students, professional mining engineers, as well as mine foremen, mine supervisors, mine rescue members, mine maintenance personnel, mining researchers and working miners to identify these conceptual errors and errors in mine ventilation procedures. Results indicate that misconceptions of flow distribution changes within circuits exist in over 70 percent of the subjects sampled. It is assumed that these misconceptions of flow distribution changes within circuits result in errors of judgment when miners are faced with inferring and changing ventilation arrangements when two or more mine sections are connected. Furthermore, it is assumed that these misconceptions are pervasive in the mining industry and may be responsible for at least two mine ventilation disasters. The findings of this study are consistent with Piaget's (1929) model of figurative and operative knowledge. This model states that misconceptions are in part due to a lack of knowledge of dynamic transformations and how to apply content information. Recommendations for future research include the development of an interactive expert system for training miners with ventilation arrangements. Such a system would meet the educational recommendations made by Piaget (1973b) by involving a hands-on approach that allows discovery, interaction, the opportunity to make mistakes and to review the cognitive concepts on which the subject relied during his manipulation of the ventilation system.

MinePath: Mining for Phenotype Differential Sub-paths in Molecular Pathways

PubMed Central

Koumakis, Lefteris; Kartsaki, Evgenia; Chatzimina, Maria; Zervakis, Michalis; Vassou, Despoina; Marias, Kostas; Moustakis, Vassilis; Potamias, George

2016-01-01

Pathway analysis methodologies couple traditional gene expression analysis with knowledge encoded in established molecular pathway networks, offering a promising approach towards the biological interpretation of phenotype differentiating genes. Early pathway analysis methodologies, named as gene set analysis (GSA), view pathways just as plain lists of genes without taking into account either the underlying pathway network topology or the involved gene regulatory relations. These approaches, even if they achieve computational efficiency and simplicity, consider pathways that involve the same genes as equivalent in terms of their gene enrichment characteristics. Most recent pathway analysis approaches take into account the underlying gene regulatory relations by examining their consistency with gene expression profiles and computing a score for each profile. Even with this approach, assessing and scoring single-relations limits the ability to reveal key gene regulation mechanisms hidden in longer pathway sub-paths. We introduce MinePath, a pathway analysis methodology that addresses and overcomes the aforementioned problems. MinePath facilitates the decomposition of pathways into their constituent sub-paths. Decomposition leads to the transformation of single-relations to complex regulation sub-paths. Regulation sub-paths are then matched with gene expression sample profiles in order to evaluate their functional status and to assess phenotype differential power. Assessment of differential power supports the identification of the most discriminant profiles. In addition, MinePath assess the significance of the pathways as a whole, ranking them by their p-values. Comparison results with state-of-the-art pathway analysis systems are indicative for the soundness and reliability of the MinePath approach. In contrast with many pathway analysis tools, MinePath is a web-based system (www.minepath.org) offering dynamic and rich pathway visualization functionality, with the unique characteristic to color regulatory relations between genes and reveal their phenotype inclination. This unique characteristic makes MinePath a valuable tool for in silico molecular biology experimentation as it serves the biomedical researchers’ exploratory needs to reveal and interpret the regulatory mechanisms that underlie and putatively govern the expression of target phenotypes. PMID:27832067

MinePath: Mining for Phenotype Differential Sub-paths in Molecular Pathways.

PubMed

Koumakis, Lefteris; Kanterakis, Alexandros; Kartsaki, Evgenia; Chatzimina, Maria; Zervakis, Michalis; Tsiknakis, Manolis; Vassou, Despoina; Kafetzopoulos, Dimitris; Marias, Kostas; Moustakis, Vassilis; Potamias, George

2016-11-01

Pathway analysis methodologies couple traditional gene expression analysis with knowledge encoded in established molecular pathway networks, offering a promising approach towards the biological interpretation of phenotype differentiating genes. Early pathway analysis methodologies, named as gene set analysis (GSA), view pathways just as plain lists of genes without taking into account either the underlying pathway network topology or the involved gene regulatory relations. These approaches, even if they achieve computational efficiency and simplicity, consider pathways that involve the same genes as equivalent in terms of their gene enrichment characteristics. Most recent pathway analysis approaches take into account the underlying gene regulatory relations by examining their consistency with gene expression profiles and computing a score for each profile. Even with this approach, assessing and scoring single-relations limits the ability to reveal key gene regulation mechanisms hidden in longer pathway sub-paths. We introduce MinePath, a pathway analysis methodology that addresses and overcomes the aforementioned problems. MinePath facilitates the decomposition of pathways into their constituent sub-paths. Decomposition leads to the transformation of single-relations to complex regulation sub-paths. Regulation sub-paths are then matched with gene expression sample profiles in order to evaluate their functional status and to assess phenotype differential power. Assessment of differential power supports the identification of the most discriminant profiles. In addition, MinePath assess the significance of the pathways as a whole, ranking them by their p-values. Comparison results with state-of-the-art pathway analysis systems are indicative for the soundness and reliability of the MinePath approach. In contrast with many pathway analysis tools, MinePath is a web-based system (www.minepath.org) offering dynamic and rich pathway visualization functionality, with the unique characteristic to color regulatory relations between genes and reveal their phenotype inclination. This unique characteristic makes MinePath a valuable tool for in silico molecular biology experimentation as it serves the biomedical researchers' exploratory needs to reveal and interpret the regulatory mechanisms that underlie and putatively govern the expression of target phenotypes.

Utilizing the Structure and Content Information for XML Document Clustering

NASA Astrophysics Data System (ADS)

Tran, Tien; Kutty, Sangeetha; Nayak, Richi

This paper reports on the experiments and results of a clustering approach used in the INEX 2008 document mining challenge. The clustering approach utilizes both the structure and content information of the Wikipedia XML document collection. A latent semantic kernel (LSK) is used to measure the semantic similarity between XML documents based on their content features. The construction of a latent semantic kernel involves the computing of singular vector decomposition (SVD). On a large feature space matrix, the computation of SVD is very expensive in terms of time and memory requirements. Thus in this clustering approach, the dimension of the document space of a term-document matrix is reduced before performing SVD. The document space reduction is based on the common structural information of the Wikipedia XML document collection. The proposed clustering approach has shown to be effective on the Wikipedia collection in the INEX 2008 document mining challenge.

What the papers say: Text mining for genomics and systems biology

PubMed Central

2010-01-01

Keeping up with the rapidly growing literature has become virtually impossible for most scientists. This can have dire consequences. First, we may waste research time and resources on reinventing the wheel simply because we can no longer maintain a reliable grasp on the published literature. Second, and perhaps more detrimental, judicious (or serendipitous) combination of knowledge from different scientific disciplines, which would require following disparate and distinct research literatures, is rapidly becoming impossible for even the most ardent readers of research publications. Text mining -- the automated extraction of information from (electronically) published sources -- could potentially fulfil an important role -- but only if we know how to harness its strengths and overcome its weaknesses. As we do not expect that the rate at which scientific results are published will decrease, text mining tools are now becoming essential in order to cope with, and derive maximum benefit from, this information explosion. In genomics, this is particularly pressing as more and more rare disease-causing variants are found and need to be understood. Not being conversant with this technology may put scientists and biomedical regulators at a severe disadvantage. In this review, we introduce the basic concepts underlying modern text mining and its applications in genomics and systems biology. We hope that this review will serve three purposes: (i) to provide a timely and useful overview of the current status of this field, including a survey of present challenges; (ii) to enable researchers to decide how and when to apply text mining tools in their own research; and (iii) to highlight how the research communities in genomics and systems biology can help to make text mining from biomedical abstracts and texts more straightforward. PMID:21106487

PKDE4J: Entity and relation extraction for public knowledge discovery.

PubMed

Song, Min; Kim, Won Chul; Lee, Dahee; Heo, Go Eun; Kang, Keun Young

2015-10-01

Due to an enormous number of scientific publications that cannot be handled manually, there is a rising interest in text-mining techniques for automated information extraction, especially in the biomedical field. Such techniques provide effective means of information search, knowledge discovery, and hypothesis generation. Most previous studies have primarily focused on the design and performance improvement of either named entity recognition or relation extraction. In this paper, we present PKDE4J, a comprehensive text-mining system that integrates dictionary-based entity extraction and rule-based relation extraction in a highly flexible and extensible framework. Starting with the Stanford CoreNLP, we developed the system to cope with multiple types of entities and relations. The system also has fairly good performance in terms of accuracy as well as the ability to configure text-processing components. We demonstrate its competitive performance by evaluating it on many corpora and found that it surpasses existing systems with average F-measures of 85% for entity extraction and 81% for relation extraction. Copyright © 2015 Elsevier Inc. All rights reserved.

PubMedPortable: A Framework for Supporting the Development of Text Mining Applications.

PubMed

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.

PubMedPortable: A Framework for Supporting the Development of Text Mining Applications

PubMed Central

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

Model of environmental life cycle assessment for coal mining operations.

PubMed

Burchart-Korol, Dorota; Fugiel, Agata; Czaplicka-Kolarz, Krystyna; Turek, Marian

2016-08-15

This paper presents a novel approach to environmental assessment of coal mining operations, which enables assessment of the factors that are both directly and indirectly affecting the environment and are associated with the production of raw materials and energy used in processes. The primary novelty of the paper is the development of a computational environmental life cycle assessment (LCA) model for coal mining operations and the application of the model for coal mining operations in Poland. The LCA model enables the assessment of environmental indicators for all identified unit processes in hard coal mines with the life cycle approach. The proposed model enables the assessment of greenhouse gas emissions (GHGs) based on the IPCC method and the assessment of damage categories, such as human health, ecosystems and resources based on the ReCiPe method. The model enables the assessment of GHGs for hard coal mining operations in three time frames: 20, 100 and 500years. The model was used to evaluate the coal mines in Poland. It was demonstrated that the largest environmental impacts in damage categories were associated with the use of fossil fuels, methane emissions and the use of electricity, processing of wastes, heat, and steel supports. It was concluded that an environmental assessment of coal mining operations, apart from direct influence from processing waste, methane emissions and drainage water, should include the use of electricity, heat and steel, particularly for steel supports. Because the model allows the comparison of environmental impact assessment for various unit processes, it can be used for all hard coal mines, not only in Poland but also in the world. This development is an important step forward in the study of the impacts of fossil fuels on the environment with the potential to mitigate the impact of the coal industry on the environment. Copyright © 2016 Elsevier B.V. All rights reserved.

Data mining for personal navigation

NASA Astrophysics Data System (ADS)

Hariharan, Gurushyam; Franti, Pasi; Mehta, Sandeep

2002-03-01

Relevance is the key in defining what data is to be extracted from the Internet. Traditionally, relevance has been defined mainly by keywords and user profiles. In this paper we discuss a fairly untouched dimension to relevance: location. Any navigational information sought by a user at large on earth is evidently governed by his location. We believe that task oriented data mining of the web amalgamated with location information is the key to providing relevant information for personal navigation. We explore the existential hurdles and propose novel approaches to tackle them. We also present naive, task-oriented data mining based approaches and their implementations in Java, to extract location based information. Ad-hoc pairing of data with coordinates (x, y) is very rare on the web. But if the same co-ordinates are converted to a logical address (state/city/street), a wide spectrum of location-based information base opens up. Hence, given the coordinates (x, y) on the earth, the scheme points to the logical address of the user. Location based information could either be picked up from fixed and known service providers (e.g. Yellow Pages) or from any arbitrary website on the Web. Once the web servers providing information relevant to the logical address are located, task oriented data mining is performed over these sites keeping in mind what information is interesting to the contemporary user. After all this, a simple data stream is provided to the user with information scaled to his convenience. The scheme has been implemented for cities of Finland.

Toward Personalized Pressure Ulcer Care Planning: Development of a Bioinformatics System for Individualized Prioritization of Clinical Pratice Guideline

DTIC Science & Technology

2016-10-01

and text data mining . A Spinal Cord Injury Pressure Ulcer and Deep tissue injury ontology, SCIPUDO, will be developed to ensure robust and extensive...on natural language programming and the need to convert text in to data for analysis. In progress c) Define Physio-MIMI based SCIPUD+ Resource...information extraction from the free text clinical note. 3) Significant Results Nothing to report 4) Other Achievements Nothing to report

Data Mining.

ERIC Educational Resources Information Center

Benoit, Gerald

2002-01-01

Discusses data mining (DM) and knowledge discovery in databases (KDD), taking the view that KDD is the larger view of the entire process, with DM emphasizing the cleaning, warehousing, mining, and visualization of knowledge discovery in databases. Highlights include algorithms; users; the Internet; text mining; and information extraction.…

Literature-based concept profiles for gene annotation: the issue of weighting.

PubMed

Jelier, Rob; Schuemie, Martijn J; Roes, Peter-Jan; van Mulligen, Erik M; Kors, Jan A

2008-05-01

Text-mining has been used to link biomedical concepts, such as genes or biological processes, to each other for annotation purposes or the generation of new hypotheses. To relate two concepts to each other several authors have used the vector space model, as vectors can be compared efficiently and transparently. Using this model, a concept is characterized by a list of associated concepts, together with weights that indicate the strength of the association. The associated concepts in the vectors and their weights are derived from a set of documents linked to the concept of interest. An important issue with this approach is the determination of the weights of the associated concepts. Various schemes have been proposed to determine these weights, but no comparative studies of the different approaches are available. Here we compare several weighting approaches in a large scale classification experiment. Three different techniques were evaluated: (1) weighting based on averaging, an empirical approach; (2) the log likelihood ratio, a test-based measure; (3) the uncertainty coefficient, an information-theory based measure. The weighting schemes were applied in a system that annotates genes with Gene Ontology codes. As the gold standard for our study we used the annotations provided by the Gene Ontology Annotation project. Classification performance was evaluated by means of the receiver operating characteristics (ROC) curve using the area under the curve (AUC) as the measure of performance. All methods performed well with median AUC scores greater than 0.84, and scored considerably higher than a binary approach without any weighting. Especially for the more specific Gene Ontology codes excellent performance was observed. The differences between the methods were small when considering the whole experiment. However, the number of documents that were linked to a concept proved to be an important variable. When larger amounts of texts were available for the generation of the concepts' vectors, the performance of the methods diverged considerably, with the uncertainty coefficient then outperforming the two other methods.

Cluster Analysis-Based Approaches for Geospatiotemporal Data Mining of Massive Data Sets for Identification of Forest Threats

Treesearch

Richard Trans Mills; Forrest M Hoffman; Jitendra Kumar; William W. Hargrove

2011-01-01

We investigate methods for geospatiotemporal data mining of multi-year land surface phenology data (250 m2 Normalized Difference Vegetation Index (NDVI) values derived from the Moderate Resolution Imaging Spectrometer (MODIS) in this study) for the conterminous United States (CONUS) as part of an early warning system for detecting threats to forest ecosystems. The...

Material flow-based economic assessment of landfill mining processes.

PubMed

Kieckhäfer, Karsten; Breitenstein, Anna; Spengler, Thomas S

2017-02-01

This paper provides an economic assessment of alternative processes for landfill mining compared to landfill aftercare with the goal of assisting landfill operators with the decision to choose between the two alternatives. A material flow-based assessment approach is developed and applied to a landfill in Germany. In addition to landfill aftercare, six alternative landfill mining processes are considered. These range from simple approaches where most of the material is incinerated or landfilled again to sophisticated technology combinations that allow for recovering highly differentiated products such as metals, plastics, glass, recycling sand, and gravel. For the alternatives, the net present value of all relevant cash flows associated with plant installation and operation, supply, recycling, and disposal of material flows, recovery of land and landfill airspace, as well as landfill closure and aftercare is computed with an extensive sensitivity analyses. The economic performance of landfill mining processes is found to be significantly influenced by the prices of thermal treatment (waste incineration as well as refuse-derived fuels incineration plant) and recovered land or airspace. The results indicate that the simple process alternatives have the highest economic potential, which contradicts the aim of recovering most of the resources. Copyright © 2016 Elsevier Ltd. All rights reserved.

Simplified process model discovery based on role-oriented genetic mining.

PubMed

Zhao, Weidong; Liu, Xi; Dai, Weihui

2014-01-01

Process mining is automated acquisition of process models from event logs. Although many process mining techniques have been developed, most of them are based on control flow. Meanwhile, the existing role-oriented process mining methods focus on correctness and integrity of roles while ignoring role complexity of the process model, which directly impacts understandability and quality of the model. To address these problems, we propose a genetic programming approach to mine the simplified process model. Using a new metric of process complexity in terms of roles as the fitness function, we can find simpler process models. The new role complexity metric of process models is designed from role cohesion and coupling, and applied to discover roles in process models. Moreover, the higher fitness derived from role complexity metric also provides a guideline for redesigning process models. Finally, we conduct case study and experiments to show that the proposed method is more effective for streamlining the process by comparing with related studies.

To build a mine: Prospect to product

NASA Technical Reports Server (NTRS)

Gertsch, Richard E.

1992-01-01

The terrestrial definition of ore is a quantity of earth materials containing a mineral that can be extracted at a profit. While a space-based resource-gathering operation may well be driven by other motives, such an operation should have the most favorable cost-benefit ratio possible. To this end, principles and procedures already tested by the stringent requirements of the profit motive should guide the selection, design, construction, and operation of a space-based mine. Proceeding from project initiation to a fully operational mine requires several interacting and overlapping steps, which are designed to facilitate the decision process and insure economic viability. The steps to achieve a fully operational mine are outlined. Presuming that the approach to developing nonterrestrial resources will parallel that for developing mineral resources on Earth, we can speculate on some of the problems associated with developing lunar and asteroidal resources. The baseline for our study group was a small lunar mine and oxygen extraction facility. The development of this facility is described in accordance with the steps outlined.

Ranked centroid projection: a data visualization approach with self-organizing maps.

PubMed

Yen, G G; Wu, Z

2008-02-01

The self-organizing map (SOM) is an efficient tool for visualizing high-dimensional data. In this paper, the clustering and visualization capabilities of the SOM, especially in the analysis of textual data, i.e., document collections, are reviewed and further developed. A novel clustering and visualization approach based on the SOM is proposed for the task of text mining. The proposed approach first transforms the document space into a multidimensional vector space by means of document encoding. Afterwards, a growing hierarchical SOM (GHSOM) is trained and used as a baseline structure to automatically produce maps with various levels of detail. Following the GHSOM training, the new projection method, namely the ranked centroid projection (RCP), is applied to project the input vectors to a hierarchy of 2-D output maps. The RCP is used as a data analysis tool as well as a direct interface to the data. In a set of simulations, the proposed approach is applied to an illustrative data set and two real-world scientific document collections to demonstrate its applicability.

A gossip based information fusion protocol for distributed frequent itemset mining

NASA Astrophysics Data System (ADS)

Sohrabi, Mohammad Karim

2018-07-01

The computational complexity, huge memory space requirement, and time-consuming nature of frequent pattern mining process are the most important motivations for distribution and parallelization of this mining process. On the other hand, the emergence of distributed computational and operational environments, which causes the production and maintenance of data on different distributed data sources, makes the parallelization and distribution of the knowledge discovery process inevitable. In this paper, a gossip based distributed itemset mining (GDIM) algorithm is proposed to extract frequent itemsets, which are special types of frequent patterns, in a wireless sensor network environment. In this algorithm, local frequent itemsets of each sensor are extracted using a bit-wise horizontal approach (LHPM) from the nodes which are clustered using a leach-based protocol. Heads of clusters exploit a gossip based protocol in order to communicate each other to find the patterns which their global support is equal to or more than the specified support threshold. Experimental results show that the proposed algorithm outperforms the best existing gossip based algorithm in term of execution time.

Ask and Ye Shall Receive? Automated Text Mining of Michigan Capital Facility Finance Bond Election Proposals to Identify Which Topics Are Associated with Bond Passage and Voter Turnout

ERIC Educational Resources Information Center

Bowers, Alex J.; Chen, Jingjing

2015-01-01

The purpose of this study is to bring together recent innovations in the research literature around school district capital facility finance, municipal bond elections, statistical models of conditional time-varying outcomes, and data mining algorithms for automated text mining of election ballot proposals to examine the factors that influence the…

A survey on annotation tools for the biomedical literature.

PubMed

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.

Systematic Review of Data Mining Applications in Patient-Centered Mobile-Based Information Systems.

PubMed

Fallah, Mina; Niakan Kalhori, Sharareh R

2017-10-01

Smartphones represent a promising technology for patient-centered healthcare. It is claimed that data mining techniques have improved mobile apps to address patients' needs at subgroup and individual levels. This study reviewed the current literature regarding data mining applications in patient-centered mobile-based information systems. We systematically searched PubMed, Scopus, and Web of Science for original studies reported from 2014 to 2016. After screening 226 records at the title/abstract level, the full texts of 92 relevant papers were retrieved and checked against inclusion criteria. Finally, 30 papers were included in this study and reviewed. Data mining techniques have been reported in development of mobile health apps for three main purposes: data analysis for follow-up and monitoring, early diagnosis and detection for screening purpose, classification/prediction of outcomes, and risk calculation (n = 27); data collection (n = 3); and provision of recommendations (n = 2). The most accurate and frequently applied data mining method was support vector machine; however, decision tree has shown superior performance to enhance mobile apps applied for patients' self-management. Embedded data-mining-based feature in mobile apps, such as case detection, prediction/classification, risk estimation, or collection of patient data, particularly during self-management, would save, apply, and analyze patient data during and after care. More intelligent methods, such as artificial neural networks, fuzzy logic, and genetic algorithms, and even the hybrid methods may result in more patients-centered recommendations, providing education, guidance, alerts, and awareness of personalized output.

A systems biology approach to the global analysis of transcription factors in colorectal cancer.

PubMed

Pradhan, Meeta P; Prasad, Nagendra K A; Palakal, Mathew J

2012-08-01

Biological entities do not perform in isolation, and often, it is the nature and degree of interactions among numerous biological entities which ultimately determines any final outcome. Hence, experimental data on any single biological entity can be of limited value when considered only in isolation. To address this, we propose that augmenting individual entity data with the literature will not only better define the entity's own significance but also uncover relationships with novel biological entities.To test this notion, we developed a comprehensive text mining and computational methodology that focused on discovering new targets of one class of molecular entities, transcription factors (TF), within one particular disease, colorectal cancer (CRC). We used 39 molecular entities known to be associated with CRC along with six colorectal cancer terms as the bait list, or list of search terms, for mining the biomedical literature to identify CRC-specific genes and proteins. Using the literature-mined data, we constructed a global TF interaction network for CRC. We then developed a multi-level, multi-parametric methodology to identify TFs to CRC. The small bait list, when augmented with literature-mined data, identified a large number of biological entities associated with CRC. The relative importance of these TF and their associated modules was identified using functional and topological features. Additional validation of these highly-ranked TF using the literature strengthened our findings. Some of the novel TF that we identified were: SLUG, RUNX1, IRF1, HIF1A, ATF-2, ABL1, ELK-1 and GATA-1. Some of these TFs are associated with functional modules in known pathways of CRC, including the Beta-catenin/development, immune response, transcription, and DNA damage pathways. Our methodology of using text mining data and a multi-level, multi-parameter scoring technique was able to identify both known and novel TF that have roles in CRC. Starting with just one TF (SMAD3) in the bait list, the literature mining process identified an additional 116 CRC-associated TFs. Our network-based analysis showed that these TFs all belonged to any of 13 major functional groups that are known to play important roles in CRC. Among these identified TFs, we obtained a novel six-node module consisting of ATF2-P53-JNK1-ELK1-EPHB2-HIF1A, from which the novel JNK1-ELK1 association could potentially be a significant marker for CRC.

Efficient chemical-disease identification and relationship extraction using Wikipedia to improve recall.

PubMed

Lowe, Daniel M; O'Boyle, Noel M; Sayle, Roger A

2016-01-01

Awareness of the adverse effects of chemicals is important in biomedical research and healthcare. Text mining can allow timely and low-cost extraction of this knowledge from the biomedical literature. We extended our text mining solution, LeadMine, to identify diseases and chemical-induced disease relationships (CIDs). LeadMine is a dictionary/grammar-based entity recognizer and was used to recognize and normalize both chemicals and diseases to Medical Subject Headings (MeSH) IDs. The disease lexicon was obtained from three sources: MeSH, the Disease Ontology and Wikipedia. The Wikipedia dictionary was derived from pages with a disease/symptom box, or those where the page title appeared in the lexicon. Composite entities (e.g. heart and lung disease) were detected and mapped to their composite MeSH IDs. For CIDs, we developed a simple pattern-based system to find relationships within the same sentence. Our system was evaluated in the BioCreative V Chemical-Disease Relation task and achieved very good results for both disease concept ID recognition (F1-score: 86.12%) and CIDs (F1-score: 52.20%) on the test set. As our system was over an order of magnitude faster than other solutions evaluated on the task, we were able to apply the same system to the entirety of MEDLINE allowing us to extract a collection of over 250 000 distinct CIDs. © The Author(s) 2016. Published by Oxford University Press.

Impact of translation on named-entity recognition in radiology texts

PubMed Central

Pedro, Vasco

2017-01-01

Abstract Radiology reports describe the results of radiography procedures and have the potential of being a useful source of information which can bring benefits to health care systems around the world. One way to automatically extract information from the reports is by using Text Mining tools. The problem is that these tools are mostly developed for English and reports are usually written in the native language of the radiologist, which is not necessarily English. This creates an obstacle to the sharing of Radiology information between different communities. This work explores the solution of translating the reports to English before applying the Text Mining tools, probing the question of what translation approach should be used. We created MRRAD (Multilingual Radiology Research Articles Dataset), a parallel corpus of Portuguese research articles related to Radiology and a number of alternative translations (human, automatic and semi-automatic) to English. This is a novel corpus which can be used to move forward the research on this topic. Using MRRAD we studied which kind of automatic or semi-automatic translation approach is more effective on the Named-entity recognition task of finding RadLex terms in the English version of the articles. Considering the terms extracted from human translations as our gold standard, we calculated how similar to this standard were the terms extracted using other translations. We found that a completely automatic translation approach using Google leads to F-scores (between 0.861 and 0.868, depending on the extraction approach) similar to the ones obtained through a more expensive semi-automatic translation approach using Unbabel (between 0.862 and 0.870). To better understand the results we also performed a qualitative analysis of the type of errors found in the automatic and semi-automatic translations. Database URL: https://github.com/lasigeBioTM/MRRAD PMID:29220455

Identifying Liver Cancer and Its Relations with Diseases, Drugs, and Genes: A Literature-Based Approach

PubMed Central

Song, Min

2016-01-01

In biomedicine, scientific literature is a valuable source for knowledge discovery. Mining knowledge from textual data has become an ever important task as the volume of scientific literature is growing unprecedentedly. In this paper, we propose a framework for examining a certain disease based on existing information provided by scientific literature. Disease-related entities that include diseases, drugs, and genes are systematically extracted and analyzed using a three-level network-based approach. A paper-entity network and an entity co-occurrence network (macro-level) are explored and used to construct six entity specific networks (meso-level). Important diseases, drugs, and genes as well as salient entity relations (micro-level) are identified from these networks. Results obtained from the literature-based literature mining can serve to assist clinical applications. PMID:27195695

Fusion and Gaussian mixture based classifiers for SONAR data

NASA Astrophysics Data System (ADS)

Kotari, Vikas; Chang, KC

2011-06-01

Underwater mines are inexpensive and highly effective weapons. They are difficult to detect and classify. Hence detection and classification of underwater mines is essential for the safety of naval vessels. This necessitates a formulation of highly efficient classifiers and detection techniques. Current techniques primarily focus on signals from one source. Data fusion is known to increase the accuracy of detection and classification. In this paper, we formulated a fusion-based classifier and a Gaussian mixture model (GMM) based classifier for classification of underwater mines. The emphasis has been on sound navigation and ranging (SONAR) signals due to their extensive use in current naval operations. The classifiers have been tested on real SONAR data obtained from University of California Irvine (UCI) repository. The performance of both GMM based classifier and fusion based classifier clearly demonstrate their superior classification accuracy over conventional single source cases and validate our approach.

Using Open Web APIs in Teaching Web Mining

ERIC Educational Resources Information Center

Chen, Hsinchun; Li, Xin; Chau, M.; Ho, Yi-Jen; Tseng, Chunju

2009-01-01

With the advent of the World Wide Web, many business applications that utilize data mining and text mining techniques to extract useful business information on the Web have evolved from Web searching to Web mining. It is important for students to acquire knowledge and hands-on experience in Web mining during their education in information systems…

Process mining in oncology using the MIMIC-III dataset

NASA Astrophysics Data System (ADS)

Prima Kurniati, Angelina; Hall, Geoff; Hogg, David; Johnson, Owen

2018-03-01

Process mining is a data analytics approach to discover and analyse process models based on the real activities captured in information systems. There is a growing body of literature on process mining in healthcare, including oncology, the study of cancer. In earlier work we found 37 peer-reviewed papers describing process mining research in oncology with a regular complaint being the limited availability and accessibility of datasets with suitable information for process mining. Publicly available datasets are one option and this paper describes the potential to use MIMIC-III, for process mining in oncology. MIMIC-III is a large open access dataset of de-identified patient records. There are 134 publications listed as using the MIMIC dataset, but none of them have used process mining. The MIMIC-III dataset has 16 event tables which are potentially useful for process mining and this paper demonstrates the opportunities to use MIMIC-III for process mining in oncology. Our research applied the L* lifecycle method to provide a worked example showing how process mining can be used to analyse cancer pathways. The results and data quality limitations are discussed along with opportunities for further work and reflection on the value of MIMIC-III for reproducible process mining research.

Using Workflows to Explore and Optimise Named Entity Recognition for Chemistry

PubMed Central

Kolluru, BalaKrishna; Hawizy, Lezan; Murray-Rust, Peter; Tsujii, Junichi; Ananiadou, Sophia

2011-01-01

Chemistry text mining tools should be interoperable and adaptable regardless of system-level implementation, installation or even programming issues. We aim to abstract the functionality of these tools from the underlying implementation via reconfigurable workflows for automatically identifying chemical names. To achieve this, we refactored an established named entity recogniser (in the chemistry domain), OSCAR and studied the impact of each component on the net performance. We developed two reconfigurable workflows from OSCAR using an interoperable text mining framework, U-Compare. These workflows can be altered using the drag-&-drop mechanism of the graphical user interface of U-Compare. These workflows also provide a platform to study the relationship between text mining components such as tokenisation and named entity recognition (using maximum entropy Markov model (MEMM) and pattern recognition based classifiers). Results indicate that, for chemistry in particular, eliminating noise generated by tokenisation techniques lead to a slightly better performance than others, in terms of named entity recognition (NER) accuracy. Poor tokenisation translates into poorer input to the classifier components which in turn leads to an increase in Type I or Type II errors, thus, lowering the overall performance. On the Sciborg corpus, the workflow based system, which uses a new tokeniser whilst retaining the same MEMM component, increases the F-score from 82.35% to 84.44%. On the PubMed corpus, it recorded an F-score of 84.84% as against 84.23% by OSCAR. PMID:21633495

Collaborative biocuration--text-mining development task for document prioritization for curation.

PubMed

Wiegers, Thomas C; Davis, Allan Peter; Mattingly, Carolyn J

2012-01-01

The Critical Assessment of Information Extraction systems in Biology (BioCreAtIvE) challenge evaluation is a community-wide effort for evaluating text mining and information extraction systems for the biological domain. The 'BioCreative Workshop 2012' subcommittee identified three areas, or tracks, that comprised independent, but complementary aspects of data curation in which they sought community input: literature triage (Track I); curation workflow (Track II) and text mining/natural language processing (NLP) systems (Track III). Track I participants were invited to develop tools or systems that would effectively triage and prioritize articles for curation and present results in a prototype web interface. Training and test datasets were derived from the Comparative Toxicogenomics Database (CTD; http://ctdbase.org) and consisted of manuscripts from which chemical-gene-disease data were manually curated. A total of seven groups participated in Track I. For the triage component, the effectiveness of participant systems was measured by aggregate gene, disease and chemical 'named-entity recognition' (NER) across articles; the effectiveness of 'information retrieval' (IR) was also measured based on 'mean average precision' (MAP). Top recall scores for gene, disease and chemical NER were 49, 65 and 82%, respectively; the top MAP score was 80%. Each participating group also developed a prototype web interface; these interfaces were evaluated based on functionality and ease-of-use by CTD's biocuration project manager. In this article, we present a detailed description of the challenge and a summary of the results.

Using workflows to explore and optimise named entity recognition for chemistry.

PubMed

Kolluru, Balakrishna; Hawizy, Lezan; Murray-Rust, Peter; Tsujii, Junichi; Ananiadou, Sophia

2011-01-01

Chemistry text mining tools should be interoperable and adaptable regardless of system-level implementation, installation or even programming issues. We aim to abstract the functionality of these tools from the underlying implementation via reconfigurable workflows for automatically identifying chemical names. To achieve this, we refactored an established named entity recogniser (in the chemistry domain), OSCAR and studied the impact of each component on the net performance. We developed two reconfigurable workflows from OSCAR using an interoperable text mining framework, U-Compare. These workflows can be altered using the drag-&-drop mechanism of the graphical user interface of U-Compare. These workflows also provide a platform to study the relationship between text mining components such as tokenisation and named entity recognition (using maximum entropy Markov model (MEMM) and pattern recognition based classifiers). Results indicate that, for chemistry in particular, eliminating noise generated by tokenisation techniques lead to a slightly better performance than others, in terms of named entity recognition (NER) accuracy. Poor tokenisation translates into poorer input to the classifier components which in turn leads to an increase in Type I or Type II errors, thus, lowering the overall performance. On the Sciborg corpus, the workflow based system, which uses a new tokeniser whilst retaining the same MEMM component, increases the F-score from 82.35% to 84.44%. On the PubMed corpus, it recorded an F-score of 84.84% as against 84.23% by OSCAR.

Unsupervised user similarity mining in GSM sensor networks.

PubMed

Shad, Shafqat Ali; Chen, Enhong

2013-01-01

Mobility data has attracted the researchers for the past few years because of its rich context and spatiotemporal nature, where this information can be used for potential applications like early warning system, route prediction, traffic management, advertisement, social networking, and community finding. All the mentioned applications are based on mobility profile building and user trend analysis, where mobility profile building is done through significant places extraction, user's actual movement prediction, and context awareness. However, significant places extraction and user's actual movement prediction for mobility profile building are a trivial task. In this paper, we present the user similarity mining-based methodology through user mobility profile building by using the semantic tagging information provided by user and basic GSM network architecture properties based on unsupervised clustering approach. As the mobility information is in low-level raw form, our proposed methodology successfully converts it to a high-level meaningful information by using the cell-Id location information rather than previously used location capturing methods like GPS, Infrared, and Wifi for profile mining and user similarity mining.

Medical Named Entity Recognition for Indonesian Language Using Word Representations

NASA Astrophysics Data System (ADS)

Rahman, Arief

2018-03-01

Nowadays, Named Entity Recognition (NER) system is used in medical texts to obtain important medical information, like diseases, symptoms, and drugs. While most NER systems are applied to formal medical texts, informal ones like those from social media (also called semi-formal texts) are starting to get recognition as a gold mine for medical information. We propose a theoretical Named Entity Recognition (NER) model for semi-formal medical texts in our medical knowledge management system by comparing two kinds of word representations: cluster-based word representation and distributed representation.

Improving Hospital-wide Patient Scheduling Decisions by Clinical Pathway Mining.

PubMed

Gartner, Daniel; Arnolds, Ines V; Nickel, Stefan

2015-01-01

Recent research has highlighted the need for solving hospital-wide patient scheduling problems. Inpatient scheduling, patient activities have to be scheduled on scarce hospital resources such that temporal relations between activities (e.g. for recovery times) are ensured. Common objectives are, among others, the minimization of the length of stay (LOS). In this paper, we consider a hospital-wide patient scheduling problem with LOS minimization based on uncertain clinical pathways. We approach the problem in three stages: First, we learn most likely clinical pathways using a sequential pattern mining approach. Second, we provide a mathematical model for patient scheduling and finally, we combine the two approaches. In an experimental study carried out using real-world data, we show that our approach outperforms baseline approaches on two metrics.

FREQUENT SUBGRAPH MINING OF PERSONALIZED SIGNALING PATHWAY NETWORKS GROUPS PATIENTS WITH FREQUENTLY DYSREGULATED DISEASE PATHWAYS AND PREDICTS PROGNOSIS.

PubMed

Durmaz, Arda; Henderson, Tim A D; Brubaker, Douglas; Bebek, Gurkan

2017-01-01

Large scale genomics studies have generated comprehensive molecular characterization of numerous cancer types. Subtypes for many tumor types have been established; however, these classifications are based on molecular characteristics of a small gene sets with limited power to detect dysregulation at the patient level. We hypothesize that frequent graph mining of pathways to gather pathways functionally relevant to tumors can characterize tumor types and provide opportunities for personalized therapies. In this study we present an integrative omics approach to group patients based on their altered pathway characteristics and show prognostic differences within breast cancer (p < 9:57E - 10) and glioblastoma multiforme (p < 0:05) patients. We were able validate this approach in secondary RNA-Seq datasets with p < 0:05 and p < 0:01 respectively. We also performed pathway enrichment analysis to further investigate the biological relevance of dysregulated pathways. We compared our approach with network-based classifier algorithms and showed that our unsupervised approach generates more robust and biologically relevant clustering whereas previous approaches failed to report specific functions for similar patient groups or classify patients into prognostic groups. These results could serve as a means to improve prognosis for future cancer patients, and to provide opportunities for improved treatment options and personalized interventions. The proposed novel graph mining approach is able to integrate PPI networks with gene expression in a biologically sound approach and cluster patients in to clinically distinct groups. We have utilized breast cancer and glioblastoma multiforme datasets from microarray and RNA-Seq platforms and identified disease mechanisms differentiating samples. Supplementary methods, figures, tables and code are available at https://github.com/bebeklab/dysprog.

Tree-based approach for exploring marine spatial patterns with raster datasets.

PubMed

Liao, Xiaohan; Xue, Cunjin; Su, Fenzhen

2017-01-01

From multiple raster datasets to spatial association patterns, the data-mining technique is divided into three subtasks, i.e., raster dataset pretreatment, mining algorithm design, and spatial pattern exploration from the mining results. Comparison with the former two subtasks reveals that the latter remains unresolved. Confronted with the interrelated marine environmental parameters, we propose a Tree-based Approach for eXploring Marine Spatial Patterns with multiple raster datasets called TAXMarSP, which includes two models. One is the Tree-based Cascading Organization Model (TCOM), and the other is the Spatial Neighborhood-based CAlculation Model (SNCAM). TCOM designs the "Spatial node→Pattern node" from top to bottom layers to store the table-formatted frequent patterns. Together with TCOM, SNCAM considers the spatial neighborhood contributions to calculate the pattern-matching degree between the specified marine parameters and the table-formatted frequent patterns and then explores the marine spatial patterns. Using the prevalent quantification Apriori algorithm and a real remote sensing dataset from January 1998 to December 2014, a successful application of TAXMarSP to marine spatial patterns in the Pacific Ocean is described, and the obtained marine spatial patterns present not only the well-known but also new patterns to Earth scientists.

Ergonomics in the arctic - a study and checklist for heavy machinery in open pit mining.

PubMed

Reiman, Arto; Sormunen, Erja; Morris, Drew

2016-11-22

Heavy mining vehicle operators at arctic mines have a high risk of discomfort, musculoskeletal disorders and occupational accidents. There is a need for tailored approaches and safety management tools that take into account the specific characteristics of arctic work environments. The aim of this study was to develop a holistic evaluation tool for heavy mining vehicles and operator well-being in arctic mine environments. Data collection was based on design science principles and included literature review, expert observations and participatory ergonomic sessions. As a result of this study, a systemic checklist was developed and tested by eight individuals in a 350-employee mining environment. The checklist includes sections for evaluating vehicle specific ergonomic and safety aspects from a technological point of view and for checking if the work has been arranged so that it can be performed safely and fluently from an employee's point of view.

Cart'Eaux: an automatic mapping procedure for wastewater networks using machine learning and data mining

NASA Astrophysics Data System (ADS)

Bailly, J. S.; Delenne, C.; Chahinian, N.; Bringay, S.; Commandré, B.; Chaumont, M.; Derras, M.; Deruelle, L.; Roche, M.; Rodriguez, F.; Subsol, G.; Teisseire, M.

2017-12-01

In France, local government institutions must establish a detailed description of wastewater networks. The information should be available, but it remains fragmented (different formats held by different stakeholders) and incomplete. In the "Cart'Eaux" project, a multidisciplinary team, including an industrial partner, develops a global methodology using Machine Learning and Data Mining approaches applied to various types of large data to recover information in the aim of mapping urban sewage systems for hydraulic modelling. Deep-learning is first applied using a Convolution Neural Network to localize manhole covers on 5 cm resolution aerial RGB images. The detected manhole covers are then automatically connected using a tree-shaped graph constrained by industry rules. Based on a Delaunay triangulation, connections are chosen to minimize a cost function depending on pipe length, slope and possible intersection with roads or buildings. A stochastic version of this algorithm is currently being developed to account for positional uncertainty and detection errors, and generate sets of probable networks. As more information is required for hydraulic modeling (slopes, diameters, materials, etc.), text data mining is used to extract network characteristics from data posted on the Web or available through governmental or specific databases. Using an appropriate list of keywords, the web is scoured for documents which are saved in text format. The thematic entities are identified and linked to the surrounding spatial and temporal entities. The methodology is developed and tested on two towns in southern France. The primary results are encouraging: 54% of manhole covers are detected with few false detections, enabling the reconstruction of probable networks. The data mining results are still being investigated. It is clear at this stage that getting numerical values on specific pipes will be challenging. Thus, when no information is found, decision rules will be used to assign admissible numerical values to enable the final hydraulic modelling. Consequently, sensitivity analysis of the hydraulic model will be performed to take into account the uncertainty associated with each piece of information. Project funded by the European Regional Development Fund and the Occitanie Region.

Pedoinformatics Approach to Soil Text Analytics

NASA Astrophysics Data System (ADS)

Furey, J.; Seiter, J.; Davis, A.

2017-12-01

The several extant schema for the classification of soils rely on differing criteria, but the major soil science taxonomies, including the United States Department of Agriculture (USDA) and the international harmonized World Reference Base for Soil Resources systems, are based principally on inferred pedogenic properties. These taxonomies largely result from compiled individual observations of soil morphologies within soil profiles, and the vast majority of this pedologic information is contained in qualitative text descriptions. We present text mining analyses of hundreds of gigabytes of parsed text and other data in the digitally available USDA soil taxonomy documentation, the Soil Survey Geographic (SSURGO) database, and the National Cooperative Soil Survey (NCSS) soil characterization database. These analyses implemented iPython calls to Gensim modules for topic modelling, with latent semantic indexing completed down to the lowest taxon level (soil series) paragraphs. Via a custom extension of the Natural Language Toolkit (NLTK), approximately one percent of the USDA soil series descriptions were used to train a classifier for the remainder of the documents, essentially by treating soil science words as comprising a novel language. While location-specific descriptors at the soil series level are amenable to geomatics methods, unsupervised clustering of the occurrence of other soil science words did not closely follow the usual hierarchy of soil taxa. We present preliminary phrasal analyses that may account for some of these effects.

Are Female Applicants Disadvantaged in National Institutes of Health Peer Review? Combining Algorithmic Text Mining and Qualitative Methods to Detect Evaluative Differences in R01 Reviewers' Critiques.

PubMed

Magua, Wairimu; Zhu, Xiaojin; Bhattacharya, Anupama; Filut, Amarette; Potvien, Aaron; Leatherberry, Renee; Lee, You-Geon; Jens, Madeline; Malikireddy, Dastagiri; Carnes, Molly; Kaatz, Anna

2017-05-01

Women are less successful than men in renewing R01 grants from the National Institutes of Health. Continuing to probe text mining as a tool to identify gender bias in peer review, we used algorithmic text mining and qualitative analysis to examine a sample of critiques from men's and women's R01 renewal applications previously analyzed by counting and comparing word categories. We analyzed 241 critiques from 79 Summary Statements for 51 R01 renewals awarded to 45 investigators (64% male, 89% white, 80% PhD) at the University of Wisconsin-Madison between 2010 and 2014. We used latent Dirichlet allocation to discover evaluative "topics" (i.e., words that co-occur with high probability). We then qualitatively examined the context in which evaluative words occurred for male and female investigators. We also examined sex differences in assigned scores controlling for investigator productivity. Text analysis results showed that male investigators were described as "leaders" and "pioneers" in their "fields," with "highly innovative" and "highly significant research." By comparison, female investigators were characterized as having "expertise" and working in "excellent" environments. Applications from men received significantly better priority, approach, and significance scores, which could not be accounted for by differences in productivity. Results confirm our previous analyses suggesting that gender stereotypes operate in R01 grant peer review. Reviewers may more easily view male than female investigators as scientific leaders with significant and innovative research, and score their applications more competitively. Such implicit bias may contribute to sex differences in award rates for R01 renewals.

Are Female Applicants Disadvantaged in National Institutes of Health Peer Review? Combining Algorithmic Text Mining and Qualitative Methods to Detect Evaluative Differences in R01 Reviewers' Critiques

PubMed Central

Magua, Wairimu; Zhu, Xiaojin; Bhattacharya, Anupama; Filut, Amarette; Potvien, Aaron; Leatherberry, Renee; Lee, You-Geon; Jens, Madeline; Malikireddy, Dastagiri; Carnes, Molly

2017-01-01

Abstract Background: Women are less successful than men in renewing R01 grants from the National Institutes of Health. Continuing to probe text mining as a tool to identify gender bias in peer review, we used algorithmic text mining and qualitative analysis to examine a sample of critiques from men's and women's R01 renewal applications previously analyzed by counting and comparing word categories. Methods: We analyzed 241 critiques from 79 Summary Statements for 51 R01 renewals awarded to 45 investigators (64% male, 89% white, 80% PhD) at the University of Wisconsin-Madison between 2010 and 2014. We used latent Dirichlet allocation to discover evaluative “topics” (i.e., words that co-occur with high probability). We then qualitatively examined the context in which evaluative words occurred for male and female investigators. We also examined sex differences in assigned scores controlling for investigator productivity. Results: Text analysis results showed that male investigators were described as “leaders” and “pioneers” in their “fields,” with “highly innovative” and “highly significant research.” By comparison, female investigators were characterized as having “expertise” and working in “excellent” environments. Applications from men received significantly better priority, approach, and significance scores, which could not be accounted for by differences in productivity. Conclusions: Results confirm our previous analyses suggesting that gender stereotypes operate in R01 grant peer review. Reviewers may more easily view male than female investigators as scientific leaders with significant and innovative research, and score their applications more competitively. Such implicit bias may contribute to sex differences in award rates for R01 renewals. PMID:28281870

BioTextQuest: a web-based biomedical text mining suite for concept discovery.

PubMed

Papanikolaou, Nikolas; Pafilis, Evangelos; Nikolaou, Stavros; Ouzounis, Christos A; Iliopoulos, Ioannis; Promponas, Vasilis J

2011-12-01

BioTextQuest combines automated discovery of significant terms in article clusters with structured knowledge annotation, via Named Entity Recognition services, offering interactive user-friendly visualization. A tag-cloud-based illustration of terms labeling each document cluster are semantically annotated according to the biological entity, and a list of document titles enable users to simultaneously compare terms and documents of each cluster, facilitating concept association and hypothesis generation. BioTextQuest allows customization of analysis parameters, e.g. clustering/stemming algorithms, exclusion of documents/significant terms, to better match the biological question addressed. http://biotextquest.biol.ucy.ac.cy vprobon@ucy.ac.cy; iliopj@med.uoc.gr Supplementary data are available at Bioinformatics online.

From data towards knowledge: revealing the architecture of signaling systems by unifying knowledge mining and data mining of systematic perturbation data.

PubMed

Lu, Songjian; Jin, Bo; Cowart, L Ashley; Lu, Xinghua

2013-01-01

Genetic and pharmacological perturbation experiments, such as deleting a gene and monitoring gene expression responses, are powerful tools for studying cellular signal transduction pathways. However, it remains a challenge to automatically derive knowledge of a cellular signaling system at a conceptual level from systematic perturbation-response data. In this study, we explored a framework that unifies knowledge mining and data mining towards the goal. The framework consists of the following automated processes: 1) applying an ontology-driven knowledge mining approach to identify functional modules among the genes responding to a perturbation in order to reveal potential signals affected by the perturbation; 2) applying a graph-based data mining approach to search for perturbations that affect a common signal; and 3) revealing the architecture of a signaling system by organizing signaling units into a hierarchy based on their relationships. Applying this framework to a compendium of yeast perturbation-response data, we have successfully recovered many well-known signal transduction pathways; in addition, our analysis has led to many new hypotheses regarding the yeast signal transduction system; finally, our analysis automatically organized perturbed genes as a graph reflecting the architecture of the yeast signaling system. Importantly, this framework transformed molecular findings from a gene level to a conceptual level, which can be readily translated into computable knowledge in the form of rules regarding the yeast signaling system, such as "if genes involved in the MAPK signaling are perturbed, genes involved in pheromone responses will be differentially expressed."

Data Mining in Institutional Economics Tasks

NASA Astrophysics Data System (ADS)

Kirilyuk, Igor; Kuznetsova, Anna; Senko, Oleg

2018-02-01

The paper discusses problems associated with the use of data mining tools to study discrepancies between countries with different types of institutional matrices by variety of potential explanatory variables: climate, economic or infrastructure indicators. An approach is presented which is based on the search of statistically valid regularities describing the dependence of the institutional type on a single variable or a pair of variables. Examples of regularities are given.

The Role of Work Experiences in College Student Leadership Development: Evidence from a National Dataset and a Text Mining Approach to Examining Beliefs about Leadership

ERIC Educational Resources Information Center

Lewis, Jonathan S.

2017-01-01

Paid employment is one of the most common extracurricular activities among full-time undergraduates, and an array of studies has attempted to measure its impact. Methodological concerns with the extant literature, however, make it difficult to draw reliable conclusions. Furthermore, the research on working college students has little to say about…

Citation-related reliability analysis for a pilot sample of underground coal mines.

PubMed

Kinilakodi, Harisha; Grayson, R Larry

2011-05-01

The scrutiny of underground coal mine safety was heightened because of the disasters that occurred in 2006-2007, and more recently in 2010. In the aftermath of the 2006 incidents, the U.S. Congress passed the Mine Improvement and New Emergency Response Act of 2006 (MINER Act), which strengthened the existing regulations and mandated new laws to address various issues related to emergency preparedness and response, escape from an emergency situation, and protection of miners. The National Mining Association-sponsored Mine Safety Technology and Training Commission study highlighted the role of risk management in identifying and controlling major hazards, which are elements that could come together and cause a mine disaster. In 2007 MSHA revised its approach to the "Pattern of Violations" (POV) process in order to target unsafe mines and then force them to remediate conditions in their mines. The POV approach has certain limitations that make it difficult for it to be enforced. One very understandable way to focus on removing threats from major-hazard conditions is to use citation-related reliability analysis. The citation reliability approach, which focuses on the probability of not getting a citation on a given inspector day, is considered an analogue to the maintenance reliability approach, which many mine operators understand and use. In this study, the citation reliability approach was applied to a stratified random sample of 31 underground coal mines to examine its potential for broader application. The results clearly show the best-performing and worst-performing mines for compliance with mine safety standards, and they highlight differences among different mine sizes. Copyright © 2010 Elsevier Ltd. All rights reserved.

Proactive Supply Chain Performance Management with Predictive Analytics

PubMed Central

Stefanovic, Nenad

2014-01-01

Today's business climate requires supply chains to be proactive rather than reactive, which demands a new approach that incorporates data mining predictive analytics. This paper introduces a predictive supply chain performance management model which combines process modelling, performance measurement, data mining models, and web portal technologies into a unique model. It presents the supply chain modelling approach based on the specialized metamodel which allows modelling of any supply chain configuration and at different level of details. The paper also presents the supply chain semantic business intelligence (BI) model which encapsulates data sources and business rules and includes the data warehouse model with specific supply chain dimensions, measures, and KPIs (key performance indicators). Next, the paper describes two generic approaches for designing the KPI predictive data mining models based on the BI semantic model. KPI predictive models were trained and tested with a real-world data set. Finally, a specialized analytical web portal which offers collaborative performance monitoring and decision making is presented. The results show that these models give very accurate KPI projections and provide valuable insights into newly emerging trends, opportunities, and problems. This should lead to more intelligent, predictive, and responsive supply chains capable of adapting to future business environment. PMID:25386605

Proactive supply chain performance management with predictive analytics.

PubMed

Stefanovic, Nenad

2014-01-01

Today's business climate requires supply chains to be proactive rather than reactive, which demands a new approach that incorporates data mining predictive analytics. This paper introduces a predictive supply chain performance management model which combines process modelling, performance measurement, data mining models, and web portal technologies into a unique model. It presents the supply chain modelling approach based on the specialized metamodel which allows modelling of any supply chain configuration and at different level of details. The paper also presents the supply chain semantic business intelligence (BI) model which encapsulates data sources and business rules and includes the data warehouse model with specific supply chain dimensions, measures, and KPIs (key performance indicators). Next, the paper describes two generic approaches for designing the KPI predictive data mining models based on the BI semantic model. KPI predictive models were trained and tested with a real-world data set. Finally, a specialized analytical web portal which offers collaborative performance monitoring and decision making is presented. The results show that these models give very accurate KPI projections and provide valuable insights into newly emerging trends, opportunities, and problems. This should lead to more intelligent, predictive, and responsive supply chains capable of adapting to future business environment.

PuReD-MCL: a graph-based PubMed document clustering methodology.

PubMed

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/

Text Mining of Journal Articles for Sleep Disorder Terminologies.

PubMed

Lam, Calvin; Lai, Fu-Chih; Wang, Chia-Hui; Lai, Mei-Hsin; Hsu, Nanly; Chung, Min-Huey

2016-01-01

Research on publication trends in journal articles on sleep disorders (SDs) and the associated methodologies by using text mining has been limited. The present study involved text mining for terms to determine the publication trends in sleep-related journal articles published during 2000-2013 and to identify associations between SD and methodology terms as well as conducting statistical analyses of the text mining findings. SD and methodology terms were extracted from 3,720 sleep-related journal articles in the PubMed database by using MetaMap. The extracted data set was analyzed using hierarchical cluster analyses and adjusted logistic regression models to investigate publication trends and associations between SD and methodology terms. MetaMap had a text mining precision, recall, and false positive rate of 0.70, 0.77, and 11.51%, respectively. The most common SD term was breathing-related sleep disorder, whereas narcolepsy was the least common. Cluster analyses showed similar methodology clusters for each SD term, except narcolepsy. The logistic regression models showed an increasing prevalence of insomnia, parasomnia, and other sleep disorders but a decreasing prevalence of breathing-related sleep disorder during 2000-2013. Different SD terms were positively associated with different methodology terms regarding research design terms, measure terms, and analysis terms. Insomnia-, parasomnia-, and other sleep disorder-related articles showed an increasing publication trend, whereas those related to breathing-related sleep disorder showed a decreasing trend. Furthermore, experimental studies more commonly focused on hypersomnia and other SDs and less commonly on insomnia, breathing-related sleep disorder, narcolepsy, and parasomnia. Thus, text mining may facilitate the exploration of the publication trends in SDs and the associated methodologies.

Text mining applications in psychiatry: a systematic literature review.

PubMed

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.

VIOLENT FRAMES IN ACTION

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sanfilippo, Antonio P.; McGrath, Liam R.; Whitney, Paul D.

2011-11-17

We present a computational approach to radical rhetoric that leverages the co-expression of rhetoric and action features in discourse to identify violent intent. The approach combines text mining and machine learning techniques with insights from Frame Analysis and theories that explain the emergence of violence in terms of moral disengagement, the violation of sacred values and social isolation in order to build computational models that identify messages from terrorist sources and estimate their proximity to an attack. We discuss a specific application of this approach to a body of documents from and about radical and terrorist groups in the Middlemore » East and present the results achieved.« less

Using data mining techniques to characterize participation in observational studies.

PubMed

Linden, Ariel; Yarnold, Paul R

2016-12-01

Data mining techniques are gaining in popularity among health researchers for an array of purposes, such as improving diagnostic accuracy, identifying high-risk patients and extracting concepts from unstructured data. In this paper, we describe how these techniques can be applied to another area in the health research domain: identifying characteristics of individuals who do and do not choose to participate in observational studies. In contrast to randomized studies where individuals have no control over their treatment assignment, participants in observational studies self-select into the treatment arm and therefore have the potential to differ in their characteristics from those who elect not to participate. These differences may explain part, or all, of the difference in the observed outcome, making it crucial to assess whether there is differential participation based on observed characteristics. As compared to traditional approaches to this assessment, data mining offers a more precise understanding of these differences. To describe and illustrate the application of data mining in this domain, we use data from a primary care-based medical home pilot programme and compare the performance of commonly used classification approaches - logistic regression, support vector machines, random forests and classification tree analysis (CTA) - in correctly classifying participants and non-participants. We find that CTA is substantially more accurate than the other models. Moreover, unlike the other models, CTA offers transparency in its computational approach, ease of interpretation via the decision rules produced and provides statistical results familiar to health researchers. Beyond their application to research, data mining techniques could help administrators to identify new candidates for participation who may most benefit from the intervention. © 2016 John Wiley & Sons, Ltd.

Genome-wide protein-protein interactions and protein function exploration in cyanobacteria

PubMed Central

Lv, Qi; Ma, Weimin; Liu, Hui; Li, Jiang; Wang, Huan; Lu, Fang; Zhao, Chen; Shi, Tieliu

2015-01-01

Genome-wide network analysis is well implemented to study proteins of unknown function. Here, we effectively explored protein functions and the biological mechanism based on inferred high confident protein-protein interaction (PPI) network in cyanobacteria. We integrated data from seven different sources and predicted 1,997 PPIs, which were evaluated by experiments in molecular mechanism, text mining of literatures in proved direct/indirect evidences, and “interologs” in conservation. Combined the predicted PPIs with known PPIs, we obtained 4,715 no-redundant PPIs (involving 3,231 proteins covering over 90% of genome) to generate the PPI network. Based on the PPI network, terms in Gene ontology (GO) were assigned to function-unknown proteins. Functional modules were identified by dissecting the PPI network into sub-networks and analyzing pathway enrichment, with which we investigated novel function of underlying proteins in protein complexes and pathways. Examples of photosynthesis and DNA repair indicate that the network approach is a powerful tool in protein function analysis. Overall, this systems biology approach provides a new insight into posterior functional analysis of PPIs in cyanobacteria. PMID:26490033

Indirect estimation of emission factors for phosphate surface mining using air dispersion modeling.

PubMed

Tartakovsky, Dmitry; Stern, Eli; Broday, David M

2016-06-15

To date, phosphate surface mining suffers from lack of reliable emission factors. Due to complete absence of data to derive emissions factors, we developed a methodology for estimating them indirectly by studying a range of possible emission factors for surface phosphate mining operations and comparing AERMOD calculated concentrations to concentrations measured around the mine. We applied this approach for the Khneifiss phosphate mine, Syria, and the Al-Hassa and Al-Abyad phosphate mines, Jordan. The work accounts for numerous model unknowns and parameter uncertainties by applying prudent assumptions concerning the parameter values. Our results suggest that the net mining operations (bulldozing, grading and dragline) contribute rather little to ambient TSP concentrations in comparison to phosphate processing and transport. Based on our results, the common practice of deriving the emission rates for phosphate mining operations from the US EPA emission factors for surface coal mining or from the default emission factor of the EEA seems to be reasonable. Yet, since multiple factors affect dispersion from surface phosphate mines, a range of emission factors, rather than only a single value, was found to satisfy the model performance. Copyright © 2016 Elsevier B.V. All rights reserved.

Data mining for multiagent rules, strategies, and fuzzy decision tree structure

NASA Astrophysics Data System (ADS)

Smith, James F., III; Rhyne, Robert D., II; Fisher, Kristin

2002-03-01

A fuzzy logic based resource manager (RM) has been developed that automatically allocates electronic attack resources in real-time over many dissimilar platforms. Two different data mining algorithms have been developed to determine rules, strategies, and fuzzy decision tree structure. The first data mining algorithm uses a genetic algorithm as a data mining function and is called from an electronic game. The game allows a human expert to play against the resource manager in a simulated battlespace with each of the defending platforms being exclusively directed by the fuzzy resource manager and the attacking platforms being controlled by the human expert or operating autonomously under their own logic. This approach automates the data mining problem. The game automatically creates a database reflecting the domain expert's knowledge. It calls a data mining function, a genetic algorithm, for data mining of the database as required and allows easy evaluation of the information mined in the second step. The criterion for re- optimization is discussed as well as experimental results. Then a second data mining algorithm that uses a genetic program as a data mining function is introduced to automatically discover fuzzy decision tree structures. Finally, a fuzzy decision tree generated through this process is discussed.

Information mining in remote sensing imagery

NASA Astrophysics Data System (ADS)

Li, Jiang

The volume of remotely sensed imagery continues to grow at an enormous rate due to the advances in sensor technology, and our capability for collecting and storing images has greatly outpaced our ability to analyze and retrieve information from the images. This motivates us to develop image information mining techniques, which is very much an interdisciplinary endeavor drawing upon expertise in image processing, databases, information retrieval, machine learning, and software design. This dissertation proposes and implements an extensive remote sensing image information mining (ReSIM) system prototype for mining useful information implicitly stored in remote sensing imagery. The system consists of three modules: image processing subsystem, database subsystem, and visualization and graphical user interface (GUI) subsystem. Land cover and land use (LCLU) information corresponding to spectral characteristics is identified by supervised classification based on support vector machines (SVM) with automatic model selection, while textural features that characterize spatial information are extracted using Gabor wavelet coefficients. Within LCLU categories, textural features are clustered using an optimized k-means clustering approach to acquire search efficient space. The clusters are stored in an object-oriented database (OODB) with associated images indexed in an image database (IDB). A k-nearest neighbor search is performed using a query-by-example (QBE) approach. Furthermore, an automatic parametric contour tracing algorithm and an O(n) time piecewise linear polygonal approximation (PLPA) algorithm are developed for shape information mining of interesting objects within the image. A fuzzy object-oriented database based on the fuzzy object-oriented data (FOOD) model is developed to handle the fuzziness and uncertainty. Three specific applications are presented: integrated land cover and texture pattern mining, shape information mining for change detection of lakes, and fuzzy normalized difference vegetation index (NDVI) pattern mining. The study results show the effectiveness of the proposed system prototype and the potentials for other applications in remote sensing.

Recent progress in automatically extracting information from the pharmacogenomic literature

PubMed Central

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

QTLTableMiner++: semantic mining of QTL tables in scientific articles.

PubMed

Singh, Gurnoor; Kuzniar, Arnold; van Mulligen, Erik M; Gavai, Anand; Bachem, Christian W; Visser, Richard G F; Finkers, Richard

2018-05-25

A quantitative trait locus (QTL) is a genomic region that correlates with a phenotype. Most of the experimental information about QTL mapping studies is described in tables of scientific publications. Traditional text mining techniques aim to extract information from unstructured text rather than from tables. We present QTLTableMiner ++ (QTM), a table mining tool that extracts and semantically annotates QTL information buried in (heterogeneous) tables of plant science literature. QTM is a command line tool written in the Java programming language. This tool takes scientific articles from the Europe PMC repository as input, extracts QTL tables using keyword matching and ontology-based concept identification. The tables are further normalized using rules derived from table properties such as captions, column headers and table footers. Furthermore, table columns are classified into three categories namely column descriptors, properties and values based on column headers and data types of cell entries. Abbreviations found in the tables are expanded using the Schwartz and Hearst algorithm. Finally, the content of QTL tables is semantically enriched with domain-specific ontologies (e.g. Crop Ontology, Plant Ontology and Trait Ontology) using the Apache Solr search platform and the results are stored in a relational database and a text file. The performance of the QTM tool was assessed by precision and recall based on the information retrieved from two manually annotated corpora of open access articles, i.e. QTL mapping studies in tomato (Solanum lycopersicum) and in potato (S. tuberosum). In summary, QTM detected QTL statements in tomato with 74.53% precision and 92.56% recall and in potato with 82.82% precision and 98.94% recall. QTM is a unique tool that aids in providing QTL information in machine-readable and semantically interoperable formats.

Quantitative Analysis of Critical Factors for the Climate Impact of Landfill Mining.

PubMed

Laner, David; Cencic, Oliver; Svensson, Niclas; Krook, Joakim

2016-07-05

Landfill mining has been proposed as an innovative strategy to mitigate environmental risks associated with landfills, to recover secondary raw materials and energy from the deposited waste, and to enable high-valued land uses at the site. The present study quantitatively assesses the importance of specific factors and conditions for the net contribution of landfill mining to global warming using a novel, set-based modeling approach and provides policy recommendations for facilitating the development of projects contributing to global warming mitigation. Building on life-cycle assessment, scenario modeling and sensitivity analysis methods are used to identify critical factors for the climate impact of landfill mining. The net contributions to global warming of the scenarios range from -1550 (saving) to 640 (burden) kg CO2e per Mg of excavated waste. Nearly 90% of the results' total variation can be explained by changes in four factors, namely the landfill gas management in the reference case (i.e., alternative to mining the landfill), the background energy system, the composition of the excavated waste, and the applied waste-to-energy technology. Based on the analyses, circumstances under which landfill mining should be prioritized or not are identified and sensitive parameters for the climate impact assessment of landfill mining are highlighted.

MSL: Facilitating automatic and physical analysis of published scientific literature in PDF format.

PubMed

Ahmed, Zeeshan; Dandekar, Thomas

2015-01-01

Published scientific literature contains millions of figures, including information about the results obtained from different scientific experiments e.g. PCR-ELISA data, microarray analysis, gel electrophoresis, mass spectrometry data, DNA/RNA sequencing, diagnostic imaging (CT/MRI and ultrasound scans), and medicinal imaging like electroencephalography (EEG), magnetoencephalography (MEG), echocardiography  (ECG), positron-emission tomography (PET) images. The importance of biomedical figures has been widely recognized in scientific and medicine communities, as they play a vital role in providing major original data, experimental and computational results in concise form. One major challenge for implementing a system for scientific literature analysis is extracting and analyzing text and figures from published PDF files by physical and logical document analysis. Here we present a product line architecture based bioinformatics tool 'Mining Scientific Literature (MSL)', which supports the extraction of text and images by interpreting all kinds of published PDF files using advanced data mining and image processing techniques. It provides modules for the marginalization of extracted text based on different coordinates and keywords, visualization of extracted figures and extraction of embedded text from all kinds of biological and biomedical figures using applied Optimal Character Recognition (OCR). Moreover, for further analysis and usage, it generates the system's output in different formats including text, PDF, XML and images files. Hence, MSL is an easy to install and use analysis tool to interpret published scientific literature in PDF format.

Mining moving object trajectories in location-based services for spatio-temporal database update

NASA Astrophysics Data System (ADS)

Guo, Danhuai; Cui, Weihong

2008-10-01

Advances in wireless transmission and mobile technology applied to LBS (Location-based Services) flood us with amounts of moving objects data. Vast amounts of gathered data from position sensors of mobile phones, PDAs, or vehicles hide interesting and valuable knowledge and describe the behavior of moving objects. The correlation between temporal moving patterns of moving objects and geo-feature spatio-temporal attribute was ignored, and the value of spatio-temporal trajectory data was not fully exploited too. Urban expanding or frequent town plan change bring about a large amount of outdated or imprecise data in spatial database of LBS, and they cannot be updated timely and efficiently by manual processing. In this paper we introduce a data mining approach to movement pattern extraction of moving objects, build a model to describe the relationship between movement patterns of LBS mobile objects and their environment, and put up with a spatio-temporal database update strategy in LBS database based on trajectories spatiotemporal mining. Experimental evaluation reveals excellent performance of the proposed model and strategy. Our original contribution include formulation of model of interaction between trajectory and its environment, design of spatio-temporal database update strategy based on moving objects data mining, and the experimental application of spatio-temporal database update by mining moving objects trajectories.

Developing image processing meta-algorithms with data mining of multiple metrics.

PubMed

Leung, Kelvin; Cunha, Alexandre; Toga, A W; Parker, D Stott

2014-01-01

People often use multiple metrics in image processing, but here we take a novel approach of mining the values of batteries of metrics on image processing results. We present a case for extending image processing methods to incorporate automated mining of multiple image metric values. Here by a metric we mean any image similarity or distance measure, and in this paper we consider intensity-based and statistical image measures and focus on registration as an image processing problem. We show how it is possible to develop meta-algorithms that evaluate different image processing results with a number of different metrics and mine the results in an automated fashion so as to select the best results. We show that the mining of multiple metrics offers a variety of potential benefits for many image processing problems, including improved robustness and validation.

Integrated investigations of environmental effects of historical mining in the Basin and Boulder Mining Districts, Boulder River watershed, Jefferson County, Montana

USGS Publications Warehouse

Nimick, David A.; Church, Stan E.; Finger, Susan E.

2004-01-01

The Boulder River watershed is one of many watersheds in the western United States where historical mining has left a legacy of acid mine drainage and elevated concentrations of potentially toxic trace elements. Abandoned mine lands commonly are located on or affect Federal land. Cleaning up these Federal lands will require substantial investment of resources. As part of a cooperative effort with Federal land-management agencies, the U.S. Geological Survey implemented an Abandoned Mine Lands Initiative in 1997. The goal of the initiative was to use the watershed approach to develop a strategy for gathering and communicating the scientific information needed to formulate effective and cost-efficient remediation of affected lands in a watershed. The watershed approach is based on the premise that contaminated sites that have the most profound effect on water and ecosystem quality within an entire watershed should be identified, characterized, and ranked for remediation.The watershed approach provides an effective means to evaluate the overall status of affected resources and helps to focus remediation at sites where the most benefit will be gained in the watershed. Such a large-scale approach can result in the collection of extensive information on the geology and geochemistry of rocks and sediment, the hydrology and water chemistry of streams and ground water, and the diversity and health of aquatic and terrestrial organisms. During the assessment of the Boulder River watershed, we inventoried historical mines, defined geological conditions, assessed fish habitat, collected and chemically analyzed hundreds of water and sediment samples, conducted toxicity tests, analyzed fish tissue and indicators of physiological malfunction, examined invertebrates and biofilm, and defined hydrological regimes. Land- and resource-management agencies are faced with evaluating risks associated with thousands of potentially harmful mine sites, and this level of effort is not always feasible for every affected watershed. The detailed work described in this report can help Federal land-management agencies decide which characterization efforts would be most useful in characterization of other affected watersheds.

Development and application of the Safe Performance Index as a risk-based methodology for identifying major hazard-related safety issues in underground coal mines

NASA Astrophysics Data System (ADS)

Kinilakodi, Harisha

The underground coal mining industry has been under constant watch due to the high risk involved in its activities, and scrutiny increased because of the disasters that occurred in 2006-07. In the aftermath of the incidents, the U.S. Congress passed the Mine Improvement and New Emergency Response Act of 2006 (MINER Act), which strengthened the existing regulations and mandated new laws to address the various issues related to a safe working environment in the mines. Risk analysis in any form should be done on a regular basis to tackle the possibility of unwanted major hazard-related events such as explosions, outbursts, airbursts, inundations, spontaneous combustion, and roof fall instabilities. One of the responses by the Mine Safety and Health Administration (MSHA) in 2007 involved a new pattern of violations (POV) process to target mines with a poor safety performance, specifically to improve their safety. However, the 2010 disaster (worst in 40 years) gave an impression that the collective effort of the industry, federal/state agencies, and researchers to achieve the goal of zero fatalities and serious injuries has gone awry. The Safe Performance Index (SPI) methodology developed in this research is a straight-forward, effective, transparent, and reproducible approach that can help in identifying and addressing some of the existing issues while targeting (poor safety performance) mines which need help. It combines three injury and three citation measures that are scaled to have an equal mean (5.0) in a balanced way with proportionate weighting factors (0.05, 0.15, 0.30) and overall normalizing factor (15) into a mine safety performance evaluation tool. It can be used to assess the relative safety-related risk of mines, including by mine-size category. Using 2008 and 2009 data, comparisons were made of SPI-associated, normalized safety performance measures across mine-size categories, with emphasis on small-mine safety performance as compared to large- and medium-sized mines. The accident rates (NDL IR, NFDL IR, SM/100) of very small and small mines in 2008 and 2009 were less than those of medium and large mines. The data indicates a heavy occurrence of very severe injuries in a number of very small and small mines. In another application which is a part of this research, the six normalized safety measures and the SPI are used to evaluate the risk that existed at mines in the two years preceding the occurrence of a fatality. This mine safety performance tracking method could have been helpful to the companies, state agency, or MSHA in recognizing and addressing emerging problems with actions that may have been able to prevent high-risk conditions, the fatality, and/or other serious injuries. The approach would have given scrutiny to the risk of mines that encompassed 74% of the fatalities during 2007-2010. In order to assess the SPI as a comparable risk measurement tool, a traditional risk approach is also developed using data embracing frequency and severity in the final equation to analyze the relative risk for all underground coal mines for the years 2007--2010. Then, the SPI is compared with this traditional risk analysis method to demonstrate that the results attained by either method provide the relative safety-related risk of underground coal mines regarding injuries and citations for violations of regulations. The comparison reveals that the SPI does emulate a traditional approach to risk analysis. A correlation coefficient of --0.89 or more was observed between the results of these two methodologies and either can be used to assist companies, the Mine Safety and Health Administration (MSHA), or state agencies in target-ing mines with high risk for serious injuries and elevated citations for remediation of their injury and/or violation experience. The SPI, however, provides a more understandable approach for mine operators to apply using measures compatible with MSHA's enforcement tools. These methodologies form an all-encompassing approach that can be used to assist companies, the MSHA, or state agencies in targeting mines with high risk for serious injuries and elevated citations. Once targeted as high risk, mines can then pursue appropriate intervention to remediate their violation and/or injury experience. This research may help in plugging the gap in the safety system and better pursue the goal of zero fatalities and serious injuries in the underground coal mines.

GPU Based N-Gram String Matching Algorithm with Score Table Approach for String Searching in Many Documents

NASA Astrophysics Data System (ADS)

Srinivasa, K. G.; Shree Devi, B. N.

2017-10-01

String searching in documents has become a tedious task with the evolution of Big Data. Generation of large data sets demand for a high performance search algorithm in areas such as text mining, information retrieval and many others. The popularity of GPU's for general purpose computing has been increasing for various applications. Therefore it is of great interest to exploit the thread feature of a GPU to provide a high performance search algorithm. This paper proposes an optimized new approach to N-gram model for string search in a number of lengthy documents and its GPU implementation. The algorithm exploits GPGPUs for searching strings in many documents employing character level N-gram matching with parallel Score Table approach and search using CUDA API. The new approach of Score table used for frequency storage of N-grams in a document, makes the search independent of the document's length and allows faster access to the frequency values, thus decreasing the search complexity. The extensive thread feature in a GPU has been exploited to enable parallel pre-processing of trigrams in a document for Score Table creation and parallel search in huge number of documents, thus speeding up the whole search process even for a large pattern size. Experiments were carried out for many documents of varied length and search strings from the standard Lorem Ipsum text on NVIDIA's GeForce GT 540M GPU with 96 cores. Results prove that the parallel approach for Score Table creation and searching gives a good speed up than the same approach executed serially.

Privacy Preserving Technique for Euclidean Distance Based Mining Algorithms Using a Wavelet Related Transform

NASA Astrophysics Data System (ADS)

Kadampur, Mohammad Ali; D. v. L. N., Somayajulu

Privacy preserving data mining is an art of knowledge discovery without revealing the sensitive data of the data set. In this paper a data transformation technique using wavelets is presented for privacy preserving data mining. Wavelets use well known energy compaction approach during data transformation and only the high energy coefficients are published to the public domain instead of the actual data proper. It is found that the transformed data preserves the Eucleadian distances and the method can be used in privacy preserving clustering. Wavelets offer the inherent improved time complexity.

Individual Profiling Using Text Analysis

DTIC Science & Technology

2016-04-15

Mining a Text for Errors. . . . on Knowledge discovery in data mining , pages 624–628, 2005. [12] Michal Kosinski, David Stillwell, and Thore Graepel...AFRL-AFOSR-UK-TR-2016-0011 Individual Profiling using Text Analysis 140333 Mark Stevenson UNIVERSITY OF SHEFFIELD, DEPARTMENT OF PSYCHOLOGY Final...REPORT TYPE      Final 3.  DATES COVERED (From - To)      15 Sep 2014 to 14 Sep 2015 4.  TITLE AND SUBTITLE Individual Profiling using Text Analysis

Mining the pharmacogenomics literature—a survey of the state of the art

PubMed Central

Cohen, K. Bretonnel; Garten, Yael; Shah, Nigam H.

2012-01-01

This article surveys efforts on text mining of the pharmacogenomics literature, mainly from the period 2008 to 2011. Pharmacogenomics (or pharmacogenetics) is the field that studies how human genetic variation impacts drug response. Therefore, publications span the intersection of research in genotypes, phenotypes and pharmacology, a topic that has increasingly become a focus of active research in recent years. This survey covers efforts dealing with the automatic recognition of relevant named entities (e.g. genes, gene variants and proteins, diseases and other pathological phenomena, drugs and other chemicals relevant for medical treatment), as well as various forms of relations between them. A wide range of text genres is considered, such as scientific publications (abstracts, as well as full texts), patent texts and clinical narratives. We also discuss infrastructure and resources needed for advanced text analytics, e.g. document corpora annotated with corresponding semantic metadata (gold standards and training data), biomedical terminologies and ontologies providing domain-specific background knowledge at different levels of formality and specificity, software architectures for building complex and scalable text analytics pipelines and Web services grounded to them, as well as comprehensive ways to disseminate and interact with the typically huge amounts of semiformal knowledge structures extracted by text mining tools. Finally, we consider some of the novel applications that have already been developed in the field of pharmacogenomic text mining and point out perspectives for future research. PMID:22833496

Mining the pharmacogenomics literature--a survey of the state of the art.

PubMed

Hahn, Udo; Cohen, K Bretonnel; Garten, Yael; Shah, Nigam H

2012-07-01

This article surveys efforts on text mining of the pharmacogenomics literature, mainly from the period 2008 to 2011. Pharmacogenomics (or pharmacogenetics) is the field that studies how human genetic variation impacts drug response. Therefore, publications span the intersection of research in genotypes, phenotypes and pharmacology, a topic that has increasingly become a focus of active research in recent years. This survey covers efforts dealing with the automatic recognition of relevant named entities (e.g. genes, gene variants and proteins, diseases and other pathological phenomena, drugs and other chemicals relevant for medical treatment), as well as various forms of relations between them. A wide range of text genres is considered, such as scientific publications (abstracts, as well as full texts), patent texts and clinical narratives. We also discuss infrastructure and resources needed for advanced text analytics, e.g. document corpora annotated with corresponding semantic metadata (gold standards and training data), biomedical terminologies and ontologies providing domain-specific background knowledge at different levels of formality and specificity, software architectures for building complex and scalable text analytics pipelines and Web services grounded to them, as well as comprehensive ways to disseminate and interact with the typically huge amounts of semiformal knowledge structures extracted by text mining tools. Finally, we consider some of the novel applications that have already been developed in the field of pharmacogenomic text mining and point out perspectives for future research.

Application of Laser Scanning for Creating Geological Documentation

NASA Astrophysics Data System (ADS)

Buczek, Michał; Paszek, Martyna; Szafarczyk, Anna

2018-03-01

A geological documentation is based on the analyses obtained from boreholes, geological exposures, and geophysical methods. It consists of text and graphic documents, containing drilling sections, vertical crosssections through the deposit and various types of maps. The surveying methods (such as LIDAR) can be applied in measurements of exposed rock layers, presented in appendices to the geological documentation. The laser scanning allows obtaining a complete profile of exposed surfaces in a short time and with a millimeter accuracy. The possibility of verifying the existing geological cross-section with laser scanning was tested on the example of the AGH experimental mine. The test field is built of different lithological rocks. Scans were taken from a single station, under favorable measuring conditions. The analysis of the signal intensity allowed to divide point cloud into separate geological layers. The results were compared with the geological profiles of the measured object. The same approach was applied to the data from the Vietnamese hard coal open pit mine Coc Sau. The thickness of exposed coal bed deposits and gangue layers were determined from the obtained data (point cloud) in combination with the photographs. The results were compared with the geological cross-section.

Using Text Mining to Characterize Online Discussion Facilitation

ERIC Educational Resources Information Center

Ming, Norma; Baumer, Eric

2011-01-01

Facilitating class discussions effectively is a critical yet challenging component of instruction, particularly in online environments where student and faculty interaction is limited. Our goals in this research were to identify facilitation strategies that encourage productive discussion, and to explore text mining techniques that can help…

40 CFR 372.23 - SIC and NAICS codes to which this Part applies.

Code of Federal Regulations, 2010 CFR

2010-07-01

... facilities primarily engaged in reproducing text, drawings, plans, maps, or other copy, by blueprinting...)); 212324Kaolin and Ball Clay Mining Limited to facilities operating without a mine or quarry and that are...)); 212393Other Chemical and Fertilizer Mineral Mining Limited to facilities operating without a mine or quarry...