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.

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

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…

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.

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.

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

OntoGene web services for biomedical text mining.

PubMed

Rinaldi, Fabio; Clematide, Simon; Marques, Hernani; Ellendorff, Tilia; Romacker, Martin; Rodriguez-Esteban, Raul

2014-01-01

Text mining services are rapidly becoming a crucial component of various knowledge management pipelines, for example in the process of database curation, or for exploration and enrichment of biomedical data within the pharmaceutical industry. Traditional architectures, based on monolithic applications, do not offer sufficient flexibility for a wide range of use case scenarios, and therefore open architectures, as provided by web services, are attracting increased interest. We present an approach towards providing advanced text mining capabilities through web services, using a recently proposed standard for textual data interchange (BioC). The web services leverage a state-of-the-art platform for text mining (OntoGene) which has been tested in several community-organized evaluation challenges,with top ranked results in several of them.

OntoGene web services for biomedical text mining

PubMed Central

2014-01-01

Text mining services are rapidly becoming a crucial component of various knowledge management pipelines, for example in the process of database curation, or for exploration and enrichment of biomedical data within the pharmaceutical industry. Traditional architectures, based on monolithic applications, do not offer sufficient flexibility for a wide range of use case scenarios, and therefore open architectures, as provided by web services, are attracting increased interest. We present an approach towards providing advanced text mining capabilities through web services, using a recently proposed standard for textual data interchange (BioC). The web services leverage a state-of-the-art platform for text mining (OntoGene) which has been tested in several community-organized evaluation challenges, with top ranked results in several of them. PMID:25472638

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.

Text Classification for Organizational Researchers

PubMed Central

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

2017-01-01

Organizations are increasingly interested in classifying texts or parts thereof into categories, as this enables more effective use of their information. Manual procedures for text classification work well for up to a few hundred documents. However, when the number of documents is larger, manual procedures become laborious, time-consuming, and potentially unreliable. Techniques from text mining facilitate the automatic assignment of text strings to categories, making classification expedient, fast, and reliable, which creates potential for its application in organizational research. The purpose of this article is to familiarize organizational researchers with text mining techniques from machine learning and statistics. We describe the text classification process in several roughly sequential steps, namely training data preparation, preprocessing, transformation, application of classification techniques, and validation, and provide concrete recommendations at each step. To help researchers develop their own text classifiers, the R code associated with each step is presented in a tutorial. The tutorial draws from our own work on job vacancy mining. We end the article by discussing how researchers can validate a text classification model and the associated output. PMID:29881249

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.

Generative Topic Modeling in Image Data Mining and Bioinformatics Studies

ERIC Educational Resources Information Center

Chen, Xin

2012-01-01

Probabilistic topic models have been developed for applications in various domains such as text mining, information retrieval and computer vision and bioinformatics domain. In this thesis, we focus on developing novel probabilistic topic models for image mining and bioinformatics studies. Specifically, a probabilistic topic-connection (PTC) model…

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.

Macromolecule mass spectrometry: citation mining of user documents.

PubMed

Kostoff, Ronald N; Bedford, Clifford D; del Río, J Antonio; Cortes, Héctor D; Karypis, George

2004-03-01

Identifying research users, applications, and impact is important for research performers, managers, evaluators, and sponsors. Identification of the user audience and the research impact is complex and time consuming due to the many indirect pathways through which fundamental research can impact applications. This paper identified the literature pathways through which two highly-cited papers of 2002 Chemistry Nobel Laureates Fenn and Tanaka impacted research, technology development, and applications. Citation Mining, an integration of citation bibliometrics and text mining, was applied to the >1600 first generation Science Citation Index (SCI) citing papers to Fenn's 1989 Science paper on Electrospray Ionization for Mass Spectrometry, and to the >400 first generation SCI citing papers to Tanaka's 1988 Rapid Communications in Mass Spectrometry paper on Laser Ionization Time-of-Flight Mass Spectrometry. Bibliometrics was performed on the citing papers to profile the user characteristics. Text mining was performed on the citing papers to identify the technical areas impacted by the research, and the relationships among these technical areas.

Text Mining for Drugs and Chemical Compounds: Methods, Tools and Applications.

PubMed

Vazquez, Miguel; Krallinger, Martin; Leitner, Florian; Valencia, Alfonso

2011-06-01

Providing prior knowledge about biological properties of chemicals, such as kinetic values, protein targets, or toxic effects, can facilitate many aspects of drug development. Chemical information is rapidly accumulating in all sorts of free text documents like patents, industry reports, or scientific articles, which has motivated the development of specifically tailored text mining applications. Despite the potential gains, chemical text mining still faces significant challenges. One of the most salient is the recognition of chemical entities mentioned in text. To help practitioners contribute to this area, a good portion of this review is devoted to this issue, and presents the basic concepts and principles underlying the main strategies. The technical details are introduced and accompanied by relevant bibliographic references. Other tasks discussed are retrieving relevant articles, identifying relationships between chemicals and other entities, or determining the chemical structures of chemicals mentioned in text. This review also introduces a number of published applications that can be used to build pipelines in topics like drug side effects, toxicity, and protein-disease-compound network analysis. We conclude the review with an outlook on how we expect the field to evolve, discussing its possibilities and its current limitations. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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.

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.

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.

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.

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.

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.

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.

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.

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

@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.

75 FR 8316 - Office of Postsecondary Education; Overview Information; Erma Byrd Scholarship Program; Notice...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-02-24

... Transmittal of Applications: March 26, 2010. Full Text of Announcement I. Funding Opportunity Description... related to industrial health and safety: Mining and mineral engineering, industrial engineering... technology/technician, hazardous materials information systems technology/technician, mining technology...

Mining Consumer Health Vocabulary from Community-Generated Text

PubMed Central

Vydiswaran, V.G. Vinod; Mei, Qiaozhu; Hanauer, David A.; Zheng, Kai

2014-01-01

Community-generated text corpora can be a valuable resource to extract consumer health vocabulary (CHV) and link them to professional terminologies and alternative variants. In this research, we propose a pattern-based text-mining approach to identify pairs of CHV and professional terms from Wikipedia, a large text corpus created and maintained by the community. A novel measure, leveraging the ratio of frequency of occurrence, was used to differentiate consumer terms from professional terms. We empirically evaluated the applicability of this approach using a large data sample consisting of MedLine abstracts and all posts from an online health forum, MedHelp. The results show that the proposed approach is able to identify synonymous pairs and label the terms as either consumer or professional term with high accuracy. We conclude that the proposed approach provides great potential to produce a high quality CHV to improve the performance of computational applications in processing consumer-generated health text. PMID:25954426

30 CFR 900.12 - State regulatory programs.

Code of Federal Regulations, 2010 CFR

2010-07-01

....12 Mineral Resources OFFICE OF SURFACE MINING RECLAMATION AND ENFORCEMENT, DEPARTMENT OF THE INTERIOR PROGRAMS FOR THE CONDUCT OF SURFACE MINING OPERATIONS WITHIN EACH STATE INTRODUCTION § 900.12 State... to be codified under the applicable part number assigned to the State. The full text will not appear...

30 CFR 745.11 - Application and agreement.

Code of Federal Regulations, 2010 CFR

2010-07-01

....11 Mineral Resources OFFICE OF SURFACE MINING RECLAMATION AND ENFORCEMENT, DEPARTMENT OF THE INTERIOR... approval under part 731 of this chapter, and has or may have within the State surface coal mining and... the full text of the terms of the proposed cooperative agreement as submitted or as subsequently...

29 CFR 570.118 - Sixteen-year minimum.

Code of Federal Regulations, 2010 CFR

2010-07-01

... for employment in manufacturing or mining occupations. Furthermore, this age minimum is applicable to... convenience of the user, the revised text is set forth as follows: § 570.118 Sixteen-year minimum. The Act sets a 16-year-age minimum for employment in manufacturing or mining occupations, although under FLSA...

The Functional Genomics Network in the evolution of biological text mining over the past decade.

PubMed

Blaschke, Christian; Valencia, Alfonso

2013-03-25

Different programs of The European Science Foundation (ESF) have contributed significantly to connect researchers in Europe and beyond through several initiatives. This support was particularly relevant for the development of the areas related with extracting information from papers (text-mining) because it supported the field in its early phases long before it was recognized by the community. We review the historical development of text mining research and how it was introduced in bioinformatics. Specific applications in (functional) genomics are described like it's integration in genome annotation pipelines and the support to the analysis of high-throughput genomics experimental data, and we highlight the activities of evaluation of methods and benchmarking for which the ESF programme support was instrumental. Copyright © 2013 Elsevier B.V. All rights reserved.

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.

Transfer Learning beyond Text Classification

NASA Astrophysics Data System (ADS)

Yang, Qiang

Transfer learning is a new machine learning and data mining framework that allows the training and test data to come from different distributions or feature spaces. We can find many novel applications of machine learning and data mining where transfer learning is necessary. While much has been done in transfer learning in text classification and reinforcement learning, there has been a lack of documented success stories of novel applications of transfer learning in other areas. In this invited article, I will argue that transfer learning is in fact quite ubiquitous in many real world applications. In this article, I will illustrate this point through an overview of a broad spectrum of applications of transfer learning that range from collaborative filtering to sensor based location estimation and logical action model learning for AI planning. I will also discuss some potential future directions of transfer learning.

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

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

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.

Deploying and sharing U-Compare workflows as web services.

PubMed

Kontonatsios, Georgios; Korkontzelos, Ioannis; Kolluru, Balakrishna; Thompson, Paul; Ananiadou, Sophia

2013-02-18

U-Compare is a text mining platform that allows the construction, evaluation and comparison of text mining workflows. U-Compare contains a large library of components that are tuned to the biomedical domain. Users can rapidly develop biomedical text mining workflows by mixing and matching U-Compare's components. Workflows developed using U-Compare can be exported and sent to other users who, in turn, can import and re-use them. However, the resulting workflows are standalone applications, i.e., software tools that run and are accessible only via a local machine, and that can only be run with the U-Compare platform. We address the above issues by extending U-Compare to convert standalone workflows into web services automatically, via a two-click process. The resulting web services can be registered on a central server and made publicly available. Alternatively, users can make web services available on their own servers, after installing the web application framework, which is part of the extension to U-Compare. We have performed a user-oriented evaluation of the proposed extension, by asking users who have tested the enhanced functionality of U-Compare to complete questionnaires that assess its functionality, reliability, usability, efficiency and maintainability. The results obtained reveal that the new functionality is well received by users. The web services produced by U-Compare are built on top of open standards, i.e., REST and SOAP protocols, and therefore, they are decoupled from the underlying platform. Exported workflows can be integrated with any application that supports these open standards. We demonstrate how the newly extended U-Compare enhances the cross-platform interoperability of workflows, by seamlessly importing a number of text mining workflow web services exported from U-Compare into Taverna, i.e., a generic scientific workflow construction platform.

Deploying and sharing U-Compare workflows as web services

PubMed Central

2013-01-01

Background U-Compare is a text mining platform that allows the construction, evaluation and comparison of text mining workflows. U-Compare contains a large library of components that are tuned to the biomedical domain. Users can rapidly develop biomedical text mining workflows by mixing and matching U-Compare’s components. Workflows developed using U-Compare can be exported and sent to other users who, in turn, can import and re-use them. However, the resulting workflows are standalone applications, i.e., software tools that run and are accessible only via a local machine, and that can only be run with the U-Compare platform. Results We address the above issues by extending U-Compare to convert standalone workflows into web services automatically, via a two-click process. The resulting web services can be registered on a central server and made publicly available. Alternatively, users can make web services available on their own servers, after installing the web application framework, which is part of the extension to U-Compare. We have performed a user-oriented evaluation of the proposed extension, by asking users who have tested the enhanced functionality of U-Compare to complete questionnaires that assess its functionality, reliability, usability, efficiency and maintainability. The results obtained reveal that the new functionality is well received by users. Conclusions The web services produced by U-Compare are built on top of open standards, i.e., REST and SOAP protocols, and therefore, they are decoupled from the underlying platform. Exported workflows can be integrated with any application that supports these open standards. We demonstrate how the newly extended U-Compare enhances the cross-platform interoperability of workflows, by seamlessly importing a number of text mining workflow web services exported from U-Compare into Taverna, i.e., a generic scientific workflow construction platform. PMID:23419017

Mining Quality Phrases from Massive Text Corpora

PubMed Central

Liu, Jialu; Shang, Jingbo; Wang, Chi; Ren, Xiang; Han, Jiawei

2015-01-01

Text data are ubiquitous and play an essential role in big data applications. However, text data are mostly unstructured. Transforming unstructured text into structured units (e.g., semantically meaningful phrases) will substantially reduce semantic ambiguity and enhance the power and efficiency at manipulating such data using database technology. Thus mining quality phrases is a critical research problem in the field of databases. In this paper, we propose a new framework that extracts quality phrases from text corpora integrated with phrasal segmentation. The framework requires only limited training but the quality of phrases so generated is close to human judgment. Moreover, the method is scalable: both computation time and required space grow linearly as corpus size increases. Our experiments on large text corpora demonstrate the quality and efficiency of the new method. PMID:26705375

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

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.

Extraction and Classification of Emotions for Business Research

NASA Astrophysics Data System (ADS)

Verma, Rajib

The commercial study of emotions has not embraced Internet / social mining yet, even though it has important applications in management. This is surprising since the emotional content is freeform, wide spread, can give a better indication of feelings (for instance with taboo subjects), and is inexpensive compared to other business research methods. A brief framework for applying text mining to this new research domain is shown and classification issues are discussed in an effort to quickly get businessman and researchers to adopt the mining methodology.

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.

Discovering and visualizing indirect associations between biomedical concepts

PubMed Central

Tsuruoka, Yoshimasa; Miwa, Makoto; Hamamoto, Kaisei; Tsujii, Jun'ichi; Ananiadou, Sophia

2011-01-01

Motivation: Discovering useful associations between biomedical concepts has been one of the main goals in biomedical text-mining, and understanding their biomedical contexts is crucial in the discovery process. Hence, we need a text-mining system that helps users explore various types of (possibly hidden) associations in an easy and comprehensible manner. Results: This article describes FACTA+, a real-time text-mining system for finding and visualizing indirect associations between biomedical concepts from MEDLINE abstracts. The system can be used as a text search engine like PubMed with additional features to help users discover and visualize indirect associations between important biomedical concepts such as genes, diseases and chemical compounds. FACTA+ inherits all functionality from its predecessor, FACTA, and extends it by incorporating three new features: (i) detecting biomolecular events in text using a machine learning model, (ii) discovering hidden associations using co-occurrence statistics between concepts, and (iii) visualizing associations to improve the interpretability of the output. To the best of our knowledge, FACTA+ is the first real-time web application that offers the functionality of finding concepts involving biomolecular events and visualizing indirect associations of concepts with both their categories and importance. Availability: FACTA+ is available as a web application at http://refine1-nactem.mc.man.ac.uk/facta/, and its visualizer is available at http://refine1-nactem.mc.man.ac.uk/facta-visualizer/. Contact: tsuruoka@jaist.ac.jp PMID:21685059

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.

Text Mining for Neuroscience

NASA Astrophysics Data System (ADS)

Tirupattur, Naveen; Lapish, Christopher C.; Mukhopadhyay, Snehasis

2011-06-01

Text mining, sometimes alternately referred to as text analytics, refers to the process of extracting high-quality knowledge from the analysis of textual data. Text mining has wide variety of applications in areas such as biomedical science, news analysis, and homeland security. In this paper, we describe an approach and some relatively small-scale experiments which apply text mining to neuroscience research literature to find novel associations among a diverse set of entities. Neuroscience is a discipline which encompasses an exceptionally wide range of experimental approaches and rapidly growing interest. This combination results in an overwhelmingly large and often diffuse literature which makes a comprehensive synthesis difficult. Understanding the relations or associations among the entities appearing in the literature not only improves the researchers current understanding of recent advances in their field, but also provides an important computational tool to formulate novel hypotheses and thereby assist in scientific discoveries. We describe a methodology to automatically mine the literature and form novel associations through direct analysis of published texts. The method first retrieves a set of documents from databases such as PubMed using a set of relevant domain terms. In the current study these terms yielded a set of documents ranging from 160,909 to 367,214 documents. Each document is then represented in a numerical vector form from which an Association Graph is computed which represents relationships between all pairs of domain terms, based on co-occurrence. Association graphs can then be subjected to various graph theoretic algorithms such as transitive closure and cycle (circuit) detection to derive additional information, and can also be visually presented to a human researcher for understanding. In this paper, we present three relatively small-scale problem-specific case studies to demonstrate that such an approach is very successful in replicating a neuroscience expert's mental model of object-object associations entirely by means of text mining. These preliminary results provide the confidence that this type of text mining based research approach provides an extremely powerful tool to better understand the literature and drive novel discovery for the neuroscience community.

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.

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

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

Advances in Machine Learning and Data Mining for Astronomy

NASA Astrophysics Data System (ADS)

Way, Michael J.; Scargle, Jeffrey D.; Ali, Kamal M.; Srivastava, Ashok N.

2012-03-01

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book's introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications. With contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community.

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

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).

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.

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.

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.

Identifying Understudied Nuclear Reactions by Text-mining the EXFOR Experimental Nuclear Reaction Library

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

Hirdt, J.A.; Brown, D.A., E-mail: dbrown@bnl.gov

The EXFOR library contains the largest collection of experimental nuclear reaction data available as well as the data's bibliographic information and experimental details. We text-mined the REACTION and MONITOR fields of the ENTRYs in the EXFOR library in order to identify understudied reactions and quantities. Using the results of the text-mining, we created an undirected graph from the EXFOR datasets with each graph node representing a single reaction and quantity and graph links representing the various types of connections between these reactions and quantities. This graph is an abstract representation of the connections in EXFOR, similar to graphs of socialmore » networks, authorship networks, etc. We use various graph theoretical tools to identify important yet understudied reactions and quantities in EXFOR. Although we identified a few cross sections relevant for shielding applications and isotope production, mostly we identified charged particle fluence monitor cross sections. As a side effect of this work, we learn that our abstract graph is typical of other real-world graphs.« less

Knowledge acquisition, semantic text mining, and security risks in health and biomedical informatics

PubMed Central

Huang, Jingshan; Dou, Dejing; Dang, Jiangbo; Pardue, J Harold; Qin, Xiao; Huan, Jun; Gerthoffer, William T; Tan, Ming

2012-01-01

Computational techniques have been adopted in medical and biological systems for a long time. There is no doubt that the development and application of computational methods will render great help in better understanding biomedical and biological functions. Large amounts of datasets have been produced by biomedical and biological experiments and simulations. In order for researchers to gain knowledge from original data, nontrivial transformation is necessary, which is regarded as a critical link in the chain of knowledge acquisition, sharing, and reuse. Challenges that have been encountered include: how to efficiently and effectively represent human knowledge in formal computing models, how to take advantage of semantic text mining techniques rather than traditional syntactic text mining, and how to handle security issues during the knowledge sharing and reuse. This paper summarizes the state-of-the-art in these research directions. We aim to provide readers with an introduction of major computing themes to be applied to the medical and biological research. PMID:22371823

Chemical named entities recognition: a review on approaches and applications.

PubMed

Eltyeb, Safaa; Salim, Naomie

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.

Identifying Understudied Nuclear Reactions by Text-mining the EXFOR Experimental Nuclear Reaction Library

NASA Astrophysics Data System (ADS)

Hirdt, J. A.; Brown, D. A.

2016-01-01

The EXFOR library contains the largest collection of experimental nuclear reaction data available as well as the data's bibliographic information and experimental details. We text-mined the REACTION and MONITOR fields of the ENTRYs in the EXFOR library in order to identify understudied reactions and quantities. Using the results of the text-mining, we created an undirected graph from the EXFOR datasets with each graph node representing a single reaction and quantity and graph links representing the various types of connections between these reactions and quantities. This graph is an abstract representation of the connections in EXFOR, similar to graphs of social networks, authorship networks, etc. We use various graph theoretical tools to identify important yet understudied reactions and quantities in EXFOR. Although we identified a few cross sections relevant for shielding applications and isotope production, mostly we identified charged particle fluence monitor cross sections. As a side effect of this work, we learn that our abstract graph is typical of other real-world graphs.

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…

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.

Summary of the BioLINK SIG 2013 meeting at ISMB/ECCB 2013.

PubMed

Verspoor, Karin; Shatkay, Hagit; Hirschman, Lynette; Blaschke, Christian; Valencia, Alfonso

2015-01-15

The ISMB Special Interest Group on Linking Literature, Information and Knowledge for Biology (BioLINK) organized a one-day workshop at ISMB/ECCB 2013 in Berlin, Germany. The theme of the workshop was 'Roles for text mining in biomedical knowledge discovery and translational medicine'. This summary reviews the outcomes of the workshop. Meeting themes included concept annotation methods and applications, extraction of biological relationships and the use of text-mined data for biological data analysis. All articles are available at http://biolinksig.org/proceedings-online/. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Mining free-text medical records for companion animal enteric syndrome surveillance.

PubMed

Anholt, R M; Berezowski, J; Jamal, I; Ribble, C; Stephen, C

2014-03-01

Large amounts of animal health care data are present in veterinary electronic medical records (EMR) and they present an opportunity for companion animal disease surveillance. Veterinary patient records are largely in free-text without clinical coding or fixed vocabulary. Text-mining, a computer and information technology application, is needed to identify cases of interest and to add structure to the otherwise unstructured data. In this study EMR's were extracted from veterinary management programs of 12 participating veterinary practices and stored in a data warehouse. Using commercially available text-mining software (WordStat™), we developed a categorization dictionary that could be used to automatically classify and extract enteric syndrome cases from the warehoused electronic medical records. The diagnostic accuracy of the text-miner for retrieving cases of enteric syndrome was measured against human reviewers who independently categorized a random sample of 2500 cases as enteric syndrome positive or negative. Compared to the reviewers, the text-miner retrieved cases with enteric signs with a sensitivity of 87.6% (95%CI, 80.4-92.9%) and a specificity of 99.3% (95%CI, 98.9-99.6%). Automatic and accurate detection of enteric syndrome cases provides an opportunity for community surveillance of enteric pathogens in companion animals. Copyright © 2014 Elsevier B.V. All rights reserved.

Application of Ferulic Acid for Alzheimer’s Disease: Combination of Text Mining and Experimental Validation

PubMed Central

Meng, Guilin; Meng, Xiulin; Ma, Xiaoye; Zhang, Gengping; Hu, Xiaolin; Jin, Aiping; Liu, Xueyuan

2018-01-01

Alzheimer’s disease (AD) is an increasing concern in human health. Despite significant research, highly effective drugs to treat AD are lacking. The present study describes the text mining process to identify drug candidates from a traditional Chinese medicine (TCM) database, along with associated protein target mechanisms. We carried out text mining to identify literatures that referenced both AD and TCM and focused on identifying compounds and protein targets of interest. After targeting one potential TCM candidate, corresponding protein-protein interaction (PPI) networks were assembled in STRING to decipher the most possible mechanism of action. This was followed by validation using Western blot and co-immunoprecipitation in an AD cell model. The text mining strategy using a vast amount of AD-related literature and the TCM database identified curcumin, whose major component was ferulic acid (FA). This was used as a key candidate compound for further study. Using the top calculated interaction score in STRING, BACE1 and MMP2 were implicated in the activity of FA in AD. Exposure of SHSY5Y-APP cells to FA resulted in the decrease in expression levels of BACE-1 and APP, while the expression of MMP-2 and MMP-9 increased in a dose-dependent manner. This suggests that FA induced BACE1 and MMP2 pathways maybe novel potential mechanisms involved in AD. The text mining of literature and TCM database related to AD suggested FA as a promising TCM ingredient for the treatment of AD. Potential mechanisms interconnected and integrated with Aβ aggregation inhibition and extracellular matrix remodeling underlying the activity of FA were identified using in vitro studies. PMID:29896095

Application of Ferulic Acid for Alzheimer's Disease: Combination of Text Mining and Experimental Validation.

PubMed

Meng, Guilin; Meng, Xiulin; Ma, Xiaoye; Zhang, Gengping; Hu, Xiaolin; Jin, Aiping; Zhao, Yanxin; Liu, Xueyuan

2018-01-01

Alzheimer's disease (AD) is an increasing concern in human health. Despite significant research, highly effective drugs to treat AD are lacking. The present study describes the text mining process to identify drug candidates from a traditional Chinese medicine (TCM) database, along with associated protein target mechanisms. We carried out text mining to identify literatures that referenced both AD and TCM and focused on identifying compounds and protein targets of interest. After targeting one potential TCM candidate, corresponding protein-protein interaction (PPI) networks were assembled in STRING to decipher the most possible mechanism of action. This was followed by validation using Western blot and co-immunoprecipitation in an AD cell model. The text mining strategy using a vast amount of AD-related literature and the TCM database identified curcumin, whose major component was ferulic acid (FA). This was used as a key candidate compound for further study. Using the top calculated interaction score in STRING, BACE1 and MMP2 were implicated in the activity of FA in AD. Exposure of SHSY5Y-APP cells to FA resulted in the decrease in expression levels of BACE-1 and APP, while the expression of MMP-2 and MMP-9 increased in a dose-dependent manner. This suggests that FA induced BACE1 and MMP2 pathways maybe novel potential mechanisms involved in AD. The text mining of literature and TCM database related to AD suggested FA as a promising TCM ingredient for the treatment of AD. Potential mechanisms interconnected and integrated with Aβ aggregation inhibition and extracellular matrix remodeling underlying the activity of FA were identified using in vitro studies.

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.

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…

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

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.

Text mining for search term development in systematic reviewing: A discussion of some methods and challenges.

PubMed

Stansfield, Claire; O'Mara-Eves, Alison; Thomas, James

2017-09-01

Using text mining to aid the development of database search strings for topics described by diverse terminology has potential benefits for systematic reviews; however, methods and tools for accomplishing this are poorly covered in the research methods literature. We briefly review the literature on applications of text mining for search term development for systematic reviewing. We found that the tools can be used in 5 overarching ways: improving the precision of searches; identifying search terms to improve search sensitivity; aiding the translation of search strategies across databases; searching and screening within an integrated system; and developing objectively derived search strategies. Using a case study and selected examples, we then reflect on the utility of certain technologies (term frequency-inverse document frequency and Termine, term frequency, and clustering) in improving the precision and sensitivity of searches. Challenges in using these tools are discussed. The utility of these tools is influenced by the different capabilities of the tools, the way the tools are used, and the text that is analysed. Increased awareness of how the tools perform facilitates the further development of methods for their use in systematic reviews. Copyright © 2017 John Wiley & Sons, Ltd.

Weighted mining of massive collections of [Formula: see text]-values by convex optimization.

PubMed

Dobriban, Edgar

2018-06-01

Researchers in data-rich disciplines-think of computational genomics and observational cosmology-often wish to mine large bodies of [Formula: see text]-values looking for significant effects, while controlling the false discovery rate or family-wise error rate. Increasingly, researchers also wish to prioritize certain hypotheses, for example, those thought to have larger effect sizes, by upweighting, and to impose constraints on the underlying mining, such as monotonicity along a certain sequence. We introduce Princessp , a principled method for performing weighted multiple testing by constrained convex optimization. Our method elegantly allows one to prioritize certain hypotheses through upweighting and to discount others through downweighting, while constraining the underlying weights involved in the mining process. When the [Formula: see text]-values derive from monotone likelihood ratio families such as the Gaussian means model, the new method allows exact solution of an important optimal weighting problem previously thought to be non-convex and computationally infeasible. Our method scales to massive data set sizes. We illustrate the applications of Princessp on a series of standard genomics data sets and offer comparisons with several previous 'standard' methods. Princessp offers both ease of operation and the ability to scale to extremely large problem sizes. The method is available as open-source software from github.com/dobriban/pvalue_weighting_matlab (accessed 11 October 2017).

Zsyntax: a formal language for molecular biology with projected applications in text mining and biological prediction.

PubMed

Boniolo, Giovanni; D'Agostino, Marcello; Di Fiore, Pier Paolo

2010-03-03

We propose a formal language that allows for transposing biological information precisely and rigorously into machine-readable information. This language, which we call Zsyntax (where Z stands for the Greek word zetaomegaeta, life), is grounded on a particular type of non-classical logic, and it can be used to write algorithms and computer programs. We present it as a first step towards a comprehensive formal language for molecular biology in which any biological process can be written and analyzed as a sort of logical "deduction". Moreover, we illustrate the potential value of this language, both in the field of text mining and in that of biological prediction.

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

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…

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

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.

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 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.

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

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.

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.

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

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.

Adaptive semantic tag mining from heterogeneous clinical research texts.

PubMed

Hao, T; Weng, C

2015-01-01

To develop an adaptive approach to mine frequent semantic tags (FSTs) from heterogeneous clinical research texts. We develop a "plug-n-play" framework that integrates replaceable unsupervised kernel algorithms with formatting, functional, and utility wrappers for FST mining. Temporal information identification and semantic equivalence detection were two example functional wrappers. We first compared this approach's recall and efficiency for mining FSTs from ClinicalTrials.gov to that of a recently published tag-mining algorithm. Then we assessed this approach's adaptability to two other types of clinical research texts: clinical data requests and clinical trial protocols, by comparing the prevalence trends of FSTs across three texts. Our approach increased the average recall and speed by 12.8% and 47.02% respectively upon the baseline when mining FSTs from ClinicalTrials.gov, and maintained an overlap in relevant FSTs with the base- line ranging between 76.9% and 100% for varying FST frequency thresholds. The FSTs saturated when the data size reached 200 documents. Consistent trends in the prevalence of FST were observed across the three texts as the data size or frequency threshold changed. This paper contributes an adaptive tag-mining framework that is scalable and adaptable without sacrificing its recall. This component-based architectural design can be potentially generalizable to improve the adaptability of other clinical text mining methods.

Design and Implementation of a Comprehensive Web-based Survey for Ovarian Cancer Survivorship with an Analysis of Prediagnosis Symptoms via Text Mining

PubMed Central

Sun, Jiayang; Bogie, Kath M; Teagno, Joe; Sun, Yu-Hsiang (Sam); Carter, Rebecca R; Cui, Licong; Zhang, Guo-Qiang

2014-01-01

Ovarian cancer (OvCa) is the most lethal gynecologic disease in the United States, with an overall 5-year survival rate of 44.5%, about half of the 89.2% for all breast cancer patients. To identify factors that possibly contribute to the long-term survivorship of women with OvCa, we conducted a comprehensive online Ovarian Cancer Survivorship Survey from 2009 to 2013. This paper presents the design and implementation of our survey, introduces its resulting data source, the OVA-CRADLE™ (Clinical Research Analytics and Data Lifecycle Environment), and illustrates a sample application of the survey and data by an analysis of prediagnosis symptoms, using text mining and statistics. The OVA-CRADLE™ is an application of our patented Physio-MIMI technology, facilitating Web-based access, online query and exploration of data. The prediagnostic symptoms and association of early-stage OvCa diagnosis with endometriosis provide potentially important indicators for future studies in this field. PMID:25861211

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.

Effective use of latent semantic indexing and computational linguistics in biological and biomedical applications.

PubMed

Chen, Hongyu; Martin, Bronwen; Daimon, Caitlin M; Maudsley, Stuart

2013-01-01

Text mining is rapidly becoming an essential technique for the annotation and analysis of large biological data sets. Biomedical literature currently increases at a rate of several thousand papers per week, making automated information retrieval methods the only feasible method of managing this expanding corpus. With the increasing prevalence of open-access journals and constant growth of publicly-available repositories of biomedical literature, literature mining has become much more effective with respect to the extraction of biomedically-relevant data. In recent years, text mining of popular databases such as MEDLINE has evolved from basic term-searches to more sophisticated natural language processing techniques, indexing and retrieval methods, structural analysis and integration of literature with associated metadata. In this review, we will focus on Latent Semantic Indexing (LSI), a computational linguistics technique increasingly used for a variety of biological purposes. It is noted for its ability to consistently outperform benchmark Boolean text searches and co-occurrence models at information retrieval and its power to extract indirect relationships within a data set. LSI has been used successfully to formulate new hypotheses, generate novel connections from existing data, and validate empirical data.

Large-Scale Event Extraction from Literature with Multi-Level Gene Normalization

PubMed Central

Wei, Chih-Hsuan; Hakala, Kai; Pyysalo, Sampo; Ananiadou, Sophia; Kao, Hung-Yu; Lu, Zhiyong; Salakoski, Tapio; Van de Peer, Yves; Ginter, Filip

2013-01-01

Text mining for the life sciences aims to aid database curation, knowledge summarization and information retrieval through the automated processing of biomedical texts. To provide comprehensive coverage and enable full integration with existing biomolecular database records, it is crucial that text mining tools scale up to millions of articles and that their analyses can be unambiguously linked to information recorded in resources such as UniProt, KEGG, BioGRID and NCBI databases. In this study, we investigate how fully automated text mining of complex biomolecular events can be augmented with a normalization strategy that identifies biological concepts in text, mapping them to identifiers at varying levels of granularity, ranging from canonicalized symbols to unique gene and proteins and broad gene families. To this end, we have combined two state-of-the-art text mining components, previously evaluated on two community-wide challenges, and have extended and improved upon these methods by exploiting their complementary nature. Using these systems, we perform normalization and event extraction to create a large-scale resource that is publicly available, unique in semantic scope, and covers all 21.9 million PubMed abstracts and 460 thousand PubMed Central open access full-text articles. This dataset contains 40 million biomolecular events involving 76 million gene/protein mentions, linked to 122 thousand distinct genes from 5032 species across the full taxonomic tree. Detailed evaluations and analyses reveal promising results for application of this data in database and pathway curation efforts. The main software components used in this study are released under an open-source license. Further, the resulting dataset is freely accessible through a novel API, providing programmatic and customized access (http://www.evexdb.org/api/v001/). Finally, to allow for large-scale bioinformatic analyses, the entire resource is available for bulk download from http://evexdb.org/download/, under the Creative Commons – Attribution – Share Alike (CC BY-SA) license. PMID:23613707

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

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.

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.

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.

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

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 ...

[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.

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.

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

Domain Visualization Using VxInsight[R] for Science and Technology Management.

ERIC Educational Resources Information Center

Boyack, Kevin W.; Wylie, Brian N.; Davidson, George S.

2002-01-01

Presents the application of a knowledge visualization tool, VxInsight[R], to enable domain analysis for science and technology management. Uses data mining from sources of bibliographic information to define subsets of relevant information and discusses citation mapping, text mapping, and journal mapping. (Author/LRW)

Gene prioritization and clustering by multi-view text mining

PubMed Central

2010-01-01

Background Text mining has become a useful tool for biologists trying to understand the genetics of diseases. In particular, it can help identify the most interesting candidate genes for a disease for further experimental analysis. Many text mining approaches have been introduced, but the effect of disease-gene identification varies in different text mining models. Thus, the idea of incorporating more text mining models may be beneficial to obtain more refined and accurate knowledge. However, how to effectively combine these models still remains a challenging question in machine learning. In particular, it is a non-trivial issue to guarantee that the integrated model performs better than the best individual model. Results We present a multi-view approach to retrieve biomedical knowledge using different controlled vocabularies. These controlled vocabularies are selected on the basis of nine well-known bio-ontologies and are applied to index the vast amounts of gene-based free-text information available in the MEDLINE repository. The text mining result specified by a vocabulary is considered as a view and the obtained multiple views are integrated by multi-source learning algorithms. We investigate the effect of integration in two fundamental computational disease gene identification tasks: gene prioritization and gene clustering. The performance of the proposed approach is systematically evaluated and compared on real benchmark data sets. In both tasks, the multi-view approach demonstrates significantly better performance than other comparing methods. Conclusions In practical research, the relevance of specific vocabulary pertaining to the task is usually unknown. In such case, multi-view text mining is a superior and promising strategy for text-based disease gene identification. PMID:20074336

A Study on Environmental Research Trends Using Text-Mining Method - Focus on Spatial information and ICT -

NASA Astrophysics Data System (ADS)

Lee, M. J.; Oh, K. Y.; Joung-ho, L.

2016-12-01

Recently there are many research about analysing the interaction between entities by text-mining analysis in various fields. In this paper, we aimed to quantitatively analyse research-trends in the area of environmental research relating either spatial information or ICT (Information and Communications Technology) by Text-mining analysis. To do this, we applied low-dimensional embedding method, clustering analysis, and association rule to find meaningful associative patterns of key words frequently appeared in the articles. As the authors suppose that KCI (Korea Citation Index) articles reflect academic demands, total 1228 KCI articles that have been published from 1996 to 2015 were reviewed and analysed by Text-mining method. First, we derived KCI articles from NDSL(National Discovery for Science Leaders) site. And then we pre-processed their key-words elected from abstract and then classified those in separable sectors. We investigated the appearance rates and association rule of key-words for articles in the two fields: spatial-information and ICT. In order to detect historic trends, analysis was conducted separately for the four periods: 1996-2000, 2001-2005, 2006-2010, 2011-2015. These analysis were conducted with the usage of R-software. As a result, we conformed that environmental research relating spatial information mainly focused upon such fields as `GIS(35%)', `Remote-Sensing(25%)', `environmental theme map(15.7%)'. Next, `ICT technology(23.6%)', `ICT service(5.4%)', `mobile(24%)', `big data(10%)', `AI(7%)' are primarily emerging from environmental research relating ICT. Thus, from the analysis results, this paper asserts that research trends and academic progresses are well-structured to review recent spatial information and ICT technology and the outcomes of the analysis can be an adequate guidelines to establish environment policies and strategies. KEY WORDS: Big data, Test-mining, Environmental research, Spatial-information, ICT Acknowledgements: The authors appreciate the support that this study has received from `Building application frame of environmental issues, to respond to the latest ICT trends'.

A greenhouse trial to investigate the ameliorative properties of biosolids and plants on physicochemical conditions of iron ore tailings: Implications for an iron ore mine site remediation.

PubMed

Cele, Emmanuel Nkosinathi; Maboeta, Mark

2016-01-01

An iron ore mine site in Swaziland is currently (2015) in a derelict state as a consequence of past (1964-1988) and present (2011 - current) iron ore mining operations. In order to control problems associated with mine wastes, the Swaziland Water Services Corporation (SWSC) recently (2013) proposed the application of biosolids in sites degraded by mining operations. It is thought that this practice could generally improve soil conditions and enhance plant reestablishment. More importantly, the SWSC foresees this as a potential solution to the biosolids disposal problems. In order to investigate the effects of biosolids and plants in soil physicochemical conditions of iron mine soils, we conducted two plant growth trials. Trial 1 consisted of tailings that received biosolids and topsoil (TUSB mix) while in trial 2, tailings received biosolids only (TB mix). In the two trials, the application rates of 0 (control), 10, 25, 50, 75 and 100 t ha(-1) were used. After 30 days of equilibration, 25 seeds of Cynodon dactylon were sown in each pot and thinned to 10 plants after 4 weeks. Plants were watered twice weekly and remained under greenhouse conditions for 12 weeks, subsequent to which soils were subjected to chemical analysis. According to the results obtained, there were significant improvements in soil parameters related to fertility such as organic matter (OM), water holding capacity (WHC), cation exchange capacity (CEC), ammonium [Formula: see text] , magnesium (Mg(2+)), calcium (Ca(2+)) and phosphorus ( [Formula: see text] ). With regard to heavy metals, biosolids led to significant increases in soil total concentrations of Cu, Zn, Cd, Hg and Pb. The higher concentrations of Zn and Cu in treated tailings compared to undisturbed adjacent soils are a cause for concern because in the field, this might work against the broader objectives of mine soil remediation, which include the recolonization of reclaimed sites by soil-dwelling organisms. Therefore, while biosolids contain important nutrients that may greatly improve physicochemical conditions and enhance vegetation reestablishment in mined soils, the threat of the build-up of higher levels of trace elements in treated tailings compared to surrounding adjacent soils must not be underestimated. Copyright © 2015 Elsevier Ltd. All rights reserved.

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.

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.

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

Mining of relations between proteins over biomedical scientific literature using a deep-linguistic approach.

PubMed

Rinaldi, Fabio; Schneider, Gerold; Kaljurand, Kaarel; Hess, Michael; Andronis, Christos; Konstandi, Ourania; Persidis, Andreas

2007-02-01

The amount of new discoveries (as published in the scientific literature) in the biomedical area is growing at an exponential rate. This growth makes it very difficult to filter the most relevant results, and thus the extraction of the core information becomes very expensive. Therefore, there is a growing interest in text processing approaches that can deliver selected information from scientific publications, which can limit the amount of human intervention normally needed to gather those results. This paper presents and evaluates an approach aimed at automating the process of extracting functional relations (e.g. interactions between genes and proteins) from scientific literature in the biomedical domain. The approach, using a novel dependency-based parser, is based on a complete syntactic analysis of the corpus. We have implemented a state-of-the-art text mining system for biomedical literature, based on a deep-linguistic, full-parsing approach. The results are validated on two different corpora: the manually annotated genomics information access (GENIA) corpus and the automatically annotated arabidopsis thaliana circadian rhythms (ATCR) corpus. We show how a deep-linguistic approach (contrary to common belief) can be used in a real world text mining application, offering high-precision relation extraction, while at the same time retaining a sufficient recall.

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

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…

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

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.

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/.

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.

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.

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)

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)

Multi-dimensional classification of biomedical text: Toward automated, practical provision of high-utility text to diverse users

PubMed Central

Shatkay, Hagit; Pan, Fengxia; Rzhetsky, Andrey; Wilbur, W. John

2008-01-01

Motivation: Much current research in biomedical text mining is concerned with serving biologists by extracting certain information from scientific text. We note that there is no ‘average biologist’ client; different users have distinct needs. For instance, as noted in past evaluation efforts (BioCreative, TREC, KDD) database curators are often interested in sentences showing experimental evidence and methods. Conversely, lab scientists searching for known information about a protein may seek facts, typically stated with high confidence. Text-mining systems can target specific end-users and become more effective, if the system can first identify text regions rich in the type of scientific content that is of interest to the user, retrieve documents that have many such regions, and focus on fact extraction from these regions. Here, we study the ability to characterize and classify such text automatically. We have recently introduced a multi-dimensional categorization and annotation scheme, developed to be applicable to a wide variety of biomedical documents and scientific statements, while intended to support specific biomedical retrieval and extraction tasks. Results: The annotation scheme was applied to a large corpus in a controlled effort by eight independent annotators, where three individual annotators independently tagged each sentence. We then trained and tested machine learning classifiers to automatically categorize sentence fragments based on the annotation. We discuss here the issues involved in this task, and present an overview of the results. The latter strongly suggest that automatic annotation along most of the dimensions is highly feasible, and that this new framework for scientific sentence categorization is applicable in practice. Contact: shatkay@cs.queensu.ca PMID:18718948

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

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.

40 CFR 434.30 - Applicability; description of the acid or ferruginous mine drainage subcategory.

Code of Federal Regulations, 2014 CFR

2014-07-01

... SOURCE CATEGORY BPT, BAT, BCT LIMITATIONS AND NEW SOURCE PERFORMANCE STANDARDS Acid or Ferruginous Mine Drainage § 434.30 Applicability; description of the acid or ferruginous mine drainage subcategory. The provisions of this subpart are applicable to acid or ferruginous mine drainage from an active mining area...

40 CFR 434.30 - Applicability; description of the acid or ferruginous mine drainage subcategory.

Code of Federal Regulations, 2010 CFR

2010-07-01

... BPT, BAT, BCT LIMITATIONS AND NEW SOURCE PERFORMANCE STANDARDS Acid or Ferruginous Mine Drainage § 434.30 Applicability; description of the acid or ferruginous mine drainage subcategory. The provisions of this subpart are applicable to acid or ferruginous mine drainage from an active mining area resulting...

40 CFR 434.30 - Applicability; description of the acid or ferruginous mine drainage subcategory.

Code of Federal Regulations, 2011 CFR

2011-07-01

... BPT, BAT, BCT LIMITATIONS AND NEW SOURCE PERFORMANCE STANDARDS Acid or Ferruginous Mine Drainage § 434.30 Applicability; description of the acid or ferruginous mine drainage subcategory. The provisions of this subpart are applicable to acid or ferruginous mine drainage from an active mining area resulting...

40 CFR 434.30 - Applicability; description of the acid or ferruginous mine drainage subcategory.

Code of Federal Regulations, 2012 CFR

2012-07-01

... SOURCE CATEGORY BPT, BAT, BCT LIMITATIONS AND NEW SOURCE PERFORMANCE STANDARDS Acid or Ferruginous Mine Drainage § 434.30 Applicability; description of the acid or ferruginous mine drainage subcategory. The provisions of this subpart are applicable to acid or ferruginous mine drainage from an active mining area...

40 CFR 434.30 - Applicability; description of the acid or ferruginous mine drainage subcategory.

Code of Federal Regulations, 2013 CFR

2013-07-01

... SOURCE CATEGORY BPT, BAT, BCT LIMITATIONS AND NEW SOURCE PERFORMANCE STANDARDS Acid or Ferruginous Mine Drainage § 434.30 Applicability; description of the acid or ferruginous mine drainage subcategory. The provisions of this subpart are applicable to acid or ferruginous mine drainage from an active mining area...

A best-fit model for concept vectors in biomedical research grants.

PubMed

Johnson, Calvin; Lau, William; Bhandari, Archna; Hays, Timothy

2008-11-06

The Research, Condition, and Disease Categorization (RCDC) project was created to standardize budget reporting by research topic. Text mining techniques have been implemented to classify NIH grant applications into proper research and disease categories. A best-fit model is shown to achieve classification performance rivaling that of concept vectors produced by human experts.

Fuzzy and rough formal concept analysis: a survey

NASA Astrophysics Data System (ADS)

Poelmans, Jonas; Ignatov, Dmitry I.; Kuznetsov, Sergei O.; Dedene, Guido

2014-02-01

Formal Concept Analysis (FCA) is a mathematical technique that has been extensively applied to Boolean data in knowledge discovery, information retrieval, web mining, etc. applications. During the past years, the research on extending FCA theory to cope with imprecise and incomplete information made significant progress. In this paper, we give a systematic overview of the more than 120 papers published between 2003 and 2011 on FCA with fuzzy attributes and rough FCA. We applied traditional FCA as a text-mining instrument to 1072 papers mentioning FCA in the abstract. These papers were formatted in pdf files and using a thesaurus with terms referring to research topics, we transformed them into concept lattices. These lattices were used to analyze and explore the most prominent research topics within the FCA with fuzzy attributes and rough FCA research communities. FCA turned out to be an ideal metatechnique for representing large volumes of unstructured texts.

Inferring the semantic relationships of words within an ontology using random indexing: applications to pharmacogenomics.

PubMed

Percha, Bethany; Altman, Russ B

2013-01-01

The biomedical literature presents a uniquely challenging text mining problem. Sentences are long and complex, the subject matter is highly specialized with a distinct vocabulary, and producing annotated training data for this domain is time consuming and expensive. In this environment, unsupervised text mining methods that do not rely on annotated training data are valuable. Here we investigate the use of random indexing, an automated method for producing vector-space semantic representations of words from large, unlabeled corpora, to address the problem of term normalization in sentences describing drugs and genes. We show that random indexing produces similarity scores that capture some of the structure of PHARE, a manually curated ontology of pharmacogenomics concepts. We further show that random indexing can be used to identify likely word candidates for inclusion in the ontology, and can help localize these new labels among classes and roles within the ontology.

Inferring the semantic relationships of words within an ontology using random indexing: applications to pharmacogenomics

PubMed Central

Percha, Bethany; Altman, Russ B.

2013-01-01

The biomedical literature presents a uniquely challenging text mining problem. Sentences are long and complex, the subject matter is highly specialized with a distinct vocabulary, and producing annotated training data for this domain is time consuming and expensive. In this environment, unsupervised text mining methods that do not rely on annotated training data are valuable. Here we investigate the use of random indexing, an automated method for producing vector-space semantic representations of words from large, unlabeled corpora, to address the problem of term normalization in sentences describing drugs and genes. We show that random indexing produces similarity scores that capture some of the structure of PHARE, a manually curated ontology of pharmacogenomics concepts. We further show that random indexing can be used to identify likely word candidates for inclusion in the ontology, and can help localize these new labels among classes and roles within the ontology. PMID:24551397

[Exploring pharmacological principle of Artemisia carvifolia with textmining technology].

PubMed

Zhao, Yu-Ping; Wang, Hui; Yang, Guang; Qiu, Zhi-Dong; Qu, Xiao-Bo; Zhang, Xiao-Bo

2016-08-01

To explore the pharmacological principle of Artemisia carvifolia,the text mining technique was used. All the references of A. carvifolia were collected from PubMed database, and then the rules of the main ingredient,relative diseases, organs, tissues, proteins and metabolites were analyzed. Finally, a network was set up. Then it was found that the main ingredients included sesquiterpenoids,flavonoids,and volatileoils.The diseases such as malaria, cerebral malaria, falciparum malaria, visceral leishmaniasis and systemic lupus erythematosus were often treated with A. carvifolia. In association in organ were the liver, skin, trachea,lungs,and spleen.Correlations with tissues were mainly including macrophages, T lymphocytes, blood vessels, epithelial cells.The protein was correlation with it involved CYP450, PI3K, TNF-α, AASDPPT, DNA polymerase and so on. Comprehensive and systematic treatment principle of A. carvifolia was obtained by text mining, which was helpful in clinical application. Copyright© by the Chinese Pharmaceutical Association.

Text mining approach to predict hospital admissions using early medical records from the emergency department.

PubMed

Lucini, Filipe R; S Fogliatto, Flavio; C da Silveira, Giovani J; L Neyeloff, Jeruza; Anzanello, Michel J; de S Kuchenbecker, Ricardo; D Schaan, Beatriz

2017-04-01

Emergency department (ED) overcrowding is a serious issue for hospitals. Early information on short-term inward bed demand from patients receiving care at the ED may reduce the overcrowding problem, and optimize the use of hospital resources. In this study, we use text mining methods to process data from early ED patient records using the SOAP framework, and predict future hospitalizations and discharges. We try different approaches for pre-processing of text records and to predict hospitalization. Sets-of-words are obtained via binary representation, term frequency, and term frequency-inverse document frequency. Unigrams, bigrams and trigrams are tested for feature formation. Feature selection is based on χ 2 and F-score metrics. In the prediction module, eight text mining methods are tested: Decision Tree, Random Forest, Extremely Randomized Tree, AdaBoost, Logistic Regression, Multinomial Naïve Bayes, Support Vector Machine (Kernel linear) and Nu-Support Vector Machine (Kernel linear). Prediction performance is evaluated by F1-scores. Precision and Recall values are also informed for all text mining methods tested. Nu-Support Vector Machine was the text mining method with the best overall performance. Its average F1-score in predicting hospitalization was 77.70%, with a standard deviation (SD) of 0.66%. The method could be used to manage daily routines in EDs such as capacity planning and resource allocation. Text mining could provide valuable information and facilitate decision-making by inward bed management teams. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

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.

Vaccine adverse event text mining system for extracting features from vaccine safety reports.

PubMed

Botsis, Taxiarchis; Buttolph, Thomas; Nguyen, Michael D; Winiecki, Scott; Woo, Emily Jane; Ball, Robert

2012-01-01

To develop and evaluate a text mining system for extracting key clinical features from vaccine adverse event reporting system (VAERS) narratives to aid in the automated review of adverse event reports. Based upon clinical significance to VAERS reviewing physicians, we defined the primary (diagnosis and cause of death) and secondary features (eg, symptoms) for extraction. We built a novel vaccine adverse event text mining (VaeTM) system based on a semantic text mining strategy. The performance of VaeTM was evaluated using a total of 300 VAERS reports in three sequential evaluations of 100 reports each. Moreover, we evaluated the VaeTM contribution to case classification; an information retrieval-based approach was used for the identification of anaphylaxis cases in a set of reports and was compared with two other methods: a dedicated text classifier and an online tool. The performance metrics of VaeTM were text mining metrics: recall, precision and F-measure. We also conducted a qualitative difference analysis and calculated sensitivity and specificity for classification of anaphylaxis cases based on the above three approaches. VaeTM performed best in extracting diagnosis, second level diagnosis, drug, vaccine, and lot number features (lenient F-measure in the third evaluation: 0.897, 0.817, 0.858, 0.874, and 0.914, respectively). In terms of case classification, high sensitivity was achieved (83.1%); this was equal and better compared to the text classifier (83.1%) and the online tool (40.7%), respectively. Our VaeTM implementation of a semantic text mining strategy shows promise in providing accurate and efficient extraction of key features from VAERS narratives.

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.

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.

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.

Application and Exploration of Big Data Mining in Clinical Medicine.

PubMed

Zhang, Yue; Guo, Shu-Li; Han, Li-Na; Li, Tie-Ling

2016-03-20

To review theories and technologies of big data mining and their application in clinical medicine. Literatures published in English or Chinese regarding theories and technologies of big data mining and the concrete applications of data mining technology in clinical medicine were obtained from PubMed and Chinese Hospital Knowledge Database from 1975 to 2015. Original articles regarding big data mining theory/technology and big data mining's application in the medical field were selected. This review characterized the basic theories and technologies of big data mining including fuzzy theory, rough set theory, cloud theory, Dempster-Shafer theory, artificial neural network, genetic algorithm, inductive learning theory, Bayesian network, decision tree, pattern recognition, high-performance computing, and statistical analysis. The application of big data mining in clinical medicine was analyzed in the fields of disease risk assessment, clinical decision support, prediction of disease development, guidance of rational use of drugs, medical management, and evidence-based medicine. Big data mining has the potential to play an important role in clinical medicine.

40 CFR 434.40 - Applicability; description of the alkaline mine drainage subcategory.

Code of Federal Regulations, 2010 CFR

2010-07-01

... alkaline mine drainage subcategory. 434.40 Section 434.40 Protection of Environment ENVIRONMENTAL..., BCT LIMITATIONS AND NEW SOURCE PERFORMANCE STANDARDS Alkaline Mine Drainage § 434.40 Applicability; description of the alkaline mine drainage subcategory. The provisions of this subpart are applicable to...

40 CFR 434.40 - Applicability; description of the alkaline mine drainage subcategory.

Code of Federal Regulations, 2011 CFR

2011-07-01

... alkaline mine drainage subcategory. 434.40 Section 434.40 Protection of Environment ENVIRONMENTAL..., BCT LIMITATIONS AND NEW SOURCE PERFORMANCE STANDARDS Alkaline Mine Drainage § 434.40 Applicability; description of the alkaline mine drainage subcategory. The provisions of this subpart are applicable to...

E-Cigarette Social Media Messages: A Text Mining Analysis of Marketing and Consumer Conversations on Twitter

PubMed Central

2016-01-01

Background As the use of electronic cigarettes (e-cigarettes) rises, social media likely influences public awareness and perception of this emerging tobacco product. Objective This study examined the public conversation on Twitter to determine overarching themes and insights for trending topics from commercial and consumer users. Methods Text mining uncovered key patterns and important topics for e-cigarettes on Twitter. SAS Text Miner 12.1 software (SAS Institute Inc) was used for descriptive text mining to reveal the primary topics from tweets collected from March 24, 2015, to July 3, 2015, using a Python script in conjunction with Twitter’s streaming application programming interface. A total of 18 keywords related to e-cigarettes were used and resulted in a total of 872,544 tweets that were sorted into overarching themes through a text topic node for tweets (126,127) and retweets (114,451) that represented more than 1% of the conversation. Results While some of the final themes were marketing-focused, many topics represented diverse proponent and user conversations that included discussion of policies, personal experiences, and the differentiation of e-cigarettes from traditional tobacco, often by pointing to the lack of evidence for the harm or risks of e-cigarettes or taking the position that e-cigarettes should be promoted as smoking cessation devices. Conclusions These findings reveal that unique, large-scale public conversations are occurring on Twitter alongside e-cigarette advertising and promotion. Proponents and users are turning to social media to share knowledge, experience, and questions about e-cigarette use. Future research should focus on these unique conversations to understand how they influence attitudes towards and use of e-cigarettes. PMID:27956376

Block-suffix shifting: fast, simultaneous medical concept set identification in large medical record corpora.

PubMed

Liu, Ying; Lita, Lucian Vlad; Niculescu, Radu Stefan; Mitra, Prasenjit; Giles, C Lee

2008-11-06

Owing to new advances in computer hardware, large text databases have become more prevalent than ever.Automatically mining information from these databases proves to be a challenge due to slow pattern/string matching techniques. In this paper we present a new, fast multi-string pattern matching method based on the well known Aho-Chorasick algorithm. Advantages of our algorithm include:the ability to exploit the natural structure of text, the ability to perform significant character shifting, avoiding backtracking jumps that are not useful, efficiency in terms of matching time and avoiding the typical "sub-string" false positive errors.Our algorithm is applicable to many fields with free text, such as the health care domain and the scientific document field. In this paper, we apply the BSS algorithm to health care data and mine hundreds of thousands of medical concepts from a large Electronic Medical Record (EMR) corpora simultaneously and efficiently. Experimental results show the superiority of our algorithm when compared with the top of the line multi-string matching algorithms.

Addressing Information Proliferation: Applications of Information Extraction and Text Mining

ERIC Educational Resources Information Center

Li, Jingjing

2013-01-01

The advent of the Internet and the ever-increasing capacity of storage media have made it easy to store, deliver, and share enormous volumes of data, leading to a proliferation of information on the Web, in online libraries, on news wires, and almost everywhere in our daily lives. Since our ability to process and absorb this information remains…

Event-based text mining for biology and functional genomics

PubMed Central

Thompson, Paul; Nawaz, Raheel; McNaught, John; Kell, Douglas B.

2015-01-01

The assessment of genome function requires a mapping between genome-derived entities and biochemical reactions, and the biomedical literature represents a rich source of information about reactions between biological components. However, the increasingly rapid growth in the volume of literature provides both a challenge and an opportunity for researchers to isolate information about reactions of interest in a timely and efficient manner. In response, recent text mining research in the biology domain has been largely focused on the identification and extraction of ‘events’, i.e. categorised, structured representations of relationships between biochemical entities, from the literature. Functional genomics analyses necessarily encompass events as so defined. Automatic event extraction systems facilitate the development of sophisticated semantic search applications, allowing researchers to formulate structured queries over extracted events, so as to specify the exact types of reactions to be retrieved. This article provides an overview of recent research into event extraction. We cover annotated corpora on which systems are trained, systems that achieve state-of-the-art performance and details of the community shared tasks that have been instrumental in increasing the quality, coverage and scalability of recent systems. Finally, several concrete applications of event extraction are covered, together with emerging directions of research. PMID:24907365

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.

40 CFR 440.140 - Applicability; description of the gold placer mine subcategory.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 29 2010-07-01 2010-07-01 false Applicability; description of the gold... AGENCY (CONTINUED) EFFLUENT GUIDELINES AND STANDARDS ORE MINING AND DRESSING POINT SOURCE CATEGORY Gold Placer Mine Subcategory § 440.140 Applicability; description of the gold placer mine subcategory. (a) The...

76 FR 35213 - AJT Mining Properties, Inc.; Notice of Preliminary Permit Application Accepted for Filing and...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-16

... DEPARTMENT OF ENERGY Federal Energy Regulatory Commission [Project No. 14115-000] AJT Mining..., Motions To Intervene, and Competing Applications On March 21, 2011, AJT Mining Properties, Inc., filed an...,000 megawatt-hours. Applicant Contact: Mr. Scott Willis, AJT Mining Properties, Inc., 5601 Tonsgard...

Integrating text mining into the MGI biocuration workflow

PubMed Central

Dowell, K.G.; McAndrews-Hill, M.S.; Hill, D.P.; Drabkin, H.J.; Blake, J.A.

2009-01-01

A major challenge for functional and comparative genomics resource development is the extraction of data from the biomedical literature. Although text mining for biological data is an active research field, few applications have been integrated into production literature curation systems such as those of the model organism databases (MODs). Not only are most available biological natural language (bioNLP) and information retrieval and extraction solutions difficult to adapt to existing MOD curation workflows, but many also have high error rates or are unable to process documents available in those formats preferred by scientific journals. In September 2008, Mouse Genome Informatics (MGI) at The Jackson Laboratory initiated a search for dictionary-based text mining tools that we could integrate into our biocuration workflow. MGI has rigorous document triage and annotation procedures designed to identify appropriate articles about mouse genetics and genome biology. We currently screen ∼1000 journal articles a month for Gene Ontology terms, gene mapping, gene expression, phenotype data and other key biological information. Although we do not foresee that curation tasks will ever be fully automated, we are eager to implement named entity recognition (NER) tools for gene tagging that can help streamline our curation workflow and simplify gene indexing tasks within the MGI system. Gene indexing is an MGI-specific curation function that involves identifying which mouse genes are being studied in an article, then associating the appropriate gene symbols with the article reference number in the MGI database. Here, we discuss our search process, performance metrics and success criteria, and how we identified a short list of potential text mining tools for further evaluation. We provide an overview of our pilot projects with NCBO's Open Biomedical Annotator and Fraunhofer SCAI's ProMiner. In doing so, we prove the potential for the further incorporation of semi-automated processes into the curation of the biomedical literature. PMID:20157492

Integrating text mining into the MGI biocuration workflow.

PubMed

Dowell, K G; McAndrews-Hill, M S; Hill, D P; Drabkin, H J; Blake, J A

2009-01-01

A major challenge for functional and comparative genomics resource development is the extraction of data from the biomedical literature. Although text mining for biological data is an active research field, few applications have been integrated into production literature curation systems such as those of the model organism databases (MODs). Not only are most available biological natural language (bioNLP) and information retrieval and extraction solutions difficult to adapt to existing MOD curation workflows, but many also have high error rates or are unable to process documents available in those formats preferred by scientific journals.In September 2008, Mouse Genome Informatics (MGI) at The Jackson Laboratory initiated a search for dictionary-based text mining tools that we could integrate into our biocuration workflow. MGI has rigorous document triage and annotation procedures designed to identify appropriate articles about mouse genetics and genome biology. We currently screen approximately 1000 journal articles a month for Gene Ontology terms, gene mapping, gene expression, phenotype data and other key biological information. Although we do not foresee that curation tasks will ever be fully automated, we are eager to implement named entity recognition (NER) tools for gene tagging that can help streamline our curation workflow and simplify gene indexing tasks within the MGI system. Gene indexing is an MGI-specific curation function that involves identifying which mouse genes are being studied in an article, then associating the appropriate gene symbols with the article reference number in the MGI database.Here, we discuss our search process, performance metrics and success criteria, and how we identified a short list of potential text mining tools for further evaluation. We provide an overview of our pilot projects with NCBO's Open Biomedical Annotator and Fraunhofer SCAI's ProMiner. In doing so, we prove the potential for the further incorporation of semi-automated processes into the curation of the biomedical literature.

Text mining for neuroanatomy using WhiteText with an updated corpus and a new web application

PubMed Central

French, Leon; Liu, Po; Marais, Olivia; Koreman, Tianna; Tseng, Lucia; Lai, Artemis; Pavlidis, Paul

2015-01-01

We describe the WhiteText project, and its progress towards automatically extracting statements of neuroanatomical connectivity from text. We review progress to date on the three main steps of the project: recognition of brain region mentions, standardization of brain region mentions to neuroanatomical nomenclature, and connectivity statement extraction. We further describe a new version of our manually curated corpus that adds 2,111 connectivity statements from 1,828 additional abstracts. Cross-validation classification within the new corpus replicates results on our original corpus, recalling 67% of connectivity statements at 51% precision. The resulting merged corpus provides 5,208 connectivity statements that can be used to seed species-specific connectivity matrices and to better train automated techniques. Finally, we present a new web application that allows fast interactive browsing of the over 70,000 sentences indexed by the system, as a tool for accessing the data and assisting in further curation. Software and data are freely available at http://www.chibi.ubc.ca/WhiteText/. PMID:26052282

Who wrote the "Letter to the Hebrews"?: data mining for detection of text authorship

NASA Astrophysics Data System (ADS)

Sabordo, Madeleine; Chai, Shong Y.; Berryman, Matthew J.; Abbott, Derek

2005-02-01

This paper explores the authorship of the Letter to the Hebrews using a number of different measures of relationship between different texts of the New Testament. The methods used in the study include file zipping and compression techniques, prediction by the partial matching technique and the word recurrence interval technique. The long term motivation is that the techniques employed in this study may find applicability in future generation web search engines, email authorship identification, detection of plagiarism and terrorist email traffic filtration.

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

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.

DrugQuest - a text mining workflow for drug association discovery.

PubMed

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

2016-06-06

Text mining and data integration methods are gaining ground in the field of health sciences due to the exponential growth of bio-medical literature and information stored in biological databases. While such methods mostly try to extract bioentity associations from PubMed, very few of them are dedicated in mining other types of repositories such as chemical databases. Herein, we apply a text mining approach on the DrugBank database in order to explore drug associations based on the DrugBank "Description", "Indication", "Pharmacodynamics" and "Mechanism of Action" text fields. We apply Name Entity Recognition (NER) techniques on these fields to identify chemicals, proteins, genes, pathways, diseases, and we utilize the TextQuest algorithm to find additional biologically significant words. Using a plethora of similarity and partitional clustering techniques, we group the DrugBank records based on their common terms and investigate possible scenarios why these records are clustered together. Different views such as clustered chemicals based on their textual information, tag clouds consisting of Significant Terms along with the terms that were used for clustering are delivered to the user through a user-friendly web interface. DrugQuest is a text mining tool for knowledge discovery: it is designed to cluster DrugBank records based on text attributes in order to find new associations between drugs. The service is freely available at http://bioinformatics.med.uoc.gr/drugquest .

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…

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.

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...

30 CFR 28.10 - Application procedures.

Code of Federal Regulations, 2014 CFR

2014-07-01

... Labor, Mine Safety and Health Administration, Approval and Certification Center, 765 Technology Drive... Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR TESTING, EVALUATION, AND... TRAILING CABLES IN COAL MINES Application for Approval § 28.10 Application procedures. (a) Each applicant...

30 CFR 28.10 - Application procedures.

Code of Federal Regulations, 2012 CFR

2012-07-01

... Labor, Mine Safety and Health Administration, Approval and Certification Center, 765 Technology Drive... Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR TESTING, EVALUATION, AND... TRAILING CABLES IN COAL MINES Application for Approval § 28.10 Application procedures. (a) Each applicant...

30 CFR 28.10 - Application procedures.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Labor, Mine Safety and Health Administration, Approval and Certification Center, 765 Technology Drive... Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR TESTING, EVALUATION, AND... TRAILING CABLES IN COAL MINES Application for Approval § 28.10 Application procedures. (a) Each applicant...

30 CFR 28.10 - Application procedures.

Code of Federal Regulations, 2013 CFR

2013-07-01

... Labor, Mine Safety and Health Administration, Approval and Certification Center, 765 Technology Drive... Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR TESTING, EVALUATION, AND... TRAILING CABLES IN COAL MINES Application for Approval § 28.10 Application procedures. (a) Each applicant...

Application and Exploration of Big Data Mining in Clinical Medicine

PubMed Central

Zhang, Yue; Guo, Shu-Li; Han, Li-Na; Li, Tie-Ling

2016-01-01

Objective: To review theories and technologies of big data mining and their application in clinical medicine. Data Sources: Literatures published in English or Chinese regarding theories and technologies of big data mining and the concrete applications of data mining technology in clinical medicine were obtained from PubMed and Chinese Hospital Knowledge Database from 1975 to 2015. Study Selection: Original articles regarding big data mining theory/technology and big data mining's application in the medical field were selected. Results: This review characterized the basic theories and technologies of big data mining including fuzzy theory, rough set theory, cloud theory, Dempster–Shafer theory, artificial neural network, genetic algorithm, inductive learning theory, Bayesian network, decision tree, pattern recognition, high-performance computing, and statistical analysis. The application of big data mining in clinical medicine was analyzed in the fields of disease risk assessment, clinical decision support, prediction of disease development, guidance of rational use of drugs, medical management, and evidence-based medicine. Conclusion: Big data mining has the potential to play an important role in clinical medicine. PMID:26960378

PolySearch2: a significantly improved text-mining system for discovering associations between human diseases, genes, drugs, metabolites, toxins and more.

PubMed

Liu, Yifeng; Liang, Yongjie; Wishart, David

2015-07-01

PolySearch2 (http://polysearch.ca) is an online text-mining system for identifying relationships between biomedical entities such as human diseases, genes, SNPs, proteins, drugs, metabolites, toxins, metabolic pathways, organs, tissues, subcellular organelles, positive health effects, negative health effects, drug actions, Gene Ontology terms, MeSH terms, ICD-10 medical codes, biological taxonomies and chemical taxonomies. PolySearch2 supports a generalized 'Given X, find all associated Ys' query, where X and Y can be selected from the aforementioned biomedical entities. An example query might be: 'Find all diseases associated with Bisphenol A'. To find its answers, PolySearch2 searches for associations against comprehensive collections of free-text collections, including local versions of MEDLINE abstracts, PubMed Central full-text articles, Wikipedia full-text articles and US Patent application abstracts. PolySearch2 also searches 14 widely used, text-rich biological databases such as UniProt, DrugBank and Human Metabolome Database to improve its accuracy and coverage. PolySearch2 maintains an extensive thesaurus of biological terms and exploits the latest search engine technology to rapidly retrieve relevant articles and databases records. PolySearch2 also generates, ranks and annotates associative candidates and present results with relevancy statistics and highlighted key sentences to facilitate user interpretation. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

PolySearch2: a significantly improved text-mining system for discovering associations between human diseases, genes, drugs, metabolites, toxins and more

PubMed Central

Liu, Yifeng; Liang, Yongjie; Wishart, David

2015-01-01

PolySearch2 (http://polysearch.ca) is an online text-mining system for identifying relationships between biomedical entities such as human diseases, genes, SNPs, proteins, drugs, metabolites, toxins, metabolic pathways, organs, tissues, subcellular organelles, positive health effects, negative health effects, drug actions, Gene Ontology terms, MeSH terms, ICD-10 medical codes, biological taxonomies and chemical taxonomies. PolySearch2 supports a generalized ‘Given X, find all associated Ys’ query, where X and Y can be selected from the aforementioned biomedical entities. An example query might be: ‘Find all diseases associated with Bisphenol A’. To find its answers, PolySearch2 searches for associations against comprehensive collections of free-text collections, including local versions of MEDLINE abstracts, PubMed Central full-text articles, Wikipedia full-text articles and US Patent application abstracts. PolySearch2 also searches 14 widely used, text-rich biological databases such as UniProt, DrugBank and Human Metabolome Database to improve its accuracy and coverage. PolySearch2 maintains an extensive thesaurus of biological terms and exploits the latest search engine technology to rapidly retrieve relevant articles and databases records. PolySearch2 also generates, ranks and annotates associative candidates and present results with relevancy statistics and highlighted key sentences to facilitate user interpretation. PMID:25925572

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.

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.

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…

Knowledge based word-concept model estimation and refinement for biomedical text mining.

PubMed

Jimeno Yepes, Antonio; Berlanga, Rafael

2015-02-01

Text mining of scientific literature has been essential for setting up large public biomedical databases, which are being widely used by the research community. In the biomedical domain, the existence of a large number of terminological resources and knowledge bases (KB) has enabled a myriad of machine learning methods for different text mining related tasks. Unfortunately, KBs have not been devised for text mining tasks but for human interpretation, thus performance of KB-based methods is usually lower when compared to supervised machine learning methods. The disadvantage of supervised methods though is they require labeled training data and therefore not useful for large scale biomedical text mining systems. KB-based methods do not have this limitation. In this paper, we describe a novel method to generate word-concept probabilities from a KB, which can serve as a basis for several text mining tasks. This method not only takes into account the underlying patterns within the descriptions contained in the KB but also those in texts available from large unlabeled corpora such as MEDLINE. The parameters of the model have been estimated without training data. Patterns from MEDLINE have been built using MetaMap for entity recognition and related using co-occurrences. The word-concept probabilities were evaluated on the task of word sense disambiguation (WSD). The results showed that our method obtained a higher degree of accuracy than other state-of-the-art approaches when evaluated on the MSH WSD data set. We also evaluated our method on the task of document ranking using MEDLINE citations. These results also showed an increase in performance over existing baseline retrieval approaches. Copyright © 2014 Elsevier Inc. All rights reserved.

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.…

Cyberspace and Real-World Behavioral Relationships: Towards the Applications of Interest Search Queries to Identify Individuals At-Risk for Suicide

DTIC Science & Technology

2012-06-14

weight fat loss effects diet standard nutrition lose nfpa protein Topic 214: menu restaurant engineering restaurants jones wings seat wild buffalo...Selection ................................................................................... 30 3.5 Raw Data File Format...text mining to descriptions of biological activity and the target of the biological activity (i.e., gene, protein , cell, or microorganism) to predict

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…

Analysis of Nature of Science Included in Recent Popular Writing Using Text Mining Techniques

NASA Astrophysics Data System (ADS)

Jiang, Feng; McComas, William F.

2014-09-01

This study examined the inclusion of nature of science (NOS) in popular science writing to determine whether it could serve supplementary resource for teaching NOS and to evaluate the accuracy of text mining and classification as a viable research tool in science education research. Four groups of documents published from 2001 to 2010 were analyzed: Scientific American, Discover magazine, winners of the Royal Society Winton Prize for Science Books, and books from NSTA's list of Outstanding Science Trade Books. Computer analysis categorized passages in the selected documents based on their inclusions of NOS. Human analysis assessed the frequency, context, coverage, and accuracy of the inclusions of NOS within computer identified NOS passages. NOS was rarely addressed in selected document sets but somewhat more frequently addressed in the letters section of the two magazines. This result suggests that readers seem interested in the discussion of NOS-related themes. In the popular science books analyzed, NOS presentations were found more likely to be aggregated in the beginning and the end of the book, rather than scattered throughout. The most commonly addressed NOS elements in the analyzed documents are science and society and empiricism in science. Only one inaccurate presentation of NOS were identified in all analyzed documents. The text mining technique demonstrated exciting performance, which invites more applications of the technique to analyze other aspects of science textbooks, popular science writing, or other materials involved in science teaching and learning.

An open-source framework for large-scale, flexible evaluation of biomedical text mining systems.

PubMed

Baumgartner, William A; Cohen, K Bretonnel; Hunter, Lawrence

2008-01-29

Improved evaluation methodologies have been identified as a necessary prerequisite to the improvement of text mining theory and practice. This paper presents a publicly available framework that facilitates thorough, structured, and large-scale evaluations of text mining technologies. The extensibility of this framework and its ability to uncover system-wide characteristics by analyzing component parts as well as its usefulness for facilitating third-party application integration are demonstrated through examples in the biomedical domain. Our evaluation framework was assembled using the Unstructured Information Management Architecture. It was used to analyze a set of gene mention identification systems involving 225 combinations of system, evaluation corpus, and correctness measure. Interactions between all three were found to affect the relative rankings of the systems. A second experiment evaluated gene normalization system performance using as input 4,097 combinations of gene mention systems and gene mention system-combining strategies. Gene mention system recall is shown to affect gene normalization system performance much more than does gene mention system precision, and high gene normalization performance is shown to be achievable with remarkably low levels of gene mention system precision. The software presented in this paper demonstrates the potential for novel discovery resulting from the structured evaluation of biomedical language processing systems, as well as the usefulness of such an evaluation framework for promoting collaboration between developers of biomedical language processing technologies. The code base is available as part of the BioNLP UIMA Component Repository on SourceForge.net.

An open-source framework for large-scale, flexible evaluation of biomedical text mining systems

PubMed Central

Baumgartner, William A; Cohen, K Bretonnel; Hunter, Lawrence

2008-01-01

Background Improved evaluation methodologies have been identified as a necessary prerequisite to the improvement of text mining theory and practice. This paper presents a publicly available framework that facilitates thorough, structured, and large-scale evaluations of text mining technologies. The extensibility of this framework and its ability to uncover system-wide characteristics by analyzing component parts as well as its usefulness for facilitating third-party application integration are demonstrated through examples in the biomedical domain. Results Our evaluation framework was assembled using the Unstructured Information Management Architecture. It was used to analyze a set of gene mention identification systems involving 225 combinations of system, evaluation corpus, and correctness measure. Interactions between all three were found to affect the relative rankings of the systems. A second experiment evaluated gene normalization system performance using as input 4,097 combinations of gene mention systems and gene mention system-combining strategies. Gene mention system recall is shown to affect gene normalization system performance much more than does gene mention system precision, and high gene normalization performance is shown to be achievable with remarkably low levels of gene mention system precision. Conclusion The software presented in this paper demonstrates the potential for novel discovery resulting from the structured evaluation of biomedical language processing systems, as well as the usefulness of such an evaluation framework for promoting collaboration between developers of biomedical language processing technologies. The code base is available as part of the BioNLP UIMA Component Repository on SourceForge.net. PMID:18230184

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

26 CFR 1.611-2 - Rules applicable to mines, oil and gas wells, and other natural deposits.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 26 Internal Revenue 7 2013-04-01 2013-04-01 false Rules applicable to mines, oil and gas wells....611-2 Rules applicable to mines, oil and gas wells, and other natural deposits. (a) Computation of cost depletion of mines, oil and gas wells, and other natural deposits. (1) The basis upon which cost...

26 CFR 1.611-2 - Rules applicable to mines, oil and gas wells, and other natural deposits.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 26 Internal Revenue 7 2012-04-01 2012-04-01 false Rules applicable to mines, oil and gas wells....611-2 Rules applicable to mines, oil and gas wells, and other natural deposits. (a) Computation of cost depletion of mines, oil and gas wells, and other natural deposits. (1) The basis upon which cost...

Text Mining Metal-Organic Framework Papers.

PubMed

Park, Sanghoon; Kim, Baekjun; Choi, Sihoon; Boyd, Peter G; Smit, Berend; Kim, Jihan

2018-02-26

We have developed a simple text mining algorithm that allows us to identify surface area and pore volumes of metal-organic frameworks (MOFs) using manuscript html files as inputs. The algorithm searches for common units (e.g., m 2 /g, cm 3 /g) associated with these two quantities to facilitate the search. From the sample set data of over 200 MOFs, the algorithm managed to identify 90% and 88.8% of the correct surface area and pore volume values. Further application to a test set of randomly chosen MOF html files yielded 73.2% and 85.1% accuracies for the two respective quantities. Most of the errors stem from unorthodox sentence structures that made it difficult to identify the correct data as well as bolded notations of MOFs (e.g., 1a) that made it difficult identify its real name. These types of tools will become useful when it comes to discovering structure-property relationships among MOFs as well as collecting a large set of data for references.

Community challenges in biomedical text mining over 10 years: success, failure and the future

PubMed Central

Huang, Chung-Chi

2016-01-01

One effective way to improve the state of the art is through competitions. Following the success of the Critical Assessment of protein Structure Prediction (CASP) in bioinformatics research, a number of challenge evaluations have been organized by the text-mining research community to assess and advance natural language processing (NLP) research for biomedicine. In this article, we review the different community challenge evaluations held from 2002 to 2014 and their respective tasks. Furthermore, we examine these challenge tasks through their targeted problems in NLP research and biomedical applications, respectively. Next, we describe the general workflow of organizing a Biomedical NLP (BioNLP) challenge and involved stakeholders (task organizers, task data producers, task participants and end users). Finally, we summarize the impact and contributions by taking into account different BioNLP challenges as a whole, followed by a discussion of their limitations and difficulties. We conclude with future trends in BioNLP challenge evaluations. PMID:25935162

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.

43 CFR 3862.8 - Patents for mining claims.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 43 Public Lands: Interior 2 2011-10-01 2011-10-01 false Patents for mining claims. 3862.8 Section... MANAGEMENT, DEPARTMENT OF THE INTERIOR MINERALS MANAGEMENT (3000) MINERAL PATENT APPLICATIONS Lode Mining Claim Patent Applications § 3862.8 Patents for mining claims. ...

43 CFR 3862.8 - Patents for mining claims.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 43 Public Lands: Interior 2 2014-10-01 2014-10-01 false Patents for mining claims. 3862.8 Section... MANAGEMENT, DEPARTMENT OF THE INTERIOR MINERALS MANAGEMENT (3000) MINERAL PATENT APPLICATIONS Lode Mining Claim Patent Applications § 3862.8 Patents for mining claims. ...

43 CFR 3862.8 - Patents for mining claims.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 43 Public Lands: Interior 2 2013-10-01 2013-10-01 false Patents for mining claims. 3862.8 Section... MANAGEMENT, DEPARTMENT OF THE INTERIOR MINERALS MANAGEMENT (3000) MINERAL PATENT APPLICATIONS Lode Mining Claim Patent Applications § 3862.8 Patents for mining claims. ...

43 CFR 3862.8 - Patents for mining claims.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 43 Public Lands: Interior 2 2012-10-01 2012-10-01 false Patents for mining claims. 3862.8 Section... MANAGEMENT, DEPARTMENT OF THE INTERIOR MINERALS MANAGEMENT (3000) MINERAL PATENT APPLICATIONS Lode Mining Claim Patent Applications § 3862.8 Patents for mining claims. ...

Implementation of Paste Backfill Mining Technology in Chinese Coal Mines

PubMed Central

Chang, Qingliang; Zhou, Huaqiang; Bai, Jianbiao

2014-01-01

Implementation of clean mining technology at coal mines is crucial to protect the environment and maintain balance among energy resources, consumption, and ecology. After reviewing present coal clean mining technology, we introduce the technology principles and technological process of paste backfill mining in coal mines and discuss the components and features of backfill materials, the constitution of the backfill system, and the backfill process. Specific implementation of this technology and its application are analyzed for paste backfill mining in Daizhuang Coal Mine; a practical implementation shows that paste backfill mining can improve the safety and excavation rate of coal mining, which can effectively resolve surface subsidence problems caused by underground mining activities, by utilizing solid waste such as coal gangues as a resource. Therefore, paste backfill mining is an effective clean coal mining technology, which has widespread application. PMID:25258737

Implementation of paste backfill mining technology in Chinese coal mines.

PubMed

Chang, Qingliang; Chen, Jianhang; Zhou, Huaqiang; Bai, Jianbiao

2014-01-01

Implementation of clean mining technology at coal mines is crucial to protect the environment and maintain balance among energy resources, consumption, and ecology. After reviewing present coal clean mining technology, we introduce the technology principles and technological process of paste backfill mining in coal mines and discuss the components and features of backfill materials, the constitution of the backfill system, and the backfill process. Specific implementation of this technology and its application are analyzed for paste backfill mining in Daizhuang Coal Mine; a practical implementation shows that paste backfill mining can improve the safety and excavation rate of coal mining, which can effectively resolve surface subsidence problems caused by underground mining activities, by utilizing solid waste such as coal gangues as a resource. Therefore, paste backfill mining is an effective clean coal mining technology, which has widespread application.

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 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.

[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.

40 CFR 434.40 - Applicability; description of the alkaline mine drainage subcategory.

Code of Federal Regulations, 2012 CFR

2012-07-01

... alkaline mine drainage subcategory. 434.40 Section 434.40 Protection of Environment ENVIRONMENTAL... BPT, BAT, BCT LIMITATIONS AND NEW SOURCE PERFORMANCE STANDARDS Alkaline Mine Drainage § 434.40 Applicability; description of the alkaline mine drainage subcategory. The provisions of this subpart are...

40 CFR 434.40 - Applicability; description of the alkaline mine drainage subcategory.

Code of Federal Regulations, 2013 CFR

2013-07-01

... alkaline mine drainage subcategory. 434.40 Section 434.40 Protection of Environment ENVIRONMENTAL... BPT, BAT, BCT LIMITATIONS AND NEW SOURCE PERFORMANCE STANDARDS Alkaline Mine Drainage § 434.40 Applicability; description of the alkaline mine drainage subcategory. The provisions of this subpart are...

40 CFR 434.40 - Applicability; description of the alkaline mine drainage subcategory.

Code of Federal Regulations, 2014 CFR

2014-07-01

... alkaline mine drainage subcategory. 434.40 Section 434.40 Protection of Environment ENVIRONMENTAL... BPT, BAT, BCT LIMITATIONS AND NEW SOURCE PERFORMANCE STANDARDS Alkaline Mine Drainage § 434.40 Applicability; description of the alkaline mine drainage subcategory. The provisions of this subpart are...

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.

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.

Linking descriptive geology and quantitative machine learning through an ontology of lithological concepts

NASA Astrophysics Data System (ADS)

Klump, J. F.; Huber, R.; Robertson, J.; Cox, S. J. D.; Woodcock, R.

2014-12-01

Despite the recent explosion of quantitative geological data, geology remains a fundamentally qualitative science. Numerical data only constitute a certain part of data collection in the geosciences. In many cases, geological observations are compiled as text into reports and annotations on drill cores, thin sections or drawings of outcrops. The observations are classified into concepts such as lithology, stratigraphy, geological structure, etc. These descriptions are semantically rich and are generally supported by more quantitative observations using geochemical analyses, XRD, hyperspectral scanning, etc, but the goal is geological semantics. In practice it has been difficult to bring the different observations together due to differing perception or granularity of classification in human observation, or the partial observation of only some characteristics using quantitative sensors. In the past years many geological classification schemas have been transferred into ontologies and vocabularies, formalized using RDF and OWL, and published through SPARQL endpoints. Several lithological ontologies were compiled by stratigraphy.net and published through a SPARQL endpoint. This work is complemented by the development of a Python API to integrate this vocabulary into Python-based text mining applications. The applications for the lithological vocabulary and Python API are automated semantic tagging of geochemical data and descriptions of drill cores, machine learning of geochemical compositions that are diagnostic for lithological classifications, and text mining for lithological concepts in reports and geological literature. This combination of applications can be used to identify anomalies in databases, where composition and lithological classification do not match. It can also be used to identify lithological concepts in the literature and infer quantitative values. The resulting semantic tagging opens new possibilities for linking these diverse sources of data.

Application and research of block caving in Pulang copper mine

NASA Astrophysics Data System (ADS)

Ge, Qifa; Fan, Wenlu; Zhu, Weigen; Chen, Xiaowei

2018-01-01

The application of block caving in mines shows significant advantages in large scale, low cost and high efficiency, thus block caving is worth promoting in the mines that meets the requirement of natural caving. Due to large scale of production and low ore grade in Pulang copper mine in China, comprehensive analysis and research were conducted on rock mechanics, mining sequence, undercutting and stability of bottom structure in terms of raising mine benefit and maximizing the recovery mineral resources. Finally this study summarizes that block caving is completely suitable for Pulang copper mine.

15 CFR 971.501 - Resource assessment, recovery plan, and logical mining unit.

Code of Federal Regulations, 2010 CFR

2010-01-01

..., and logical mining unit. 971.501 Section 971.501 Commerce and Foreign Trade Regulations Relating to... COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR... mining unit. (a) The applicant must submit with the application a resource assessment to provide a basis...

77 FR 12245 - Deep Seabed Mining: Request for Extension of Exploration Licenses

Federal Register 2010, 2011, 2012, 2013, 2014

2012-02-29

... DEPARTMENT OF COMMERCE National Oceanic and Atmospheric Administration Deep Seabed Mining: Request.... Department of Commerce. ACTION: Notice of receipt of application to extend Deep Seabed Mining Exploration... received an application for five-year extensions of Deep Seabed Mining Exploration Licenses USA-1 and USA-4...

30 CFR 71.404 - Application for waiver of surface facilities requirements.

Code of Federal Regulations, 2010 CFR

2010-07-01

... requirements. 71.404 Section 71.404 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR COAL MINE SAFETY AND HEALTH MANDATORY HEALTH STANDARDS-SURFACE COAL MINES AND SURFACE WORK AREAS... Facilities at Surface Coal Mines § 71.404 Application for waiver of surface facilities requirements. (a...

43 CFR 9185.1-3 - Mining claims.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 43 Public Lands: Interior 2 2011-10-01 2011-10-01 false Mining claims. 9185.1-3 Section 9185.1-3 Public Lands: Interior Regulations Relating to Public Lands (Continued) BUREAU OF LAND MANAGEMENT... Mining claims. (a) Application for survey. Application for the survey of a mining claim should be filed...

Mining protein function from text using term-based support vector machines

PubMed Central

Rice, Simon B; Nenadic, Goran; Stapley, Benjamin J

2005-01-01

Background Text mining has spurred huge interest in the domain of biology. The goal of the BioCreAtIvE exercise was to evaluate the performance of current text mining systems. We participated in Task 2, which addressed assigning Gene Ontology terms to human proteins and selecting relevant evidence from full-text documents. We approached it as a modified form of the document classification task. We used a supervised machine-learning approach (based on support vector machines) to assign protein function and select passages that support the assignments. As classification features, we used a protein's co-occurring terms that were automatically extracted from documents. Results The results evaluated by curators were modest, and quite variable for different problems: in many cases we have relatively good assignment of GO terms to proteins, but the selected supporting text was typically non-relevant (precision spanning from 3% to 50%). The method appears to work best when a substantial set of relevant documents is obtained, while it works poorly on single documents and/or short passages. The initial results suggest that our approach can also mine annotations from text even when an explicit statement relating a protein to a GO term is absent. Conclusion A machine learning approach to mining protein function predictions from text can yield good performance only if sufficient training data is available, and significant amount of supporting data is used for prediction. The most promising results are for combined document retrieval and GO term assignment, which calls for the integration of methods developed in BioCreAtIvE Task 1 and Task 2. PMID:15960835

Using text mining for study identification in systematic reviews: a systematic review of current approaches.

PubMed

O'Mara-Eves, Alison; Thomas, James; McNaught, John; Miwa, Makoto; Ananiadou, Sophia

2015-01-14

The large and growing number of published studies, and their increasing rate of publication, makes the task of identifying relevant studies in an unbiased way for inclusion in systematic reviews both complex and time consuming. Text mining has been offered as a potential solution: through automating some of the screening process, reviewer time can be saved. The evidence base around the use of text mining for screening has not yet been pulled together systematically; this systematic review fills that research gap. Focusing mainly on non-technical issues, the review aims to increase awareness of the potential of these technologies and promote further collaborative research between the computer science and systematic review communities. Five research questions led our review: what is the state of the evidence base; how has workload reduction been evaluated; what are the purposes of semi-automation and how effective are they; how have key contextual problems of applying text mining to the systematic review field been addressed; and what challenges to implementation have emerged? We answered these questions using standard systematic review methods: systematic and exhaustive searching, quality-assured data extraction and a narrative synthesis to synthesise findings. The evidence base is active and diverse; there is almost no replication between studies or collaboration between research teams and, whilst it is difficult to establish any overall conclusions about best approaches, it is clear that efficiencies and reductions in workload are potentially achievable. On the whole, most suggested that a saving in workload of between 30% and 70% might be possible, though sometimes the saving in workload is accompanied by the loss of 5% of relevant studies (i.e. a 95% recall). Using text mining to prioritise the order in which items are screened should be considered safe and ready for use in 'live' reviews. The use of text mining as a 'second screener' may also be used cautiously. The use of text mining to eliminate studies automatically should be considered promising, but not yet fully proven. In highly technical/clinical areas, it may be used with a high degree of confidence; but more developmental and evaluative work is needed in other disciplines.

MetReS, an Efficient Database for Genomic Applications.

PubMed

Vilaplana, Jordi; Alves, Rui; Solsona, Francesc; Mateo, Jordi; Teixidó, Ivan; Pifarré, Marc

2018-02-01

MetReS (Metabolic Reconstruction Server) is a genomic database that is shared between two software applications that address important biological problems. Biblio-MetReS is a data-mining tool that enables the reconstruction of molecular networks based on automated text-mining analysis of published scientific literature. Homol-MetReS allows functional (re)annotation of proteomes, to properly identify both the individual proteins involved in the processes of interest and their function. The main goal of this work was to identify the areas where the performance of the MetReS database performance could be improved and to test whether this improvement would scale to larger datasets and more complex types of analysis. The study was started with a relational database, MySQL, which is the current database server used by the applications. We also tested the performance of an alternative data-handling framework, Apache Hadoop. Hadoop is currently used for large-scale data processing. We found that this data handling framework is likely to greatly improve the efficiency of the MetReS applications as the dataset and the processing needs increase by several orders of magnitude, as expected to happen in the near future.

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.

30 CFR 18.93 - Application for field approval; filing procedures.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Application for field approval; filing... TESTING, EVALUATION, AND APPROVAL OF MINING PRODUCTS ELECTRIC MOTOR-DRIVEN MINE EQUIPMENT AND ACCESSORIES Field Approval of Electrically Operated Mining Equipment § 18.93 Application for field approval; filing...

30 CFR 18.93 - Application for field approval; filing procedures.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Application for field approval; filing... TESTING, EVALUATION, AND APPROVAL OF MINING PRODUCTS ELECTRIC MOTOR-DRIVEN MINE EQUIPMENT AND ACCESSORIES Field Approval of Electrically Operated Mining Equipment § 18.93 Application for field approval; filing...

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.

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

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…

Application of geostatistics to coal-resource characterization and mine planning. Final report

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

Kauffman, P.W.; Walton, D.R.; Martuneac, L.

1981-12-01

Geostatistics is a proven method of ore reserve estimation in many non-coal mining areas but little has been published concerning its application to coal resources. This report presents the case for using geostatistics for coal mining applications and describes how a coal mining concern can best utilize geostatistical techniques for coal resource characterization and mine planning. An overview of the theory of geostatistics is also presented. Many of the applications discussed are documented in case studies that are a part of the report. The results of an exhaustive literature search are presented and recommendations are made for needed future researchmore » and demonstration projects.« less

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...

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.

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.

30 CFR 761.17 - Regulatory authority obligations at time of permit application review.

Code of Federal Regulations, 2010 CFR

2010-07-01

... permit application review. 761.17 Section 761.17 Mineral Resources OFFICE OF SURFACE MINING RECLAMATION AND ENFORCEMENT, DEPARTMENT OF THE INTERIOR AREAS UNSUITABLE FOR MINING AREAS DESIGNATED BY ACT OF... of an administratively complete application for a permit for a surface coal mining operation, or an...

Using Text Analytics of AJPE Article Titles to Reveal Trends In Pharmacy Education Over the Past Two Decades.

PubMed

Pedrami, Farnoush; Asenso, Pamela; Devi, Sachin

2016-08-25

Objective. To identify trends in pharmacy education during last two decades using text mining. Methods. Articles published in the American Journal of Pharmaceutical Education (AJPE) in the past two decades were compiled in a database. Custom text analytics software was written using Visual Basic programming language in the Visual Basic for Applications (VBA) editor of Excel 2007. Frequency of words appearing in article titles was calculated using the custom VBA software. Data were analyzed to identify the emerging trends in pharmacy education. Results. Three educational trends emerged: active learning, interprofessional, and cultural competency. Conclusion. The text analytics program successfully identified trends in article topics and may be a useful compass to predict the future course of pharmacy education.

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.

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.

E-Cigarette Social Media Messages: A Text Mining Analysis of Marketing and Consumer Conversations on Twitter.

PubMed

Lazard, Allison J; Saffer, Adam J; Wilcox, Gary B; Chung, Arnold DongWoo; Mackert, Michael S; Bernhardt, Jay M

2016-12-12

As the use of electronic cigarettes (e-cigarettes) rises, social media likely influences public awareness and perception of this emerging tobacco product. This study examined the public conversation on Twitter to determine overarching themes and insights for trending topics from commercial and consumer users. Text mining uncovered key patterns and important topics for e-cigarettes on Twitter. SAS Text Miner 12.1 software (SAS Institute Inc) was used for descriptive text mining to reveal the primary topics from tweets collected from March 24, 2015, to July 3, 2015, using a Python script in conjunction with Twitter's streaming application programming interface. A total of 18 keywords related to e-cigarettes were used and resulted in a total of 872,544 tweets that were sorted into overarching themes through a text topic node for tweets (126,127) and retweets (114,451) that represented more than 1% of the conversation. While some of the final themes were marketing-focused, many topics represented diverse proponent and user conversations that included discussion of policies, personal experiences, and the differentiation of e-cigarettes from traditional tobacco, often by pointing to the lack of evidence for the harm or risks of e-cigarettes or taking the position that e-cigarettes should be promoted as smoking cessation devices. These findings reveal that unique, large-scale public conversations are occurring on Twitter alongside e-cigarette advertising and promotion. Proponents and users are turning to social media to share knowledge, experience, and questions about e-cigarette use. Future research should focus on these unique conversations to understand how they influence attitudes towards and use of e-cigarettes. ©Allison J Lazard, Adam J Saffer, Gary B Wilcox, Arnold DongWoo Chung, Michael S Mackert, Jay M Bernhardt. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 12.12.2016.

Automation and robotics technology for intelligent mining systems

NASA Technical Reports Server (NTRS)

Welsh, Jeffrey H.

1989-01-01

The U.S. Bureau of Mines is approaching the problems of accidents and efficiency in the mining industry through the application of automation and robotics to mining systems. This technology can increase safety by removing workers from hazardous areas of the mines or from performing hazardous tasks. The short-term goal of the Automation and Robotics program is to develop technology that can be implemented in the form of an autonomous mining machine using current continuous mining machine equipment. In the longer term, the goal is to conduct research that will lead to new intelligent mining systems that capitalize on the capabilities of robotics. The Bureau of Mines Automation and Robotics program has been structured to produce the technology required for the short- and long-term goals. The short-term goal of application of automation and robotics to an existing mining machine, resulting in autonomous operation, is expected to be accomplished within five years. Key technology elements required for an autonomous continuous mining machine are well underway and include machine navigation systems, coal-rock interface detectors, machine condition monitoring, and intelligent computer systems. The Bureau of Mines program is described, including status of key technology elements for an autonomous continuous mining machine, the program schedule, and future work. Although the program is directed toward underground mining, much of the technology being developed may have applications for space systems or mining on the Moon or other planets.

Searching for 'Unknown Unknowns'

NASA Technical Reports Server (NTRS)

Parsons, Vickie S.

2005-01-01

The NASA Engineering and Safety Center (NESC) was established to improve safety through engineering excellence within NASA programs and projects. As part of this goal, methods are being investigated to enable the NESC to become proactive in identifying areas that may be precursors to future problems. The goal is to find unknown indicators of future problems, not to duplicate the program-specific trending efforts. The data that is critical for detecting these indicators exist in a plethora of dissimilar non-conformance and other databases (without a common format or taxonomy). In fact, much of the data is unstructured text. However, one common database is not required if the right standards and electronic tools are employed. Electronic data mining is a particularly promising tool for this effort into unsupervised learning of common factors. This work in progress began with a systematic evaluation of available data mining software packages, based on documented decision techniques using weighted criteria. The four packages, which were perceived to have the most promise for NASA applications, are being benchmarked and evaluated by independent contractors. Preliminary recommendations for "best practices" in data mining and trending are provided. Final results and recommendations should be available in the Fall 2005. This critical first step in identifying "unknown unknowns" before they become problems is applicable to any set of engineering or programmatic data.

The Labour Welfare Fund Laws (Amendment) Act, 1987 (No. 15 of 1987), 22 May 1987.

PubMed

1987-01-01

This Act authorizes funds constituted under the Mica Mines Labour Welfare Fund Act, 1946, the Limestone and Dolomite Mines Labour Welfare Fund Act, 1972, the Iron Ore Mines, Manganese Ore Mines and Chrome Mines Labour Welfare Fund Act, 1976, and the Beedi Workers Welfare Fund Act, 1976, to be applied for the provision of family welfare, including family planning education and services. full text

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

Automated Text Data Mining Analysis of Five Decades of Educational Leadership Research Literature: Probabilistic Topic Modeling of "EAQ" Articles From 1965 to 2014

ERIC Educational Resources Information Center

Wang, Yinying; Bowers, Alex J.; Fikis, David J.

2017-01-01

Purpose: The purpose of this study is to describe the underlying topics and the topic evolution in the 50-year history of educational leadership research literature. Method: We used automated text data mining with probabilistic latent topic models to examine the full text of the entire publication history of all 1,539 articles published in…

30 CFR 784.1 - Scope.

Code of Federal Regulations, 2010 CFR

2010-07-01

... RECLAMATION OPERATIONS PERMITS AND COAL EXPLORATION SYSTEMS UNDER REGULATORY PROGRAMS UNDERGROUND MINING PERMIT APPLICATIONS-MINIMUM REQUIREMENTS FOR RECLAMATION AND OPERATION PLAN § 784.1 Scope. This part... mining operations and reclamation plans portions of applications for permits for underground mining...

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.

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

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.

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

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

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.

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.

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.

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.

30 CFR 900.2 - Objectives.

Code of Federal Regulations, 2010 CFR

2010-07-01

... texts of State and Federal cooperative agreements for regulation of mining on Federal lands. The... Resources OFFICE OF SURFACE MINING RECLAMATION AND ENFORCEMENT, DEPARTMENT OF THE INTERIOR PROGRAMS FOR THE CONDUCT OF SURFACE MINING OPERATIONS WITHIN EACH STATE INTRODUCTION § 900.2 Objectives. The objective of...

76 FR 40649 - Indiana Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2011-07-11

... at 312 IAC 25-6-30 Surface mining; explosives; general requirements. The full text of the program... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 914... Mining Reclamation and Enforcement, Interior. ACTION: Proposed rule; public comment period on proposed...

Complementing the Numbers: A Text Mining Analysis of College Course Withdrawals

ERIC Educational Resources Information Center

Michalski, Greg V.

2011-01-01

Excessive college course withdrawals are costly to the student and the institution in terms of time to degree completion, available classroom space, and other resources. Although generally well quantified, detailed analysis of the reasons given by students for course withdrawal is less common. To address this, a text mining analysis was performed…

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.

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 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.

Brain model of text animation as a data mining strategy.

PubMed

Astakhova, Tamara; Astakhov, Vadim

2009-01-01

Imagination is the critical point in developing of realistic intelligence (AI) systems. One way to approach imagination would be simulation of its properties and operations. We developed two models "Brain Network Hierarchy of Languages," and "Semantical Holographic Calculus" and simulation system ScriptWriter that emulate the process of imagination through an automatic animation of English texts. The purpose of this paper is to demonstrate the model and present "ScriptWriter" system http://nvo.sdsc.edu/NVO/JCSG/get_SRB_mime_file2.cgi//home/tamara.sdsc/test/demo.zip?F=/home/tamara.sdsc/test/demo.zip&M=application/x-gtar for simulation of the imagination.

Incorporating domain knowledge in chemical and biomedical named entity recognition with word representations.

PubMed

Munkhdalai, Tsendsuren; Li, Meijing; Batsuren, Khuyagbaatar; Park, Hyeon Ah; Choi, Nak Hyeon; Ryu, Keun Ho

2015-01-01

Chemical and biomedical Named Entity Recognition (NER) is an essential prerequisite task before effective text mining can begin for biochemical-text data. Exploiting unlabeled text data to leverage system performance has been an active and challenging research topic in text mining due to the recent growth in the amount of biomedical literature. We present a semi-supervised learning method that efficiently exploits unlabeled data in order to incorporate domain knowledge into a named entity recognition model and to leverage system performance. The proposed method includes Natural Language Processing (NLP) tasks for text preprocessing, learning word representation features from a large amount of text data for feature extraction, and conditional random fields for token classification. Other than the free text in the domain, the proposed method does not rely on any lexicon nor any dictionary in order to keep the system applicable to other NER tasks in bio-text data. We extended BANNER, a biomedical NER system, with the proposed method. This yields an integrated system that can be applied to chemical and drug NER or biomedical NER. We call our branch of the BANNER system BANNER-CHEMDNER, which is scalable over millions of documents, processing about 530 documents per minute, is configurable via XML, and can be plugged into other systems by using the BANNER Unstructured Information Management Architecture (UIMA) interface. BANNER-CHEMDNER achieved an 85.68% and an 86.47% F-measure on the testing sets of CHEMDNER Chemical Entity Mention (CEM) and Chemical Document Indexing (CDI) subtasks, respectively, and achieved an 87.04% F-measure on the official testing set of the BioCreative II gene mention task, showing remarkable performance in both chemical and biomedical NER. BANNER-CHEMDNER system is available at: https://bitbucket.org/tsendeemts/banner-chemdner.

30 CFR 784.10 - Information collection.

Code of Federal Regulations, 2010 CFR

2010-07-01

... COAL MINING AND RECLAMATION OPERATIONS PERMITS AND COAL EXPLORATION SYSTEMS UNDER REGULATORY PROGRAMS UNDERGROUND MINING PERMIT APPLICATIONS-MINIMUM REQUIREMENTS FOR RECLAMATION AND OPERATION PLAN § 784.10... requires applicants for permits for underground coal mines to prepare and submit an operation and...

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.

Community challenges in biomedical text mining over 10 years: success, failure and the future.

PubMed

Huang, Chung-Chi; Lu, Zhiyong

2016-01-01

One effective way to improve the state of the art is through competitions. Following the success of the Critical Assessment of protein Structure Prediction (CASP) in bioinformatics research, a number of challenge evaluations have been organized by the text-mining research community to assess and advance natural language processing (NLP) research for biomedicine. In this article, we review the different community challenge evaluations held from 2002 to 2014 and their respective tasks. Furthermore, we examine these challenge tasks through their targeted problems in NLP research and biomedical applications, respectively. Next, we describe the general workflow of organizing a Biomedical NLP (BioNLP) challenge and involved stakeholders (task organizers, task data producers, task participants and end users). Finally, we summarize the impact and contributions by taking into account different BioNLP challenges as a whole, followed by a discussion of their limitations and difficulties. We conclude with future trends in BioNLP challenge evaluations. Published by Oxford University Press 2015. This work is written by US Government employees and is in the public domain in the US.

76 FR 12849 - Kentucky Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2011-03-09

... (underground mining). The text of the Kentucky regulations can be found in the administrative record and online... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 917 [KY-252-FOR; OSM-2009-0011] Kentucky Regulatory Program AGENCY: Office of Surface Mining Reclamation...

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.

Natural language processing pipelines to annotate BioC collections with an application to the NCBI disease corpus

PubMed Central

Comeau, Donald C.; Liu, Haibin; Islamaj Doğan, Rezarta; Wilbur, W. John

2014-01-01

BioC is a new format and associated code libraries for sharing text and annotations. We have implemented BioC natural language preprocessing pipelines in two popular programming languages: C++ and Java. The current implementations interface with the well-known MedPost and Stanford natural language processing tool sets. The pipeline functionality includes sentence segmentation, tokenization, part-of-speech tagging, lemmatization and sentence parsing. These pipelines can be easily integrated along with other BioC programs into any BioC compliant text mining systems. As an application, we converted the NCBI disease corpus to BioC format, and the pipelines have successfully run on this corpus to demonstrate their functionality. Code and data can be downloaded from http://bioc.sourceforge.net. Database URL: http://bioc.sourceforge.net PMID:24935050

The structural and content aspects of abstracts versus bodies of full text journal articles are different

PubMed Central

2010-01-01

Background An increase in work on the full text of journal articles and the growth of PubMedCentral have the opportunity to create a major paradigm shift in how biomedical text mining is done. However, until now there has been no comprehensive characterization of how the bodies of full text journal articles differ from the abstracts that until now have been the subject of most biomedical text mining research. Results We examined the structural and linguistic aspects of abstracts and bodies of full text articles, the performance of text mining tools on both, and the distribution of a variety of semantic classes of named entities between them. We found marked structural differences, with longer sentences in the article bodies and much heavier use of parenthesized material in the bodies than in the abstracts. We found content differences with respect to linguistic features. Three out of four of the linguistic features that we examined were statistically significantly differently distributed between the two genres. We also found content differences with respect to the distribution of semantic features. There were significantly different densities per thousand words for three out of four semantic classes, and clear differences in the extent to which they appeared in the two genres. With respect to the performance of text mining tools, we found that a mutation finder performed equally well in both genres, but that a wide variety of gene mention systems performed much worse on article bodies than they did on abstracts. POS tagging was also more accurate in abstracts than in article bodies. Conclusions Aspects of structure and content differ markedly between article abstracts and article bodies. A number of these differences may pose problems as the text mining field moves more into the area of processing full-text articles. However, these differences also present a number of opportunities for the extraction of data types, particularly that found in parenthesized text, that is present in article bodies but not in article abstracts. PMID:20920264

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

Implementation of a Flexible Tool for Automated Literature-Mining and Knowledgebase Development (DevToxMine)

EPA Science Inventory

Deriving novel relationships from the scientific literature is an important adjunct to datamining activities for complex datasets in genomics and high-throughput screening activities. Automated text-mining algorithms can be used to extract relevant content from the literature and...

MimoSA: a system for minimotif annotation

PubMed Central

2010-01-01

Background Minimotifs are short peptide sequences within one protein, which are recognized by other proteins or molecules. While there are now several minimotif databases, they are incomplete. There are reports of many minimotifs in the primary literature, which have yet to be annotated, while entirely novel minimotifs continue to be published on a weekly basis. Our recently proposed function and sequence syntax for minimotifs enables us to build a general tool that will facilitate structured annotation and management of minimotif data from the biomedical literature. Results We have built the MimoSA application for minimotif annotation. The application supports management of the Minimotif Miner database, literature tracking, and annotation of new minimotifs. MimoSA enables the visualization, organization, selection and editing functions of minimotifs and their attributes in the MnM database. For the literature components, Mimosa provides paper status tracking and scoring of papers for annotation through a freely available machine learning approach, which is based on word correlation. The paper scoring algorithm is also available as a separate program, TextMine. Form-driven annotation of minimotif attributes enables entry of new minimotifs into the MnM database. Several supporting features increase the efficiency of annotation. The layered architecture of MimoSA allows for extensibility by separating the functions of paper scoring, minimotif visualization, and database management. MimoSA is readily adaptable to other annotation efforts that manually curate literature into a MySQL database. Conclusions MimoSA is an extensible application that facilitates minimotif annotation and integrates with the Minimotif Miner database. We have built MimoSA as an application that integrates dynamic abstract scoring with a high performance relational model of minimotif syntax. MimoSA's TextMine, an efficient paper-scoring algorithm, can be used to dynamically rank papers with respect to context. PMID:20565705

A Feature Mining Based Approach for the Classification of Text Documents into Disjoint Classes.

ERIC Educational Resources Information Center

Nieto Sanchez, Salvador; Triantaphyllou, Evangelos; Kraft, Donald

2002-01-01

Proposes a new approach for classifying text documents into two disjoint classes. Highlights include a brief overview of document clustering; a data mining approach called the One Clause at a Time (OCAT) algorithm which is based on mathematical logic; vector space model (VSM); and comparing the OCAT to the VSM. (Author/LRW)

Examining Mobile Learning Trends 2003-2008: A Categorical Meta-Trend Analysis Using Text Mining Techniques

ERIC Educational Resources Information Center

Hung, Jui-Long; Zhang, Ke

2012-01-01

This study investigated the longitudinal trends of academic articles in Mobile Learning (ML) using text mining techniques. One hundred and nineteen (119) refereed journal articles and proceedings papers from the SCI/SSCI database were retrieved and analyzed. The taxonomies of ML publications were grouped into twelve clusters (topics) and four…

Trends of E-Learning Research from 2000 to 2008: Use of Text Mining and Bibliometrics

ERIC Educational Resources Information Center

Hung, Jui-long

2012-01-01

This study investigated the longitudinal trends of e-learning research using text mining techniques. Six hundred and eighty-nine (689) refereed journal articles and proceedings were retrieved from the Science Citation Index/Social Science Citation Index database in the period from 2000 to 2008. All e-learning publications were grouped into two…

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

Remote sensing for mined area reclamation: Application inventory

NASA Technical Reports Server (NTRS)

1971-01-01

Applications of aerial remote sensing to coal mined area reclamation are documented, and information concerning available data banks for coal producing areas in the east and midwest is given. A summary of mined area information requirements to which remote sensing methods might contribute is included.

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

Discriminative and informative features for biomolecular text mining with ensemble feature selection.

PubMed

Van Landeghem, Sofie; Abeel, Thomas; Saeys, Yvan; Van de Peer, Yves

2010-09-15

In the field of biomolecular text mining, black box behavior of machine learning systems currently limits understanding of the true nature of the predictions. However, feature selection (FS) is capable of identifying the most relevant features in any supervised learning setting, providing insight into the specific properties of the classification algorithm. This allows us to build more accurate classifiers while at the same time bridging the gap between the black box behavior and the end-user who has to interpret the results. We show that our FS methodology successfully discards a large fraction of machine-generated features, improving classification performance of state-of-the-art text mining algorithms. Furthermore, we illustrate how FS can be applied to gain understanding in the predictions of a framework for biomolecular event extraction from text. We include numerous examples of highly discriminative features that model either biological reality or common linguistic constructs. Finally, we discuss a number of insights from our FS analyses that will provide the opportunity to considerably improve upon current text mining tools. The FS algorithms and classifiers are available in Java-ML (http://java-ml.sf.net). The datasets are publicly available from the BioNLP'09 Shared Task web site (http://www-tsujii.is.s.u-tokyo.ac.jp/GENIA/SharedTask/).

78 FR 40496 - Notice of availability of the Final Environmental Impact Statement for the Proposed Hollister...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-05

... silver mining operation. Most of the infrastructure to support a mining operation was authorized and.... The Proposed Action consists of underground mining, constructing a new production shaft, improving.... Public comments resulted in the addition of clarifying text, but did not significantly change the...

Remote Performance Monitoring of a Thermoplastic Composite Bridge at Camp Mackall, NC

DTIC Science & Technology

2011-11-01

level, flow, creep, and force for slope stability, subsidence, seismicity studies, structural restoration, or site assessment applications. • Mining ...monitors mine ventilation, slope stability, convergence, and equipment performance. • Machinery testing- provides temperature, pressure, RPM, veloci...Contact an Applications Engineer for help in deter- mining the best antenna for your application. • 21831 0 dBd, ’l.t Wave Dipole Whip Antenna

Applications of Geomatics in Surface Mining

NASA Astrophysics Data System (ADS)

Blachowski, Jan; Górniak-Zimroz, Justyna; Milczarek, Wojciech; Pactwa, Katarzyna

2017-12-01

In terms of method of extracting mineral from deposit, mining can be classified into: surface, underground, and borehole mining. Surface mining is a form of mining, in which the soil and the rock covering the mineral deposits are removed. Types of surface mining include mainly strip and open-cast methods, as well as quarrying. Tasks associated with surface mining of minerals include: resource estimation and deposit documentation, mine planning and deposit access, mine plant development, extraction of minerals from deposits, mineral and waste processing, reclamation and reclamation of former mining grounds. At each stage of mining, geodata describing changes occurring in space during the entire life cycle of surface mining project should be taken into consideration, i.e. collected, analysed, processed, examined, distributed. These data result from direct (e.g. geodetic) and indirect (i.e. remote or relative) measurements and observations including airborne and satellite methods, geotechnical, geological and hydrogeological data, and data from other types of sensors, e.g. located on mining equipment and infrastructure, mine plans and maps. Management of such vast sources and sets of geodata, as well as information resulting from processing, integrated analysis and examining such data can be facilitated with geomatic solutions. Geomatics is a discipline of gathering, processing, interpreting, storing and delivering spatially referenced information. Thus, geomatics integrates methods and technologies used for collecting, management, processing, visualizing and distributing spatial data. In other words, its meaning covers practically every method and tool from spatial data acquisition to distribution. In this work examples of application of geomatic solutions in surface mining on representative case studies in various stages of mine operation have been presented. These applications include: prospecting and documenting mineral deposits, assessment of land accessibility for a potential large-scale surface mining project, modelling mineral deposit (granite) management, concept of a system for management of conveyor belt network technical condition, project of a geoinformation system of former mining terrains and objects, and monitoring and control of impact of surface mining on mine surroundings with satellite radar interferometry.

Text-mining as a methodology to assess eating disorder-relevant factors: Comparing mentions of fitness tracking technology across online communities.

PubMed

McCaig, Duncan; Bhatia, Sudeep; Elliott, Mark T; Walasek, Lukasz; Meyer, Caroline

2018-05-07

Text-mining offers a technique to identify and extract information from a large corpus of textual data. As an example, this study presents the application of text-mining to assess and compare interest in fitness tracking technology across eating disorder and health-related online communities. A list of fitness tracking technology terms was developed, and communities (i.e., 'subreddits') on a large online discussion platform (Reddit) were compared regarding the frequency with which these terms occurred. The corpus used in this study comprised all comments posted between May 2015 and January 2018 (inclusive) on six subreddits-three eating disorder-related, and three relating to either fitness, weight-management, or nutrition. All comments relating to the same 'thread' (i.e., conversation) were concatenated, and formed the cases used in this study (N = 377,276). Within the eating disorder-related subreddits, the findings indicated that a 'pro-eating disorder' subreddit, which is less recovery focused than the other eating disorder subreddits, had the highest frequency of fitness tracker terms. Across all subreddits, the weight-management subreddit had the highest frequency of the fitness tracker terms' occurrence, and MyFitnessPal was the most frequently mentioned fitness tracker. The technique exemplified here can potentially be used to assess group differences to identify at-risk populations, generate and explore clinically relevant research questions in populations who are difficult to recruit, and scope an area for which there is little extant literature. The technique also facilitates methodological triangulation of research findings obtained through more 'traditional' techniques, such as surveys or interviews. © 2018 Wiley Periodicals, Inc.

EXACT2: the semantics of biomedical protocols

PubMed Central

2014-01-01

Background The reliability and reproducibility of experimental procedures is a cornerstone of scientific practice. There is a pressing technological need for the better representation of biomedical protocols to enable other agents (human or machine) to better reproduce results. A framework that ensures that all information required for the replication of experimental protocols is essential to achieve reproducibility. Methods We have developed the ontology EXACT2 (EXperimental ACTions) that is designed to capture the full semantics of biomedical protocols required for their reproducibility. To construct EXACT2 we manually inspected hundreds of published and commercial biomedical protocols from several areas of biomedicine. After establishing a clear pattern for extracting the required information we utilized text-mining tools to translate the protocols into a machine amenable format. We have verified the utility of EXACT2 through the successful processing of previously 'unseen' (not used for the construction of EXACT2) protocols. Results The paper reports on a fundamentally new version EXACT2 that supports the semantically-defined representation of biomedical protocols. The ability of EXACT2 to capture the semantics of biomedical procedures was verified through a text mining use case. In this EXACT2 is used as a reference model for text mining tools to identify terms pertinent to experimental actions, and their properties, in biomedical protocols expressed in natural language. An EXACT2-based framework for the translation of biomedical protocols to a machine amenable format is proposed. Conclusions The EXACT2 ontology is sufficient to record, in a machine processable form, the essential information about biomedical protocols. EXACT2 defines explicit semantics of experimental actions, and can be used by various computer applications. It can serve as a reference model for for the translation of biomedical protocols in natural language into a semantically-defined format. PMID:25472549

30 CFR 7.83 - Application requirements.

Code of Federal Regulations, 2010 CFR

2010-07-01

...) Injector nozzle; (9) Injection fuel pump; (10) Governor; (11) Turbocharger, if applicable; (12) Aftercooler... APPROVAL OF MINING PRODUCTS TESTING BY APPLICANT OR THIRD PARTY Diesel Engines Intended for Use in Underground Coal Mines § 7.83 Application requirements. (a) An application for approval of a diesel engine...

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.

78 FR 64933 - Texas Eastern Transmission, LP; Notice of Application

Federal Register 2010, 2011, 2012, 2013, 2014

2013-10-30

... and necessity to construct its Emerald Longwall Mine Panel D1 Project. Texas Eastern states in its application that, due to anticipated longwall mining activities of Emerald Coal Resources, LP (Emerald) in Greene County, Pennsylvania in Panel D1 of Emerald's mine, ground subsidence may occur. In order to...

40 CFR 440.140 - Applicability; description of the gold placer mine subcategory.

Code of Federal Regulations, 2011 CFR

2011-07-01

... AGENCY (CONTINUED) EFFLUENT GUIDELINES AND STANDARDS ORE MINING AND DRESSING POINT SOURCE CATEGORY Gold... gold bearing ores from placer deposits; and (2) The beneficiation processes which use gravity... applicable to any mines or beneficiation processes which process less than 1500 cubic yards (cu yd) of ore...

76 FR 69764 - Petitions for Modification of Application of Existing Mandatory Safety Standards

Federal Register 2010, 2011, 2012, 2013, 2014

2011-11-09

... DEPARTMENT OF LABOR Mine Safety and Health Administration Petitions for Modification of Application of Existing Mandatory Safety Standards AGENCY: Mine Safety and Health Administration, Labor. ACTION: Notice. SUMMARY: Section 101(c) of the Federal Mine Safety and Health Act of 1977 and 30 CFR part...

Analysis of Nature of Science Included in Recent Popular Writing Using Text Mining Techniques

ERIC Educational Resources Information Center

Jiang, Feng; McComas, William F.

2014-01-01

This study examined the inclusion of nature of science (NOS) in popular science writing to determine whether it could serve supplementary resource for teaching NOS and to evaluate the accuracy of text mining and classification as a viable research tool in science education research. Four groups of documents published from 2001 to 2010 were…

The Determination of Children's Knowledge of Global Lunar Patterns from Online Essays Using Text Mining Analysis

ERIC Educational Resources Information Center

Cheon, Jongpil; Lee, Sangno; Smith, Walter; Song, Jaeki; Kim, Yongjin

2013-01-01

The purpose of this study was to use text mining analysis of early adolescents' online essays to determine their knowledge of global lunar patterns. Australian and American students in grades five to seven wrote about global lunar patterns they had discovered by sharing observations with each other via the Internet. These essays were analyzed for…

Impact of Text-Mining and Imitating Strategies on Lexical Richness, Lexical Diversity and General Success in Second Language Writing

ERIC Educational Resources Information Center

Çepni, Sevcan Bayraktar; Demirel, Elif Tokdemir

2016-01-01

This study aimed to find out the impact of "text mining and imitating" strategies on lexical richness, lexical diversity and general success of students in their compositions in second language writing. The participants were 98 students studying their first year in Karadeniz Technical University in English Language and Literature…

Science and Technology Text Mining: Text Mining of the Journal Cortex

DTIC Science & Technology

2004-01-01

Amnesia Retrograde Amnesia GENERAL Semantic Memory Episodic Memory Working Memory TEST Serial Position Curve...in Cortex can be reasonably divided into four categories (papers in each category in parenthesis): Semantic Memory (151); Handedness (145); Amnesia ... Semantic Memory (151) is divided into Verbal/ Numerical (76) and Visual/ Spatial (75). Amnesia (119) is divided into Amnesia Symptoms (50) and

Practice-based evidence: profiling the safety of cilostazol by text-mining of clinical notes.

PubMed

Leeper, Nicholas J; Bauer-Mehren, Anna; Iyer, Srinivasan V; Lependu, Paea; Olson, Cliff; Shah, Nigam H

2013-01-01

Peripheral arterial disease (PAD) is a growing problem with few available therapies. Cilostazol is the only FDA-approved medication with a class I indication for intermittent claudication, but carries a black box warning due to concerns for increased cardiovascular mortality. To assess the validity of this black box warning, we employed a novel text-analytics pipeline to quantify the adverse events associated with Cilostazol use in a clinical setting, including patients with congestive heart failure (CHF). We analyzed the electronic medical records of 1.8 million subjects from the Stanford clinical data warehouse spanning 18 years using a novel text-mining/statistical analytics pipeline. We identified 232 PAD patients taking Cilostazol and created a control group of 1,160 PAD patients not taking this drug using 1:5 propensity-score matching. Over a mean follow up of 4.2 years, we observed no association between Cilostazol use and any major adverse cardiovascular event including stroke (OR = 1.13, CI [0.82, 1.55]), myocardial infarction (OR = 1.00, CI [0.71, 1.39]), or death (OR = 0.86, CI [0.63, 1.18]). Cilostazol was not associated with an increase in any arrhythmic complication. We also identified a subset of CHF patients who were prescribed Cilostazol despite its black box warning, and found that it did not increase mortality in this high-risk group of patients. This proof of principle study shows the potential of text-analytics to mine clinical data warehouses to uncover 'natural experiments' such as the use of Cilostazol in CHF patients. We envision this method will have broad applications for examining difficult to test clinical hypotheses and to aid in post-marketing drug safety surveillance. Moreover, our observations argue for a prospective study to examine the validity of a drug safety warning that may be unnecessarily limiting the use of an efficacious therapy.

Practice-Based Evidence: Profiling the Safety of Cilostazol by Text-Mining of Clinical Notes

PubMed Central

Iyer, Srinivasan V.; LePendu, Paea; Olson, Cliff; Shah, Nigam H.

2013-01-01

Background Peripheral arterial disease (PAD) is a growing problem with few available therapies. Cilostazol is the only FDA-approved medication with a class I indication for intermittent claudication, but carries a black box warning due to concerns for increased cardiovascular mortality. To assess the validity of this black box warning, we employed a novel text-analytics pipeline to quantify the adverse events associated with Cilostazol use in a clinical setting, including patients with congestive heart failure (CHF). Methods and Results We analyzed the electronic medical records of 1.8 million subjects from the Stanford clinical data warehouse spanning 18 years using a novel text-mining/statistical analytics pipeline. We identified 232 PAD patients taking Cilostazol and created a control group of 1,160 PAD patients not taking this drug using 1∶5 propensity-score matching. Over a mean follow up of 4.2 years, we observed no association between Cilostazol use and any major adverse cardiovascular event including stroke (OR = 1.13, CI [0.82, 1.55]), myocardial infarction (OR = 1.00, CI [0.71, 1.39]), or death (OR = 0.86, CI [0.63, 1.18]). Cilostazol was not associated with an increase in any arrhythmic complication. We also identified a subset of CHF patients who were prescribed Cilostazol despite its black box warning, and found that it did not increase mortality in this high-risk group of patients. Conclusions This proof of principle study shows the potential of text-analytics to mine clinical data warehouses to uncover ‘natural experiments’ such as the use of Cilostazol in CHF patients. We envision this method will have broad applications for examining difficult to test clinical hypotheses and to aid in post-marketing drug safety surveillance. Moreover, our observations argue for a prospective study to examine the validity of a drug safety warning that may be unnecessarily limiting the use of an efficacious therapy. PMID:23717437

Distributed data mining on grids: services, tools, and applications.

PubMed

Cannataro, Mario; Congiusta, Antonio; Pugliese, Andrea; Talia, Domenico; Trunfio, Paolo

2004-12-01

Data mining algorithms are widely used today for the analysis of large corporate and scientific datasets stored in databases and data archives. Industry, science, and commerce fields often need to analyze very large datasets maintained over geographically distributed sites by using the computational power of distributed and parallel systems. The grid can play a significant role in providing an effective computational support for distributed knowledge discovery applications. For the development of data mining applications on grids we designed a system called Knowledge Grid. This paper describes the Knowledge Grid framework and presents the toolset provided by the Knowledge Grid for implementing distributed knowledge discovery. The paper discusses how to design and implement data mining applications by using the Knowledge Grid tools starting from searching grid resources, composing software and data components, and executing the resulting data mining process on a grid. Some performance results are also discussed.

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.

Agile Text Mining for the 2014 i2b2/UTHealth Cardiac Risk Factors Challenge

PubMed Central

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

2016-01-01

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. PMID:26209007

An Overview of GIS-Based Modeling and Assessment of Mining-Induced Hazards: Soil, Water, and Forest

PubMed Central

Kim, Sung-Min; Yi, Huiuk; Choi, Yosoon

2017-01-01

In this study, current geographic information system (GIS)-based methods and their application for the modeling and assessment of mining-induced hazards were reviewed. Various types of mining-induced hazard, including soil contamination, soil erosion, water pollution, and deforestation were considered in the discussion of the strength and role of GIS as a viable problem-solving tool in relation to mining-induced hazards. The various types of mining-induced hazard were classified into two or three subtopics according to the steps involved in the reclamation procedure, or elements of the hazard of interest. Because GIS is appropriated for the handling of geospatial data in relation to mining-induced hazards, the application and feasibility of exploiting GIS-based modeling and assessment of mining-induced hazards within the mining industry could be expanded further. PMID:29186922

An Overview of GIS-Based Modeling and Assessment of Mining-Induced Hazards: Soil, Water, and Forest.

PubMed

Suh, Jangwon; Kim, Sung-Min; Yi, Huiuk; Choi, Yosoon

2017-11-27

In this study, current geographic information system (GIS)-based methods and their application for the modeling and assessment of mining-induced hazards were reviewed. Various types of mining-induced hazard, including soil contamination, soil erosion, water pollution, and deforestation were considered in the discussion of the strength and role of GIS as a viable problem-solving tool in relation to mining-induced hazards. The various types of mining-induced hazard were classified into two or three subtopics according to the steps involved in the reclamation procedure, or elements of the hazard of interest. Because GIS is appropriated for the handling of geospatial data in relation to mining-induced hazards, the application and feasibility of exploiting GIS-based modeling and assessment of mining-induced hazards within the mining industry could be expanded further.

78 FR 64397 - Mississippi Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2013-10-29

... text of the program amendment available at www.regulations.gov . A. Mississippi Surface Coal Mining... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 924...; S2D2SSS08011000SX066A00033F13XS501520] Mississippi Regulatory Program AGENCY: Office of Surface Mining Reclamation and Enforcement...

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…

Natural language processing pipelines to annotate BioC collections with an application to the NCBI disease corpus.

PubMed

Comeau, Donald C; Liu, Haibin; Islamaj Doğan, Rezarta; Wilbur, W John

2014-01-01

BioC is a new format and associated code libraries for sharing text and annotations. We have implemented BioC natural language preprocessing pipelines in two popular programming languages: C++ and Java. The current implementations interface with the well-known MedPost and Stanford natural language processing tool sets. The pipeline functionality includes sentence segmentation, tokenization, part-of-speech tagging, lemmatization and sentence parsing. These pipelines can be easily integrated along with other BioC programs into any BioC compliant text mining systems. As an application, we converted the NCBI disease corpus to BioC format, and the pipelines have successfully run on this corpus to demonstrate their functionality. Code and data can be downloaded from http://bioc.sourceforge.net. Database URL: http://bioc.sourceforge.net. © The Author(s) 2014. Published by Oxford University Press.

Data mining applications in the context of casemix.

PubMed

Koh, H C; Leong, S K

2001-07-01

In October 1999, the Singapore Government introduced casemix-based funding to public hospitals. The casemix approach to health care funding is expected to yield significant benefits, including equity and rationality in financing health care, the use of comparative casemix data for quality improvement activities, and the provision of information that enables hospitals to understand their cost behaviour and reinforces the drive for more cost-efficient services. However, there is some concern about the "quicker and sicker" syndrome (that is, the rapid discharge of patients with little regard for the quality of outcome). As it is likely that consequences of premature discharges will be reflected in the readmission data, an analysis of possible systematic patterns in readmission data can provide useful insight into the "quicker and sicker" syndrome. This paper explores potential data mining applications in the context of casemix by using readmission data as an illustration. In particular, it illustrates how data mining can be used to better understand readmission data and to detect systematic patterns, if any. From a technical perspective, data mining (which is capable of analysing complex non-linear and interaction relationships) supplements and complements traditional statistical methods in data analysis. From an applications perspective, data mining provides the technology and methodology to analyse mass volume of data to detect hidden patterns in data. Using readmission data as an illustrative data mining application, this paper explores potential data mining applications in the general casemix context.

Use of the method for addressing the challenges of resources procurement management at a mining enterprise

NASA Astrophysics Data System (ADS)

Petrova, T. V.; Strekalov, S. V.; Novichikhin, A. V.

2017-09-01

The article is devoted to the analysis of possible application of the total cost of ownership method for the purchase of resources at a mining enterprise. The description of the total cost of ownership method and experience of using this method in other spheres is provided. The article identifies the essential components needed to calculate the total cost of ownership of a resource. Particular attention is paid to the ratio of the price of the purchased resource and the total cost of ownership. To justify the relevance of application of this method at a mining enterprise for resources purchase, the technical and economic conditions of mining enterprises have been analyzed, which are quite specific and force to introduce certain adjustments to the application of the considered method and opens up new possibilities for its use. Specific spheres for application of this method at a mining enterprise are determined. The main result of the study is the proposed practical recommendations for the introduction and extension of the practice of using the method when a mining enterprise purchases resources.

Lexical Link Analysis Application: Improving Web Service to Acquisition Visibility Portal Phase II

DTIC Science & Technology

2014-04-30

bäÉîÉåíÜ=^ååì~ä=^Åèìáëáíáçå= oÉëÉ~êÅÜ=póãéçëáìã= qÜìêëÇ~ó=pÉëëáçåë= sçäìãÉ=ff= = Lexical Link Analysis Application: Improving Web Service to...DATE 30 APR 2014 2. REPORT TYPE 3. DATES COVERED 00-00-2014 to 00-00-2014 4. TITLE AND SUBTITLE Lexical Link Analysis Application: Improving...vocabulary or lexicon, to describe the attributes and surrounding environment of the system. Lexical Link Analysis (LLA) is a form of text mining in which

Application of 3D Laser Scanning Technology in Inspection and Dynamic Reserves Detection of Open-Pit Mine

NASA Astrophysics Data System (ADS)

Hu, Zhumin; Wei, Shiyu; Jiang, Jun

2017-10-01

The traditional open-pit mine mining rights verification and dynamic reserve detection means rely on the total station and RTK to collect the results of the turning point coordinates of mining surface contours. It resulted in obtaining the results of low precision and large error in the means that is limited by the traditional measurement equipment accuracy and measurement methods. The three-dimensional scanning technology can obtain the three-dimensional coordinate data of the surface of the measured object in a large area at high resolution. This paper expounds the commonly used application of 3D scanning technology in the inspection and dynamic reserve detection of open mine mining rights.

30 CFR 75.301 - Definitions.

Code of Federal Regulations, 2013 CFR

2013-07-01

... Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR COAL MINE SAFETY AND HEALTH MANDATORY SAFETY STANDARDS-UNDERGROUND COAL MINES Ventilation § 75.301 Definitions. In addition to the applicable... operator. The person(s), designated by the mine operator, who is located on the surface of the mine and...

40 CFR 440.54 - New source performance standards (NSPS).

Code of Federal Regulations, 2014 CFR

2014-07-01

... reduction attainable by the applications of the best available demonstrated technology (BADT): (a) The concentration of pollutants discharged in mine drainage from mines obtaining titanium ores from lode deposits...) The concentration of pollutants discharged in mine drainage from mines engaged in the dredge mining of...

30 CFR 75.301 - Definitions.

Code of Federal Regulations, 2014 CFR

2014-07-01

... Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR COAL MINE SAFETY AND HEALTH MANDATORY SAFETY STANDARDS-UNDERGROUND COAL MINES Ventilation § 75.301 Definitions. In addition to the applicable... operator. The person(s), designated by the mine operator, who is located on the surface of the mine and...

40 CFR 440.54 - New source performance standards (NSPS).

Code of Federal Regulations, 2012 CFR

2012-07-01

... reduction attainable by the applications of the best available demonstrated technology (BADT): (a) The concentration of pollutants discharged in mine drainage from mines obtaining titanium ores from lode deposits...) The concentration of pollutants discharged in mine drainage from mines engaged in the dredge mining of...

40 CFR 440.54 - New source performance standards (NSPS).

Code of Federal Regulations, 2013 CFR

2013-07-01

... reduction attainable by the applications of the best available demonstrated technology (BADT): (a) The concentration of pollutants discharged in mine drainage from mines obtaining titanium ores from lode deposits...) The concentration of pollutants discharged in mine drainage from mines engaged in the dredge mining of...

30 CFR 75.301 - Definitions.

Code of Federal Regulations, 2012 CFR

2012-07-01

... Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR COAL MINE SAFETY AND HEALTH MANDATORY SAFETY STANDARDS-UNDERGROUND COAL MINES Ventilation § 75.301 Definitions. In addition to the applicable... operator. The person(s), designated by the mine operator, who is located on the surface of the mine and...

Study of application of ERTS-A imagery to fracture-related mine safety hazards in the coal mining industry. [Indiana

NASA Technical Reports Server (NTRS)

Wier, C. E.; Wobber, F. J. (Principal Investigator); Russell, O. R.; Amato, R. V.; Leshendok, T.

1973-01-01

The author has identified the following significant results. The Mined Land Inventory map of Pike, Gibson, and Warrick Counties, Indiana, prepared from ERTS-1 imagery, was included in the 1973 Annual Report of the President's Council on Environmental Quality as an example of ERTS applications to mined lands. Increasing numbers of inquiries have been received from coal producing states and coal companies interested in the Indiana Program.

40 CFR 440.54 - New source performance standards (NSPS).

Code of Federal Regulations, 2011 CFR

2011-07-01

... attainable by the applications of the best available demonstrated technology (BADT): (a) The concentration of pollutants discharged in mine drainage from mines obtaining titanium ores from lode deposits shall not exceed... pollutants discharged in mine drainage from mines engaged in the dredge mining of placer deposits of sands...

40 CFR 440.54 - New source performance standards (NSPS).

Code of Federal Regulations, 2010 CFR

2010-07-01

... attainable by the applications of the best available demonstrated technology (BADT): (a) The concentration of pollutants discharged in mine drainage from mines obtaining titanium ores from lode deposits shall not exceed... pollutants discharged in mine drainage from mines engaged in the dredge mining of placer deposits of sands...

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

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…

Data Mining: A Hybrid Methodology for Complex and Dynamic Research

ERIC Educational Resources Information Center

Lang, Susan; Baehr, Craig

2012-01-01

This article provides an overview of the ways in which data and text mining have potential as research methodologies in composition studies. It introduces data mining in the context of the field of composition studies and discusses ways in which this methodology can complement and extend our existing research practices by blending the best of what…

Educational Data Mining Applications and Tasks: A Survey of the Last 10 Years

ERIC Educational Resources Information Center

Bakhshinategh, Behdad; Zaiane, Osmar R.; ElAtia, Samira; Ipperciel, Donald

2018-01-01

Educational Data Mining (EDM) is the field of using data mining techniques in educational environments. There exist various methods and applications in EDM which can follow both applied research objectives such as improving and enhancing learning quality, as well as pure research objectives, which tend to improve our understanding of the learning…

Use of an automatic resistivity system for detecting abandoned mine workings

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

Peters, W.R.; Burdick, R.G.

1983-01-01

A high-resolution earth resistivity system has been designed and constructed for use as a means of detecting abandoned coal mine workings. The automatic pole-dipole earth resistivity technique has already been applied to the detection of subsurface voids for military applications. The hardware and software of the system are described, together with applications for surveying and mapping abandoned coal mine workings. Field tests are presented to illustrate the detection of both air-filled and water-filled mine workings.

Text mining and medicine: usefulness in respiratory diseases.

PubMed

Piedra, David; Ferrer, Antoni; Gea, Joaquim

2014-03-01

It is increasingly common to have medical information in electronic format. This includes scientific articles as well as clinical management reviews, and even records from health institutions with patient data. However, traditional instruments, both individual and institutional, are of little use for selecting the most appropriate information in each case, either in the clinical or research field. So-called text or data «mining» enables this huge amount of information to be managed, extracting it from various sources using processing systems (filtration and curation), integrating it and permitting the generation of new knowledge. This review aims to provide an overview of text and data mining, and of the potential usefulness of this bioinformatic technique in the exercise of care in respiratory medicine and in research in the same field. Copyright © 2013 SEPAR. Published by Elsevier Espana. All rights reserved.

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.

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.

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

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.

Integration of Artificial Market Simulation and Text Mining for Market Analysis

NASA Astrophysics Data System (ADS)

Izumi, Kiyoshi; Matsui, Hiroki; Matsuo, Yutaka

We constructed an evaluation system of the self-impact in a financial market using an artificial market and text-mining technology. Economic trends were first extracted from text data circulating in the real world. Then, the trends were inputted into the market simulation. Our simulation revealed that an operation by intervention could reduce over 70% of rate fluctuation in 1995. By the simulation results, the system was able to help for its user to find the exchange policy which can stabilize the yen-dollar rate.

BioC implementations in Go, Perl, Python and Ruby

PubMed Central

Liu, Wanli; Islamaj Doğan, Rezarta; Kwon, Dongseop; Marques, Hernani; Rinaldi, Fabio; Wilbur, W. John; Comeau, Donald C.

2014-01-01

As part of a communitywide effort for evaluating text mining and information extraction systems applied to the biomedical domain, BioC is focused on the goal of interoperability, currently a major barrier to wide-scale adoption of text mining tools. BioC is a simple XML format, specified by DTD, for exchanging data for biomedical natural language processing. With initial implementations in C++ and Java, BioC provides libraries of code for reading and writing BioC text documents and annotations. We extend BioC to Perl, Python, Go and Ruby. We used SWIG to extend the C++ implementation for Perl and one Python implementation. A second Python implementation and the Ruby implementation use native data structures and libraries. BioC is also implemented in the Google language Go. BioC modules are functional in all of these languages, which can facilitate text mining tasks. BioC implementations are freely available through the BioC site: http://bioc.sourceforge.net. Database URL: http://bioc.sourceforge.net/ PMID:24961236

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

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.

Supporting the education evidence portal via text mining

PubMed Central

Ananiadou, Sophia; Thompson, Paul; Thomas, James; Mu, Tingting; Oliver, Sandy; Rickinson, Mark; Sasaki, Yutaka; Weissenbacher, Davy; McNaught, John

2010-01-01

The UK Education Evidence Portal (eep) provides a single, searchable, point of access to the contents of the websites of 33 organizations relating to education, with the aim of revolutionizing work practices for the education community. Use of the portal alleviates the need to spend time searching multiple resources to find relevant information. However, the combined content of the websites of interest is still very large (over 500 000 documents and growing). This means that searches using the portal can produce very large numbers of hits. As users often have limited time, they would benefit from enhanced methods of performing searches and viewing results, allowing them to drill down to information of interest more efficiently, without having to sift through potentially long lists of irrelevant documents. The Joint Information Systems Committee (JISC)-funded ASSIST project has produced a prototype web interface to demonstrate the applicability of integrating a number of text-mining tools and methods into the eep, to facilitate an enhanced searching, browsing and document-viewing experience. New features include automatic classification of documents according to a taxonomy, automatic clustering of search results according to similar document content, and automatic identification and highlighting of key terms within documents. PMID:20643679

miRTex: A Text Mining System for miRNA-Gene Relation Extraction

PubMed Central

Li, Gang; Ross, Karen E.; Arighi, Cecilia N.; Peng, Yifan; Wu, Cathy H.; Vijay-Shanker, K.

2015-01-01

MicroRNAs (miRNAs) regulate a wide range of cellular and developmental processes through gene expression suppression or mRNA degradation. Experimentally validated miRNA gene targets are often reported in the literature. In this paper, we describe miRTex, a text mining system that extracts miRNA-target relations, as well as miRNA-gene and gene-miRNA regulation relations. The system achieves good precision and recall when evaluated on a literature corpus of 150 abstracts with F-scores close to 0.90 on the three different types of relations. We conducted full-scale text mining using miRTex to process all the Medline abstracts and all the full-length articles in the PubMed Central Open Access Subset. The results for all the Medline abstracts are stored in a database for interactive query and file download via the website at http://proteininformationresource.org/mirtex. Using miRTex, we identified genes potentially regulated by miRNAs in Triple Negative Breast Cancer, as well as miRNA-gene relations that, in conjunction with kinase-substrate relations, regulate the response to abiotic stress in Arabidopsis thaliana. These two use cases demonstrate the usefulness of miRTex text mining in the analysis of miRNA-regulated biological processes. PMID:26407127

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

75 FR 82074 - Fee Adjustment for Testing, Evaluation, and Approval of Mining Products

Federal Register 2010, 2011, 2012, 2013, 2014

2010-12-29

..., and Approval of Mining Products AGENCY: Mine Safety and Health Administration (MSHA), Labor. ACTION..., evaluating, and approving mining products as provided by 30 CFR part 5. MSHA charges applicants a fee to... materials manufactured for use in the mining industry. The new fee schedule, effective January 1, 2011, is...

A systematic review of data mining and machine learning for air pollution epidemiology.

PubMed

Bellinger, Colin; Mohomed Jabbar, Mohomed Shazan; Zaïane, Osmar; Osornio-Vargas, Alvaro

2017-11-28

Data measuring airborne pollutants, public health and environmental factors are increasingly being stored and merged. These big datasets offer great potential, but also challenge traditional epidemiological methods. This has motivated the exploration of alternative methods to make predictions, find patterns and extract information. To this end, data mining and machine learning algorithms are increasingly being applied to air pollution epidemiology. We conducted a systematic literature review on the application of data mining and machine learning methods in air pollution epidemiology. We carried out our search process in PubMed, the MEDLINE database and Google Scholar. Research articles applying data mining and machine learning methods to air pollution epidemiology were queried and reviewed. Our search queries resulted in 400 research articles. Our fine-grained analysis employed our inclusion/exclusion criteria to reduce the results to 47 articles, which we separate into three primary areas of interest: 1) source apportionment; 2) forecasting/prediction of air pollution/quality or exposure; and 3) generating hypotheses. Early applications had a preference for artificial neural networks. In more recent work, decision trees, support vector machines, k-means clustering and the APRIORI algorithm have been widely applied. Our survey shows that the majority of the research has been conducted in Europe, China and the USA, and that data mining is becoming an increasingly common tool in environmental health. For potential new directions, we have identified that deep learning and geo-spacial pattern mining are two burgeoning areas of data mining that have good potential for future applications in air pollution epidemiology. We carried out a systematic review identifying the current trends, challenges and new directions to explore in the application of data mining methods to air pollution epidemiology. This work shows that data mining is increasingly being applied in air pollution epidemiology. The potential to support air pollution epidemiology continues to grow with advancements in data mining related to temporal and geo-spacial mining, and deep learning. This is further supported by new sensors and storage mediums that enable larger, better quality data. This suggests that many more fruitful applications can be expected in the future.

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

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.

[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.

Information system for preserving culture heritage in areas affected by heavy industry and mining

NASA Astrophysics Data System (ADS)

Pacina, Jan; Kopecký, Jiří; Bedrníková, Lenka; Handrychová, Barbora; Švarcová, Martina; Holá, Markéta; Pončíková, Edita

2014-05-01

The natural development of the Ústí region (North-West Bohemia, the Czech Republic) has been affected by the human activity during the past hundred years. The heavy industrialization and the brown coal mining have completely changed the land-use in the region. The open-pit coal mines are completely destroying the surrounding landscape, including settlement, communications, hydrological network and the over-all natural development of the region. The other factor affecting the natural development of the landscape, land-use and settlement was the political situation in 1945 (end of the 2nd World War) when the borderland was depopulated. All these factors caused vanishing of more than two hundreds of colonies, villages and towns during this period of time. The task of this project is to prepare and offer for public use a comprehensive information system preserving the cultural heritage in the form of processed old maps, aerial imagery, land-use and georelief reconstructions, local studies, text and photo documents covering the extinct landscape and settlement. Wide range of various maps was used for this area - Müller's map of Bohemia (ca. 1720) followed by the 1st, 2nd and 3rd Military survey of Habsburg empire (1792, 1894, 1938), maps of Stabile cadaster (ca. 1840) and State map derived in the scale 1:5000 (1953, 1972, 1981). All the maps were processed, georeferenced, hand digitized and are further used as base layers for visualization and analysis. The historical aerial imagery was processed in standard ways of photogrammetry and is covering the year 1938, 1953 and the current state. The other important task covered by this project is the georelief reconstruction. We use the old maps and aerial imagery to reconstruct the complete time-line of the georelief development. This time-line is covering the period since 1938 until now. The derived digital terrain models and further on analyzed and printed on a 3D printer. Other reconstruction task are performed using the processed old maps - here we are studying the land-use change, settlement development and the industrialization and brown coal mining effect on the hydrological network structure. The processed data (old maps, aerial photographs, land-use and georelief reconstructions) are published as a web-mapping application built using the ArcGIS API for Flex technology. The application is offering visualization and overlay tools so the user can perform basic landscape and land-use development analyses. The resulting information system will consist of three parts - the web-mapping application, database containing the text and photo information about the vanished towns and villages (spatially linked to the web-mapping application) and other local studies performed on single sites in the region. The local studies are focused on application of data collection methods as UAV (Unmanned Aerial Vehicle), KAP (Kite Aerial Photography) and LIDAR.

30 CFR Appendix I to Subpart T of... - Standard Applicability by Category or Subcategory

Code of Federal Regulations, 2010 CFR

2010-07-01

... Subcategory I Appendix I to Subpart T of Part 57 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION... AND NONMETAL MINES Safety Standards for Methane in Metal and Nonmetal Mines Pt. 57, Subpt. T, App. I Appendix I to Subpart T of Part 57—Standard Applicability by Category or Subcategory Subcategory I-A 57...

30 CFR Appendix I to Subpart T of... - Standard Applicability by Category or Subcategory

Code of Federal Regulations, 2011 CFR

2011-07-01

... Subcategory I Appendix I to Subpart T of Part 57 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION... AND NONMETAL MINES Safety Standards for Methane in Metal and Nonmetal Mines Pt. 57, Subpt. T, App. I Appendix I to Subpart T of Part 57—Standard Applicability by Category or Subcategory Subcategory I-A 57...

26 CFR 1.611-2 - Rules applicable to mines, oil and gas wells, and other natural deposits.

Code of Federal Regulations, 2014 CFR

2014-04-01

... Rules applicable to mines, oil and gas wells, and other natural deposits. (a) Computation of cost depletion of mines, oil and gas wells, and other natural deposits. (1) The basis upon which cost depletion... for the taxable year, the cost depletion for that year shall be computed by dividing such amount by...

Database constraints applied to metabolic pathway reconstruction tools.

PubMed

Vilaplana, Jordi; Solsona, Francesc; Teixido, Ivan; Usié, Anabel; Karathia, Hiren; Alves, Rui; Mateo, Jordi

2014-01-01

Our group developed two biological applications, Biblio-MetReS and Homol-MetReS, accessing the same database of organisms with annotated genes. Biblio-MetReS is a data-mining application that facilitates the reconstruction of molecular networks based on automated text-mining analysis of published scientific literature. Homol-MetReS allows functional (re)annotation of proteomes, to properly identify both the individual proteins involved in the process(es) of interest and their function. It also enables the sets of proteins involved in the process(es) in different organisms to be compared directly. The efficiency of these biological applications is directly related to the design of the shared database. We classified and analyzed the different kinds of access to the database. Based on this study, we tried to adjust and tune the configurable parameters of the database server to reach the best performance of the communication data link to/from the database system. Different database technologies were analyzed. We started the study with a public relational SQL database, MySQL. Then, the same database was implemented by a MapReduce-based database named HBase. The results indicated that the standard configuration of MySQL gives an acceptable performance for low or medium size databases. Nevertheless, tuning database parameters can greatly improve the performance and lead to very competitive runtimes.

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.

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)

Layout-aware text extraction from full-text PDF of scientific articles.

PubMed

Ramakrishnan, Cartic; Patnia, Abhishek; Hovy, Eduard; Burns, Gully Apc

2012-05-28

The Portable Document Format (PDF) is the most commonly used file format for online scientific publications. The absence of effective means to extract text from these PDF files in a layout-aware manner presents a significant challenge for developers of biomedical text mining or biocuration informatics systems that use published literature as an information source. In this paper we introduce the 'Layout-Aware PDF Text Extraction' (LA-PDFText) system to facilitate accurate extraction of text from PDF files of research articles for use in text mining applications. Our paper describes the construction and performance of an open source system that extracts text blocks from PDF-formatted full-text research articles and classifies them into logical units based on rules that characterize specific sections. The LA-PDFText system focuses only on the textual content of the research articles and is meant as a baseline for further experiments into more advanced extraction methods that handle multi-modal content, such as images and graphs. The system works in a three-stage process: (1) Detecting contiguous text blocks using spatial layout processing to locate and identify blocks of contiguous text, (2) Classifying text blocks into rhetorical categories using a rule-based method and (3) Stitching classified text blocks together in the correct order resulting in the extraction of text from section-wise grouped blocks. We show that our system can identify text blocks and classify them into rhetorical categories with Precision1 = 0.96% Recall = 0.89% and F1 = 0.91%. We also present an evaluation of the accuracy of the block detection algorithm used in step 2. Additionally, we have compared the accuracy of the text extracted by LA-PDFText to the text from the Open Access subset of PubMed Central. We then compared this accuracy with that of the text extracted by the PDF2Text system, 2commonly used to extract text from PDF. Finally, we discuss preliminary error analysis for our system and identify further areas of improvement. LA-PDFText is an open-source tool for accurately extracting text from full-text scientific articles. The release of the system is available at http://code.google.com/p/lapdftext/.

Layout-aware text extraction from full-text PDF of scientific articles

PubMed Central

2012-01-01

Background The Portable Document Format (PDF) is the most commonly used file format for online scientific publications. The absence of effective means to extract text from these PDF files in a layout-aware manner presents a significant challenge for developers of biomedical text mining or biocuration informatics systems that use published literature as an information source. In this paper we introduce the ‘Layout-Aware PDF Text Extraction’ (LA-PDFText) system to facilitate accurate extraction of text from PDF files of research articles for use in text mining applications. Results Our paper describes the construction and performance of an open source system that extracts text blocks from PDF-formatted full-text research articles and classifies them into logical units based on rules that characterize specific sections. The LA-PDFText system focuses only on the textual content of the research articles and is meant as a baseline for further experiments into more advanced extraction methods that handle multi-modal content, such as images and graphs. The system works in a three-stage process: (1) Detecting contiguous text blocks using spatial layout processing to locate and identify blocks of contiguous text, (2) Classifying text blocks into rhetorical categories using a rule-based method and (3) Stitching classified text blocks together in the correct order resulting in the extraction of text from section-wise grouped blocks. We show that our system can identify text blocks and classify them into rhetorical categories with Precision1 = 0.96% Recall = 0.89% and F1 = 0.91%. We also present an evaluation of the accuracy of the block detection algorithm used in step 2. Additionally, we have compared the accuracy of the text extracted by LA-PDFText to the text from the Open Access subset of PubMed Central. We then compared this accuracy with that of the text extracted by the PDF2Text system, 2commonly used to extract text from PDF. Finally, we discuss preliminary error analysis for our system and identify further areas of improvement. Conclusions LA-PDFText is an open-source tool for accurately extracting text from full-text scientific articles. The release of the system is available at http://code.google.com/p/lapdftext/. PMID:22640904

2006 SME annual meeting & 7th ICARD, March 26-29, 2006, St. Louis, Missouri. Pre-prints

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

NONE

2006-07-01

Subjects covered by the papers include: enhanced coalbed methane through carbon sequestration, application of laser surface coatings for raw coal screen wear resistance enhancement, application of cross-flow teeter-bed separator in the US coal industry, arsenic removal from drinking water, modelling of fire spread along combustibles in a mine entry, coal's role in sustaining society, real time characterisation of frother bubble thin films, diesel emissions, overcoming stress measurements form underground coal amines, dry jigging coal, estimation of roof strata strength, improving screen bowl centrifuge performance, installation of ventilation shaft at a New Mexico coal mine, evaluation of feasibility of CO{sub 2}more » sequestration in deep coal, robot-human control interaction in mining operations, small mine and contractor safety, coal dust explosibility meter, US coal mine fatalities versus age of mine, and water and slurry bulkheads in underground coal mines.« less

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

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).

Exploratory analysis of textual data from the Mother and Child Handbook using the text-mining method: Relationships with maternal traits and post-partum depression.

PubMed

Matsuda, Yoshio; Manaka, Tomoko; Kobayashi, Makiko; Sato, Shuhei; Ohwada, Michitaka

2016-06-01

The aim of the present study was to examine the possibility of screening apprehensive pregnant women and mothers at risk for post-partum depression from an analysis of the textual data in the Mother and Child Handbook by using the text-mining method. Uncomplicated pregnant women (n = 58) were divided into two groups according to State-Trait Anxiety Inventory grade (high trait [group I, n = 21] and low trait [group II, n = 37]) or Edinburgh Postnatal Depression Scale score (high score [group III, n = 15] and low score [group IV, n = 43]). An exploratory analysis of the textual data from the Maternal and Child Handbook was conducted using the text-mining method with the Word Miner software program. A comparison of the 'structure elements' was made between the two groups. The number of structure elements extracted by separated words from text data was 20 004 and the number of structure elements with a threshold of 2 or more as an initial value was 1168. Fifteen key words related to maternal anxiety, and six key words related to post-partum depression were extracted. The text-mining method is useful for the exploratory analysis of textual data obtained from pregnant woman, and this screening method has been suggested to be useful for apprehensive pregnant women and mothers at risk for post-partum depression. © 2016 Japan Society of Obstetrics and Gynecology.

An Integrated Suite of Text and Data Mining Tools - Phase II

DTIC Science & Technology

2005-08-30

Riverside, CA, USA Mazda Motor Corp, Jpn Univ of Darmstadt, Darmstadt, Ger Navy Center for Applied Research in Artificial Intelligence Univ of...with Georgia Tech Research Corporation developed a desktop text-mining software tool named TechOASIS (known commercially as VantagePoint). By the...of this dataset and groups the Corporate Source items that co-occur with the found items. He decides he is only interested in the institutions

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.

Using ontology network structure in text mining.

PubMed

Berndt, Donald J; McCart, James A; Luther, Stephen L

2010-11-13

Statistical text mining treats documents as bags of words, with a focus on term frequencies within documents and across document collections. Unlike natural language processing (NLP) techniques that rely on an engineered vocabulary or a full-featured ontology, statistical approaches do not make use of domain-specific knowledge. The freedom from biases can be an advantage, but at the cost of ignoring potentially valuable knowledge. The approach proposed here investigates a hybrid strategy based on computing graph measures of term importance over an entire ontology and injecting the measures into the statistical text mining process. As a starting point, we adapt existing search engine algorithms such as PageRank and HITS to determine term importance within an ontology graph. The graph-theoretic approach is evaluated using a smoking data set from the i2b2 National Center for Biomedical Computing, cast as a simple binary classification task for categorizing smoking-related documents, demonstrating consistent improvements in accuracy.

30 CFR 77.100 - Certified person.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR COAL MINE SAFETY AND HEALTH MANDATORY SAFETY STANDARDS, SURFACE COAL MINES AND SURFACE WORK AREAS OF UNDERGROUND COAL MINES Qualified and... independent contractor shall make an application which satisfactorily shows that each such person has had at...

Mine-hunting dolphins of the Navy

NASA Astrophysics Data System (ADS)

Moore, Patrick W.

1997-07-01

Current counter-mine and obstacle avoidance technology is inadequate, and limits the Navy's capability to conduct shallow water (SW) and very shallow water (VSW) MCM in support of beach assaults by Marine Corps forces. Without information as to the location or density of mined beach areas, it must be assumed that if mines are present in one area then they are present in all areas. Marine mammal systems (MMS) are an unusual, effective and unique solution to current problems of mine and obstacle hunting. In the US Navy Mine Warfare Plan for 1994-1995 Marine Mammal Systems are explicitly identified as the Navy's only means of countering buried mines and the best means for dealing with close-tethered mines. The dolphins in these systems possess a biological sonar specifically adapted for their shallow and very shallow water habitat. Research has demonstrated that the dolphin biosonar outperforms any current hardware system available for SW and VSW applications. This presentation will cover current Fleet MCM systems and future technology application to the littoral region.

40 CFR 440.40 - Applicability; description of the mercury ore subcategory.

Code of Federal Regulations, 2010 CFR

2010-07-01

... (CONTINUED) EFFLUENT GUIDELINES AND STANDARDS ORE MINING AND DRESSING POINT SOURCE CATEGORY Mercury Ore... are applicable to discharges from (a) mines, either open-pit or underground, that produce mercury ores...

30 CFR 27.4 - Application procedures and requirements.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Application procedures and requirements. 27.4 Section 27.4 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR TESTING, EVALUATION, AND APPROVAL OF MINING PRODUCTS METHANE-MONITORING SYSTEMS General Provisions § 27.4 Application...

30 CFR 7.3 - Application procedures and requirements.

Code of Federal Regulations, 2013 CFR

2013-07-01

... Section 7.3 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR TESTING, EVALUATION, AND APPROVAL OF MINING PRODUCTS TESTING BY APPLICANT OR THIRD PARTY General § 7.3 Application... Center, 765 Technology Drive, Triadelphia, WV 26059. (b) Fees. Fees calculated in accordance with part 5...

30 CFR 7.3 - Application procedures and requirements.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Section 7.3 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR TESTING, EVALUATION, AND APPROVAL OF MINING PRODUCTS TESTING BY APPLICANT OR THIRD PARTY General § 7.3 Application... Center, 765 Technology Drive, Triadelphia, WV 26059. (b) Fees. Fees calculated in accordance with part 5...

30 CFR 7.3 - Application procedures and requirements.

Code of Federal Regulations, 2014 CFR

2014-07-01

... Section 7.3 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR TESTING, EVALUATION, AND APPROVAL OF MINING PRODUCTS TESTING BY APPLICANT OR THIRD PARTY General § 7.3 Application... Center, 765 Technology Drive, Triadelphia, WV 26059. (b) Fees. Fees calculated in accordance with part 5...

30 CFR 7.3 - Application procedures and requirements.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Section 7.3 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR TESTING, EVALUATION, AND APPROVAL OF MINING PRODUCTS TESTING BY APPLICANT OR THIRD PARTY General § 7.3 Application... Center, 765 Technology Drive, Triadelphia, WV 26059. (b) Fees. Fees calculated in accordance with part 5...

43 CFR 3922.20 - Application contents.

Code of Federal Regulations, 2011 CFR

2011-10-01

..., and transportation methods, including: (1) A description of the mining, retorting, or in situ mining... applications must be filed in the proper BLM State Office. No specific form of application is required, but the... is substantially identical to a technology or method currently in use to produce marketable...

43 CFR 3922.20 - Application contents.

Code of Federal Regulations, 2012 CFR

2012-10-01

..., and transportation methods, including: (1) A description of the mining, retorting, or in situ mining... applications must be filed in the proper BLM State Office. No specific form of application is required, but the... is substantially identical to a technology or method currently in use to produce marketable...

43 CFR 3922.20 - Application contents.

Code of Federal Regulations, 2013 CFR

2013-10-01

..., and transportation methods, including: (1) A description of the mining, retorting, or in situ mining... applications must be filed in the proper BLM State Office. No specific form of application is required, but the... is substantially identical to a technology or method currently in use to produce marketable...

43 CFR 3922.20 - Application contents.

Code of Federal Regulations, 2014 CFR

2014-10-01

..., and transportation methods, including: (1) A description of the mining, retorting, or in situ mining... applications must be filed in the proper BLM State Office. No specific form of application is required, but the... is substantially identical to a technology or method currently in use to produce marketable...

A Data Miner for the Information Power Grid

NASA Technical Reports Server (NTRS)

Hinke, Thomas H.; Parks, John W. (Technical Monitor)

2002-01-01

Grid Miner (GM) is one of the early data mining applications developed by NASA to help users obtain information from the Information Power Grid (IPG). Topics cover include: benefits of data mining, potential use of grids in data mining activities, an overview of the GM application, and a brief review of GM architecture and implementation issues. The current status of the GM system is also discussed.

Data-Mining Technologies for Diabetes: A Systematic Review

PubMed Central

Marinov, Miroslav; Mosa, Abu Saleh Mohammad; Yoo, Illhoi; Boren, Suzanne Austin

2011-01-01

Background The objective of this study is to conduct a systematic review of applications of data-mining techniques in the field of diabetes research. Method We searched the MEDLINE database through PubMed. We initially identified 31 articles by the search, and selected 17 articles representing various data-mining methods used for diabetes research. Our main interest was to identify research goals, diabetes types, data sets, data-mining methods, data-mining software and technologies, and outcomes. Results The applications of data-mining techniques in the selected articles were useful for extracting valuable knowledge and generating new hypothesis for further scientific research/experimentation and improving health care for diabetes patients. The results could be used for both scientific research and real-life practice to improve the quality of health care diabetes patients. Conclusions Data mining has played an important role in diabetes research. Data mining would be a valuable asset for diabetes researchers because it can unearth hidden knowledge from a huge amount of diabetes-related data. We believe that data mining can significantly help diabetes research and ultimately improve the quality of health care for diabetes patients. PMID:22226277

Data Mining and Complex Problems: Case Study in Composite Materials

NASA Technical Reports Server (NTRS)

Rabelo, Luis; Marin, Mario

2009-01-01

Data mining is defined as the discovery of useful, possibly unexpected, patterns and relationships in data using statistical and non-statistical techniques in order to develop schemes for decision and policy making. Data mining can be used to discover the sources and causes of problems in complex systems. In addition, data mining can support simulation strategies by finding the different constants and parameters to be used in the development of simulation models. This paper introduces a framework for data mining and its application to complex problems. To further explain some of the concepts outlined in this paper, the potential application to the NASA Shuttle Reinforced Carbon-Carbon structures and genetic programming is used as an illustration.

Application of EREP imagery to fracture-related mine safety hazards in coal mining and mining-environmental problems in Indiana. [Indiana and Illinois

NASA Technical Reports Server (NTRS)

Wier, C. E. (Principal Investigator); Powell, R. L.; Amato, R. V.; Russell, O. R.; Martin, K. R.

1975-01-01

The author has identified the following significant results. This investigation evaluated the applicability of a variety of sensor types, formats, and resolution capabilities to the study of both fuel and nonfuel mined lands. The image reinforcement provided by stereo viewing of the EREP images proved useful for identifying lineaments and for mined lands mapping. Skylab S190B color and color infrared transparencies were the most useful EREP imagery. New information on lineament and fracture patterns in the bedrock of Indiana and Illinois extracted from analysis of the Skylab imagery has contributed to furthering the geological understanding of this portion of the Illinois basin.

Hydrology of area 18, Eastern Coal Province, Tennessee

USGS Publications Warehouse

May, V.J.

1981-01-01

The Eastern Coal Province is divided into 24 hydrologic reporting areas. This report describes the hydrology of area 18 which is located in the Cumberland River basin in central Tennessee near the southern end of the Province. Hydrologic information and sources are presented as text, tables, maps, and other illustrations designed to be useful to mine owners, operators, and consulting engineers in implementing permit applications that comply with the environmental requirements of the ' Surface Mining Control and Reclamation Act of 1977. ' Area 18 encompasses parts of three physiographic regions; from east to west the Cumberland Plateau, Highland Rim, and Central Basin. The Plateau is underlain by sandstones and shales, with thin interbedded coal beds, of Pennsylvanian age. The Highland Rim and Central Basin are underlain by limestone and dolomite of Mississippian age. Field and laboratory analyses of chemical and physical water-quality parameters of streamflow samples show no widespread water quality problems. Some streams, however, in the heavily mined areas have concentrations of sulfate, iron, manganese, and sediment above natural levels, and pH values below natural levels. Mine seepage and direct mine drainage were not sampled. Ground water occurs in and moves through fractures in the sandstones and shales and solution openings in the limestones and dolomites. Depth to water is variable, ranging from about 5 to 70 feet below land-surface in the limestones and dolomites, and 15 to 40 feet in the coal-bearing rocks. The quality of ground water is generally good. Locally, in coal-bearing rocks, acidic water and high concentrations of manganese, chloride, and iron have been detected. (USGS)

The structure and infrastructure of the global nanotechnology literature

NASA Astrophysics Data System (ADS)

Kostoff, Ronald N.; Stump, Jesse A.; Johnson, Dustin; Murday, James S.; Lau, Clifford G. Y.; Tolles, William M.

2006-08-01

Text mining is the extraction of useful information from large volumes of text. A text mining analysis of the global open nanotechnology literature was performed. Records from the Science Citation Index (SCI)/Social SCI were analyzed to provide the infrastructure of the global nanotechnology literature (prolific authors/journals/institutions/countries, most cited authors/papers/journals) and the thematic structure (taxonomy) of the global nanotechnology literature, from a science perspective. Records from the Engineering Compendex (EC) were analyzed to provide a taxonomy from a technology perspective. The Far Eastern countries have expanded nanotechnology publication output dramatically in the past decade.

PubMed-EX: a web browser extension to enhance PubMed search with text mining features.

PubMed

Tsai, Richard Tzong-Han; Dai, Hong-Jie; Lai, Po-Ting; Huang, Chi-Hsin

2009-11-15

PubMed-EX is a browser extension that marks up PubMed search results with additional text-mining information. PubMed-EX's page mark-up, which includes section categorization and gene/disease and relation mark-up, can help researchers to quickly focus on key terms and provide additional information on them. All text processing is performed server-side, freeing up user resources. PubMed-EX is freely available at http://bws.iis.sinica.edu.tw/PubMed-EX and http://iisr.cse.yzu.edu.tw:8000/PubMed-EX/.

30 CFR 740.15 - Bonds on Federal lands.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Mineral Resources OFFICE OF SURFACE MINING RECLAMATION AND ENFORCEMENT, DEPARTMENT OF THE INTERIOR FEDERAL LANDS PROGRAM GENERAL REQUIREMENTS FOR SURFACE COAL MINING AND RECLAMATION OPERATIONS ON FEDERAL LANDS... surface coal mining, the applicant for a mining permit, if unable to obtain the written consent of the...

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.

Video mining using combinations of unsupervised and supervised learning techniques

NASA Astrophysics Data System (ADS)

Divakaran, Ajay; Miyahara, Koji; Peker, Kadir A.; Radhakrishnan, Regunathan; Xiong, Ziyou

2003-12-01

We discuss the meaning and significance of the video mining problem, and present our work on some aspects of video mining. A simple definition of video mining is unsupervised discovery of patterns in audio-visual content. Such purely unsupervised discovery is readily applicable to video surveillance as well as to consumer video browsing applications. We interpret video mining as content-adaptive or "blind" content processing, in which the first stage is content characterization and the second stage is event discovery based on the characterization obtained in stage 1. We discuss the target applications and find that using a purely unsupervised approach are too computationally complex to be implemented on our product platform. We then describe various combinations of unsupervised and supervised learning techniques that help discover patterns that are useful to the end-user of the application. We target consumer video browsing applications such as commercial message detection, sports highlights extraction etc. We employ both audio and video features. We find that supervised audio classification combined with unsupervised unusual event discovery enables accurate supervised detection of desired events. Our techniques are computationally simple and robust to common variations in production styles etc.

Learning in the context of distribution drift

DTIC Science & Technology

2017-05-09

published in the leading data mining journal, Data Mining and Knowledge Discovery (Webb et. al., 2016)1. We have shown that the previous qualitative...learner Low-bias learner Aggregated classifier Figure 7: Architecture for learning fr m streaming data in th co text of variable or unknown...Learning limited dependence Bayesian classifiers, in Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD

Enhancements for a Dynamic Data Warehousing and Mining System for Large-scale HSCB Data

DTIC Science & Technology

2016-07-20

Intelligent Automation Incorporated Enhancements for a Dynamic Data Warehousing and Mining ...Page | 2 Intelligent Automation Incorporated Monthly Report No. 4 Enhancements for a Dynamic Data Warehousing and Mining System Large-Scale HSCB...including Top Videos, Top Users, Top Words, and Top Languages, and also applied NER to the text associated with YouTube posts. We have also developed UI for

Enhancements for a Dynamic Data Warehousing and Mining System for Large-Scale HSCB Data

DTIC Science & Technology

2016-07-20

Intelligent Automation Incorporated Enhancements for a Dynamic Data Warehousing and Mining ...Page | 2 Intelligent Automation Incorporated Monthly Report No. 4 Enhancements for a Dynamic Data Warehousing and Mining System Large-Scale HSCB...including Top Videos, Top Users, Top Words, and Top Languages, and also applied NER to the text associated with YouTube posts. We have also developed UI for

Applications of multi-season hyperspectral remote sensing for acid mine water characterization and mapping of secondary iron minerals associated with acid mine drainage

NASA Astrophysics Data System (ADS)

Davies, Gwendolyn E.

Acid mine drainage (AMD) resulting from the oxidation of sulfides in mine waste is a major environmental issue facing the mining industry today. Open pit mines, tailings ponds, ore stockpiles, and waste rock dumps can all be significant sources of pollution, primarily heavy metals. These large mining-induced footprints are often located across vast geographic expanses and are difficult to access. With the continuing advancement of imaging satellites, remote sensing may provide a useful monitoring tool for pit lake water quality and the rapid assessment of abandoned mine sites. This study explored the applications of laboratory spectroscopy and multi-season hyperspectral remote sensing for environmental monitoring of mine waste environments. Laboratory spectral experiments were first performed on acid mine waters and synthetic ferric iron solutions to identify and isolate the unique spectral properties of mine waters. These spectral characterizations were then applied to airborne hyperspectral imagery for identification of poor water quality in AMD ponds at the Leviathan Mine Superfund site, CA. Finally, imagery varying in temporal and spatial resolutions were used to identify changes in mineralogy over weathering overburden piles and on dry AMD pond liner surfaces at the Leviathan Mine. Results show the utility of hyperspectral remote sensing for monitoring a diverse range of surfaces associated with AMD.

78 FR 7458 - Petitions for Modification of Application of Existing Mandatory Safety Standards

Federal Register 2010, 2011, 2012, 2013, 2014

2013-02-01

.... Petitioner: Sterling Mining Corporation, P.O. Box 217, North Lima, Ohio 44452. Mines: Shean Hill, MSHA I.D... outlets and valves. 6. Due to the thin coal seam and low mining height, the pipes placed along the roof... Granules (Ione) LLC, 1101 Opal Court, Suite 315, Hagerstown, Maryland 21740. Mine: Ione Mine, MSHA I.D. No...

30 CFR 71.404 - Application for waiver of surface facilities requirements.

Code of Federal Regulations, 2013 CFR

2013-07-01

... OF UNDERGROUND COAL MINES Surface Bathing Facilities, Change Rooms, and Sanitary Flush Toilet Facilities at Surface Coal Mines § 71.404 Application for waiver of surface facilities requirements. (a... 30 Mineral Resources 1 2013-07-01 2013-07-01 false Application for waiver of surface facilities...

30 CFR 71.404 - Application for waiver of surface facilities requirements.

Code of Federal Regulations, 2011 CFR

2011-07-01

... OF UNDERGROUND COAL MINES Surface Bathing Facilities, Change Rooms, and Sanitary Flush Toilet Facilities at Surface Coal Mines § 71.404 Application for waiver of surface facilities requirements. (a... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Application for waiver of surface facilities...

30 CFR 71.404 - Application for waiver of surface facilities requirements.

Code of Federal Regulations, 2012 CFR

2012-07-01

... OF UNDERGROUND COAL MINES Surface Bathing Facilities, Change Rooms, and Sanitary Flush Toilet Facilities at Surface Coal Mines § 71.404 Application for waiver of surface facilities requirements. (a... 30 Mineral Resources 1 2012-07-01 2012-07-01 false Application for waiver of surface facilities...

30 CFR 71.404 - Application for waiver of surface facilities requirements.

Code of Federal Regulations, 2014 CFR

2014-07-01

... OF UNDERGROUND COAL MINES Surface Bathing Facilities, Change Rooms, and Sanitary Flush Toilet Facilities at Surface Coal Mines § 71.404 Application for waiver of surface facilities requirements. (a... 30 Mineral Resources 1 2014-07-01 2014-07-01 false Application for waiver of surface facilities...

43 CFR 2091.1 - Action on applications and mining claims.

Code of Federal Regulations, 2011 CFR

2011-10-01

... Segregation and Opening of Lands § 2091.1 Action on applications and mining claims. (a) Except where the law... lessee exclusive use of the lands; (4) Classifications existing under appropriate law: (5) Segregation due to an application previously filed under appropriate law and regulations; (6) Segregation...

Empirical advances with text mining of electronic health records.

PubMed

Delespierre, T; Denormandie, P; Bar-Hen, A; Josseran, L

2017-08-22

Korian is a private group specializing in medical accommodations for elderly and dependent people. A professional data warehouse (DWH) established in 2010 hosts all of the residents' data. Inside this information system (IS), clinical narratives (CNs) were used only by medical staff as a residents' care linking tool. The objective of this study was to show that, through qualitative and quantitative textual analysis of a relatively small physiotherapy and well-defined CN sample, it was possible to build a physiotherapy corpus and, through this process, generate a new body of knowledge by adding relevant information to describe the residents' care and lives. Meaningful words were extracted through Standard Query Language (SQL) with the LIKE function and wildcards to perform pattern matching, followed by text mining and a word cloud using R® packages. Another step involved principal components and multiple correspondence analyses, plus clustering on the same residents' sample as well as on other health data using a health model measuring the residents' care level needs. By combining these techniques, physiotherapy treatments could be characterized by a list of constructed keywords, and the residents' health characteristics were built. Feeding defects or health outlier groups could be detected, physiotherapy residents' data and their health data were matched, and differences in health situations showed qualitative and quantitative differences in physiotherapy narratives. This textual experiment using a textual process in two stages showed that text mining and data mining techniques provide convenient tools to improve residents' health and quality of care by adding new, simple, useable data to the electronic health record (EHR). When used with a normalized physiotherapy problem list, text mining through information extraction (IE), named entity recognition (NER) and data mining (DM) can provide a real advantage to describe health care, adding new medical material and helping to integrate the EHR system into the health staff work environment.

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

75 FR 79033 - Proposed Extension of Existing Information Collection; Application for Waiver of Surface Sanitary...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-12-17

... DEPARTMENT OF LABOR Mine Safety and Health Administration [OMB Control No. 1219-0024] Proposed...' Requirements (Pertaining to Coal Mines) AGENCY: Mine Safety and Health Administration, Labor. ACTION: Notice of... collection requirements on respondents can be properly assessed. Currently, the Mine Safety and Health...

Association Rule Mining from an Intelligent Tutor

ERIC Educational Resources Information Center

Dogan, Buket; Camurcu, A. Yilmaz

2008-01-01

Educational data mining is a very novel research area, offering fertile ground for many interesting data mining applications. Educational data mining can extract useful information from educational activities for better understanding and assessment of the student learning process. In this way, it is possible to explore how students learn topics in…

Data Mining and Knowledge Management in Higher Education -Potential Applications.

ERIC Educational Resources Information Center

Luan, Jing

This paper introduces a new decision support tool, data mining, in the context of knowledge management. The most striking features of data mining techniques are clustering and prediction. The clustering aspect of data mining offers comprehensive characteristics analysis of students, while the predicting function estimates the likelihood for a…

30 CFR 77.105 - Qualified hoistman; slope or shaft sinking operation; qualifications.

Code of Federal Regulations, 2010 CFR

2010-07-01

...) Applications for Secretarial qualification should be submitted to the Mine Safety and Health Administration... operation; qualifications. 77.105 Section 77.105 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR COAL MINE SAFETY AND HEALTH MANDATORY SAFETY STANDARDS, SURFACE COAL MINES AND SURFACE...

HPIminer: A text mining system for building and visualizing human protein interaction networks and pathways.

PubMed

Subramani, Suresh; Kalpana, Raja; Monickaraj, Pankaj Moses; Natarajan, Jeyakumar

2015-04-01

The knowledge on protein-protein interactions (PPI) and their related pathways are equally important to understand the biological functions of the living cell. Such information on human proteins is highly desirable to understand the mechanism of several diseases such as cancer, diabetes, and Alzheimer's disease. Because much of that information is buried in biomedical literature, an automated text mining system for visualizing human PPI and pathways is highly desirable. In this paper, we present HPIminer, a text mining system for visualizing human protein interactions and pathways from biomedical literature. HPIminer extracts human PPI information and PPI pairs from biomedical literature, and visualize their associated interactions, networks and pathways using two curated databases HPRD and KEGG. To our knowledge, HPIminer is the first system to build interaction networks from literature as well as curated databases. Further, the new interactions mined only from literature and not reported earlier in databases are highlighted as new. A comparative study with other similar tools shows that the resultant network is more informative and provides additional information on interacting proteins and their associated networks. Copyright © 2015 Elsevier Inc. All rights reserved.

Industrial Internet of Things: (IIoT) applications in underground coal mines.

PubMed

Zhou, C; Damiano, N; Whisner, B; Reyes, M

2017-12-01

The Industrial Internet of Things (IIoT), a concept that combines sensor networks and control systems, has been employed in several industries to improve productivity and safety. U.S. National Institute for Occupational Safety and Health (NIOSH) researchers are investigating IIoT applications to identify the challenges of and potential solutions for transferring IIoT from other industries to the mining industry. Specifically, NIOSH has reviewed existing sensors and communications network systems used in U.S. underground coal mines to determine whether they are capable of supporting IIoT systems. The results show that about 40 percent of the installed post-accident communication systems as of 2014 require minimal or no modification to support IIoT applications. NIOSH researchers also developed an IIoT monitoring and control prototype system using low-cost microcontroller Wi-Fi boards to detect a door opening on a refuge alternative, activate fans located inside the Pittsburgh Experimental Mine and actuate an alarm beacon on the surface. The results of this feasibility study can be used to explore IIoT applications in underground coal mines based on existing communication and tracking infrastructure.

Comparisons and Selections of Features and Classifiers for Short Text Classification

NASA Astrophysics Data System (ADS)

Wang, Ye; Zhou, Zhi; Jin, Shan; Liu, Debin; Lu, Mi

2017-10-01

Short text is considerably different from traditional long text documents due to its shortness and conciseness, which somehow hinders the applications of conventional machine learning and data mining algorithms in short text classification. According to traditional artificial intelligence methods, we divide short text classification into three steps, namely preprocessing, feature selection and classifier comparison. In this paper, we have illustrated step-by-step how we approach our goals. Specifically, in feature selection, we compared the performance and robustness of the four methods of one-hot encoding, tf-idf weighting, word2vec and paragraph2vec, and in the classification part, we deliberately chose and compared Naive Bayes, Logistic Regression, Support Vector Machine, K-nearest Neighbor and Decision Tree as our classifiers. Then, we compared and analysed the classifiers horizontally with each other and vertically with feature selections. Regarding the datasets, we crawled more than 400,000 short text files from Shanghai and Shenzhen Stock Exchanges and manually labeled them into two classes, the big and the small. There are eight labels in the big class, and 59 labels in the small class.

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

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

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.

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.

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.

Rule-based statistical data mining agents for an e-commerce application

NASA Astrophysics Data System (ADS)

Qin, Yi; Zhang, Yan-Qing; King, K. N.; Sunderraman, Rajshekhar

2003-03-01

Intelligent data mining techniques have useful e-Business applications. Because an e-Commerce application is related to multiple domains such as statistical analysis, market competition, price comparison, profit improvement and personal preferences, this paper presents a hybrid knowledge-based e-Commerce system fusing intelligent techniques, statistical data mining, and personal information to enhance QoS (Quality of Service) of e-Commerce. A Web-based e-Commerce application software system, eDVD Web Shopping Center, is successfully implemented uisng Java servlets and an Oracle81 database server. Simulation results have shown that the hybrid intelligent e-Commerce system is able to make smart decisions for different customers.

30 CFR 18.94 - Application for field approval; contents of application.

Code of Federal Regulations, 2011 CFR

2011-07-01

... approval or certification has been issued under the provisions of Bureau of Mines Schedules 2D, 2E, 2F, or... under Bureau of Mines Schedule 2D, 2E, 2F, or 2G, photographs or a single layout drawing which clearly... certified under Bureau of Mines Schedule 2D, 2E, 2F, or 2G, a single layout drawing which clearly identifies...

30 CFR 18.94 - Application for field approval; contents of application.

Code of Federal Regulations, 2012 CFR

2012-07-01

... approval or certification has been issued under the provisions of Bureau of Mines Schedules 2D, 2E, 2F, or... under Bureau of Mines Schedule 2D, 2E, 2F, or 2G, photographs or a single layout drawing which clearly... certified under Bureau of Mines Schedule 2D, 2E, 2F, or 2G, a single layout drawing which clearly identifies...

30 CFR 18.94 - Application for field approval; contents of application.

Code of Federal Regulations, 2013 CFR

2013-07-01

... approval or certification has been issued under the provisions of Bureau of Mines Schedules 2D, 2E, 2F, or... under Bureau of Mines Schedule 2D, 2E, 2F, or 2G, photographs or a single layout drawing which clearly... certified under Bureau of Mines Schedule 2D, 2E, 2F, or 2G, a single layout drawing which clearly identifies...

30 CFR 18.94 - Application for field approval; contents of application.

Code of Federal Regulations, 2014 CFR

2014-07-01

... approval or certification has been issued under the provisions of Bureau of Mines Schedules 2D, 2E, 2F, or... under Bureau of Mines Schedule 2D, 2E, 2F, or 2G, photographs or a single layout drawing which clearly... certified under Bureau of Mines Schedule 2D, 2E, 2F, or 2G, a single layout drawing which clearly identifies...

30 CFR 18.94 - Application for field approval; contents of application.

Code of Federal Regulations, 2010 CFR

2010-07-01

... approval or certification has been issued under the provisions of Bureau of Mines Schedules 2D, 2E, 2F, or... under Bureau of Mines Schedule 2D, 2E, 2F, or 2G, photographs or a single layout drawing which clearly... certified under Bureau of Mines Schedule 2D, 2E, 2F, or 2G, a single layout drawing which clearly identifies...

Data mining in radiology

PubMed Central

Kharat, Amit T; Singh, Amarjit; Kulkarni, Vilas M; Shah, Digish

2014-01-01

Data mining facilitates the study of radiology data in various dimensions. It converts large patient image and text datasets into useful information that helps in improving patient care and provides informative reports. Data mining technology analyzes data within the Radiology Information System and Hospital Information System using specialized software which assesses relationships and agreement in available information. By using similar data analysis tools, radiologists can make informed decisions and predict the future outcome of a particular imaging finding. Data, information and knowledge are the components of data mining. Classes, Clusters, Associations, Sequential patterns, Classification, Prediction and Decision tree are the various types of data mining. Data mining has the potential to make delivery of health care affordable and ensure that the best imaging practices are followed. It is a tool for academic research. Data mining is considered to be ethically neutral, however concerns regarding privacy and legality exists which need to be addressed to ensure success of data mining. PMID:25024513

15 CFR 970.301 - Requirements for applications based on pre-enactment exploration.

Code of Federal Regulations, 2012 CFR

2012-01-01

... COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR.... (f) The coordinates and any chart of the logical mining unit applied for in an application based on a...

15 CFR 970.301 - Requirements for applications based on pre-enactment exploration.

Code of Federal Regulations, 2014 CFR

2014-01-01

... COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR.... (f) The coordinates and any chart of the logical mining unit applied for in an application based on a...

15 CFR 970.301 - Requirements for applications based on pre-enactment exploration.

Code of Federal Regulations, 2013 CFR

2013-01-01

... COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR.... (f) The coordinates and any chart of the logical mining unit applied for in an application based on a...

15 CFR 970.301 - Requirements for applications based on pre-enactment exploration.

Code of Federal Regulations, 2011 CFR

2011-01-01

... COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR.... (f) The coordinates and any chart of the logical mining unit applied for in an application based on a...

15 CFR 970.301 - Requirements for applications based on pre-enactment exploration.

Code of Federal Regulations, 2010 CFR

2010-01-01

... COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR.... (f) The coordinates and any chart of the logical mining unit applied for in an application based on a...

Analyzing asset management data using data and text mining.

DOT National Transportation Integrated Search

2014-07-01

Predictive models using text from a sample competitively bid California highway projects have been used to predict a construction : projects likely level of cost overrun. A text description of the project and the text of the five largest project line...

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

BioC implementations in Go, Perl, Python and Ruby.

PubMed

Liu, Wanli; Islamaj Doğan, Rezarta; Kwon, Dongseop; Marques, Hernani; Rinaldi, Fabio; Wilbur, W John; Comeau, Donald C

2014-01-01

As part of a communitywide effort for evaluating text mining and information extraction systems applied to the biomedical domain, BioC is focused on the goal of interoperability, currently a major barrier to wide-scale adoption of text mining tools. BioC is a simple XML format, specified by DTD, for exchanging data for biomedical natural language processing. With initial implementations in C++ and Java, BioC provides libraries of code for reading and writing BioC text documents and annotations. We extend BioC to Perl, Python, Go and Ruby. We used SWIG to extend the C++ implementation for Perl and one Python implementation. A second Python implementation and the Ruby implementation use native data structures and libraries. BioC is also implemented in the Google language Go. BioC modules are functional in all of these languages, which can facilitate text mining tasks. BioC implementations are freely available through the BioC site: http://bioc.sourceforge.net. Database URL: http://bioc.sourceforge.net/ Published by Oxford University Press 2014. This work is written by US Government employees and is in the public domain in the US.

Text mining for metabolic pathways, signaling cascades, and protein networks.

PubMed

Hoffmann, Robert; Krallinger, Martin; Andres, Eduardo; Tamames, Javier; Blaschke, Christian; Valencia, Alfonso

2005-05-10

The complexity of the information stored in databases and publications on metabolic and signaling pathways, the high throughput of experimental data, and the growing number of publications make it imperative to provide systems to help the researcher navigate through these interrelated information resources. Text-mining methods have started to play a key role in the creation and maintenance of links between the information stored in biological databases and its original sources in the literature. These links will be extremely useful for database updating and curation, especially if a number of technical problems can be solved satisfactorily, including the identification of protein and gene names (entities in general) and the characterization of their types of interactions. The first generation of openly accessible text-mining systems, such as iHOP (Information Hyperlinked over Proteins), provides additional functions to facilitate the reconstruction of protein interaction networks, combine database and text information, and support the scientist in the formulation of novel hypotheses. The next challenge is the generation of comprehensive information regarding the general function of signaling pathways and protein interaction networks.

TOY SAFETY SURVEILLANCE FROM ONLINE REVIEWS

PubMed Central

Winkler, Matt; Abrahams, Alan S.; Gruss, Richard; Ehsani, Johnathan P.

2016-01-01

Toy-related injuries account for a significant number of childhood injuries and the prevention of these injuries remains a goal for regulatory agencies and manufacturers. Text-mining is an increasingly prevalent method for uncovering the significance of words using big data. This research sets out to determine the effectiveness of text-mining in uncovering potentially dangerous children’s toys. We develop a danger word list, also known as a ‘smoke word’ list, from injury and recall text narratives. We then use the smoke word lists to score over one million Amazon reviews, with the top scores denoting potential safety concerns. We compare the smoke word list to conventional sentiment analysis techniques, in terms of both word overlap and effectiveness. We find that smoke word lists are highly distinct from conventional sentiment dictionaries and provide a statistically significant method for identifying safety concerns in children’s toy reviews. Our findings indicate that text-mining is, in fact, an effective method for the surveillance of safety concerns in children’s toys and could be a gateway to effective prevention of toy-product-related injuries. PMID:27942092

30 CFR 57.22207 - Booster fans (I-A, II-A, III, and V-A mines).

Code of Federal Regulations, 2014 CFR

2014-07-01

... 30 Mineral Resources 1 2014-07-01 2014-07-01 false Booster fans (I-A, II-A, III, and V-A mines... NONMETAL MINES Safety Standards for Methane in Metal and Nonmetal Mines Ventilation § 57.22207 Booster fans (I-A, II-A, III, and V-A mines). (a) Booster fans shall be approved by MSHA under the applicable...

30 CFR 57.22207 - Booster fans (I-A, II-A, III, and V-A mines).

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Booster fans (I-A, II-A, III, and V-A mines... NONMETAL MINES Safety Standards for Methane in Metal and Nonmetal Mines Ventilation § 57.22207 Booster fans (I-A, II-A, III, and V-A mines). (a) Booster fans shall be approved by MSHA under the applicable...

30 CFR 57.22207 - Booster fans (I-A, II-A, III, and V-A mines).

Code of Federal Regulations, 2013 CFR

2013-07-01

... 30 Mineral Resources 1 2013-07-01 2013-07-01 false Booster fans (I-A, II-A, III, and V-A mines... NONMETAL MINES Safety Standards for Methane in Metal and Nonmetal Mines Ventilation § 57.22207 Booster fans (I-A, II-A, III, and V-A mines). (a) Booster fans shall be approved by MSHA under the applicable...

30 CFR 57.22207 - Booster fans (I-A, II-A, III, and V-A mines).

Code of Federal Regulations, 2012 CFR

2012-07-01

... 30 Mineral Resources 1 2012-07-01 2012-07-01 false Booster fans (I-A, II-A, III, and V-A mines... NONMETAL MINES Safety Standards for Methane in Metal and Nonmetal Mines Ventilation § 57.22207 Booster fans (I-A, II-A, III, and V-A mines). (a) Booster fans shall be approved by MSHA under the applicable...

Knight Hawk adapts highwall mining for Southern Illinois

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

Buchsbaum, L.

2007-10-15

A few years ago while planning their first underground operation and trying to decide how to mine shallow seams, Knight Hawk purchased a 'Superior Highwall Miner' (SHM). Since then this small innovative company has been pioneering the use of highwall mining in a trenching application in for example the Illinois Basin. Highwall mining is very suitable for contour mining in Appalachia. The article discusses the recent improvements and the advantages of SHM mining systems. 3 photos.

Land Ecological Security Evaluation of Underground Iron Mine Based on PSR Model

NASA Astrophysics Data System (ADS)

Xiao, Xiao; Chen, Yong; Ruan, Jinghua; Hong, Qiang; Gan, Yong

2018-01-01

Iron ore mine provides an important strategic resource to the national economy while it also causes many serious ecological problems to the environment. The study summed up the characteristics of ecological environment problems of underground iron mine. Considering the mining process of underground iron mine, we analysis connections between mining production, resource, environment and economical background. The paper proposed a land ecological security evaluation system and method of underground iron mine based on Pressure-State-Response model. Our application in Chengchao iron mine proves its efficiency and promising guide on land ecological security evaluation.

Mining the Text: 34 Text Features that Can Ease or Obstruct Text Comprehension and Use

ERIC Educational Resources Information Center

White, Sheida

2012-01-01

This article presents 34 characteristics of texts and tasks ("text features") that can make continuous (prose), noncontinuous (document), and quantitative texts easier or more difficult for adolescents and adults to comprehend and use. The text features were identified by examining the assessment tasks and associated texts in the national…

30 CFR 75.1712-8 - Application for waiver of location requirements for underground sanitary facilities.

Code of Federal Regulations, 2010 CFR

2010-07-01

... for underground sanitary facilities. 75.1712-8 Section 75.1712-8 Mineral Resources MINE SAFETY AND...-UNDERGROUND COAL MINES Miscellaneous § 75.1712-8 Application for waiver of location requirements for underground sanitary facilities. Applications for waivers of the location requirements of § 75.1712-6 shall be...

30 CFR 75.1712-8 - Application for waiver of location requirements for underground sanitary facilities.

Code of Federal Regulations, 2014 CFR

2014-07-01

... for underground sanitary facilities. 75.1712-8 Section 75.1712-8 Mineral Resources MINE SAFETY AND...-UNDERGROUND COAL MINES Miscellaneous § 75.1712-8 Application for waiver of location requirements for underground sanitary facilities. Applications for waivers of the location requirements of § 75.1712-6 shall be...

30 CFR 75.1712-8 - Application for waiver of location requirements for underground sanitary facilities.

Code of Federal Regulations, 2012 CFR

2012-07-01

... for underground sanitary facilities. 75.1712-8 Section 75.1712-8 Mineral Resources MINE SAFETY AND...-UNDERGROUND COAL MINES Miscellaneous § 75.1712-8 Application for waiver of location requirements for underground sanitary facilities. Applications for waivers of the location requirements of § 75.1712-6 shall be...

30 CFR 75.1712-8 - Application for waiver of location requirements for underground sanitary facilities.

Code of Federal Regulations, 2013 CFR

2013-07-01

... for underground sanitary facilities. 75.1712-8 Section 75.1712-8 Mineral Resources MINE SAFETY AND...-UNDERGROUND COAL MINES Miscellaneous § 75.1712-8 Application for waiver of location requirements for underground sanitary facilities. Applications for waivers of the location requirements of § 75.1712-6 shall be...

30 CFR 75.1712-8 - Application for waiver of location requirements for underground sanitary facilities.

Code of Federal Regulations, 2011 CFR

2011-07-01

... for underground sanitary facilities. 75.1712-8 Section 75.1712-8 Mineral Resources MINE SAFETY AND...-UNDERGROUND COAL MINES Miscellaneous § 75.1712-8 Application for waiver of location requirements for underground sanitary facilities. Applications for waivers of the location requirements of § 75.1712-6 shall be...

40 CFR 434.82 - Effluent limitations attainable by the application of the best practicable control technology...

Code of Federal Regulations, 2013 CFR

2013-07-01

... application of the best practicable control technology currently available (BPT). 434.82 Section 434.82... practicable control technology currently available (BPT). Except as provided in 40 CFR 125.30 through 125.32, the following effluent limitations apply to mine drainage from applicable areas of western coal mining...

40 CFR 434.82 - Effluent limitations attainable by the application of the best practicable control technology...

Code of Federal Regulations, 2010 CFR

2010-07-01

... application of the best practicable control technology currently available (BPT). 434.82 Section 434.82... control technology currently available (BPT). Except as provided in 40 CFR 125.30 through 125.32, the following effluent limitations apply to mine drainage from applicable areas of western coal mining...

40 CFR 434.82 - Effluent limitations attainable by the application of the best practicable control technology...

Code of Federal Regulations, 2014 CFR

2014-07-01

... application of the best practicable control technology currently available (BPT). 434.82 Section 434.82... practicable control technology currently available (BPT). Except as provided in 40 CFR 125.30 through 125.32, the following effluent limitations apply to mine drainage from applicable areas of western coal mining...

40 CFR 440.142 - Effluent limitations representing the degree of effluent reduction attainable by the application...

Code of Federal Regulations, 2011 CFR

2011-07-01

... degree of effluent reduction attainable by the application of the best practicable control technology... (CONTINUED) EFFLUENT GUIDELINES AND STANDARDS ORE MINING AND DRESSING POINT SOURCE CATEGORY Gold Placer Mine... application of the best practicable control technology currently available (BPT). Except as provided in 40 CFR...

40 CFR 434.82 - Effluent limitations attainable by the application of the best practicable control technology...

Code of Federal Regulations, 2012 CFR

2012-07-01

... application of the best practicable control technology currently available (BPT). 434.82 Section 434.82... practicable control technology currently available (BPT). Except as provided in 40 CFR 125.30 through 125.32, the following effluent limitations apply to mine drainage from applicable areas of western coal mining...

40 CFR 434.82 - Effluent limitations attainable by the application of the best practicable control technology...

Code of Federal Regulations, 2011 CFR

2011-07-01

... application of the best practicable control technology currently available (BPT). 434.82 Section 434.82... control technology currently available (BPT). Except as provided in 40 CFR 125.30 through 125.32, the following effluent limitations apply to mine drainage from applicable areas of western coal mining...

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

30 CFR 780.10 - Information collection.

Code of Federal Regulations, 2010 CFR

2010-07-01

... COAL MINING AND RECLAMATION OPERATIONS PERMITS AND COAL EXPLORATION SYSTEMS UNDER REGULATORY PROGRAMS SURFACE MINING PERMIT APPLICATIONS-MINIMUM REQUIREMENTS FOR RECLAMATION AND OPERATION PLAN § 780.10... activities, including a requirement that the application include an operation and reclamation plan. The...

25 CFR 215.6 - Applications for leases; consent of Indian owners.

Code of Federal Regulations, 2010 CFR

2010-04-01

... ZINC MINING OPERATIONS AND LEASES, QUAPAW AGENCY § 215.6 Applications for leases; consent of Indian... such land be leased or offered for lease for lead and zinc mining purposes should be addressed to the...

25 CFR 215.6 - Applications for leases; consent of Indian owners.

Code of Federal Regulations, 2011 CFR

2011-04-01

... ZINC MINING OPERATIONS AND LEASES, QUAPAW AGENCY § 215.6 Applications for leases; consent of Indian... such land be leased or offered for lease for lead and zinc mining purposes should be addressed to the...

78 FR 42977 - Petitions for Modification of Application of Existing Mandatory Safety Standards

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-18

...-008-M. Petitioner: U.S. Silver Idaho, Inc., 1801 California Street, Suite 4900, Denver, Colorado 80202... Limited, 1801 California Street, Suite 4900, Denver, Colorado 80202. Mine: Lucky Friday Mine, MSHA I.D. No... ribs' structure stability. (1) The Gold Hunter portion of the Lucky Friday mine is a deep mining...

76 FR 37831 - Petitions for Modification of Application of Existing Mandatory Safety Standards

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-28

... strata breakage for the mine; (2) to facilitate methane drainage, degasification casing of suitable... on the shift prior to mining through the well. The methane monitor(s) on the continuous mining.... (8) When mining is in progress, tests for methane will be made with a hand-held methane detector at...

Using text mining techniques to extract phenotypic information from the PhenoCHF corpus

PubMed Central

2015-01-01

Background Phenotypic information locked away in unstructured narrative text presents significant barriers to information accessibility, both for clinical practitioners and for computerised applications used for clinical research purposes. Text mining (TM) techniques have previously been applied successfully to extract different types of information from text in the biomedical domain. They have the potential to be extended to allow the extraction of information relating to phenotypes from free text. Methods To stimulate the development of TM systems that are able to extract phenotypic information from text, we have created a new corpus (PhenoCHF) that is annotated by domain experts with several types of phenotypic information relating to congestive heart failure. To ensure that systems developed using the corpus are robust to multiple text types, it integrates text from heterogeneous sources, i.e., electronic health records (EHRs) and scientific articles from the literature. We have developed several different phenotype extraction methods to demonstrate the utility of the corpus, and tested these methods on a further corpus, i.e., ShARe/CLEF 2013. Results Evaluation of our automated methods showed that PhenoCHF can facilitate the training of reliable phenotype extraction systems, which are robust to variations in text type. These results have been reinforced by evaluating our trained systems on the ShARe/CLEF corpus, which contains clinical records of various types. Like other studies within the biomedical domain, we found that solutions based on conditional random fields produced the best results, when coupled with a rich feature set. Conclusions PhenoCHF is the first annotated corpus aimed at encoding detailed phenotypic information. The unique heterogeneous composition of the corpus has been shown to be advantageous in the training of systems that can accurately extract phenotypic information from a range of different text types. Although the scope of our annotation is currently limited to a single disease, the promising results achieved can stimulate further work into the extraction of phenotypic information for other diseases. The PhenoCHF annotation guidelines and annotations are publicly available at https://code.google.com/p/phenochf-corpus. PMID:26099853

Using text mining techniques to extract phenotypic information from the PhenoCHF corpus.

PubMed

Alnazzawi, Noha; Thompson, Paul; Batista-Navarro, Riza; Ananiadou, Sophia

2015-01-01

Phenotypic information locked away in unstructured narrative text presents significant barriers to information accessibility, both for clinical practitioners and for computerised applications used for clinical research purposes. Text mining (TM) techniques have previously been applied successfully to extract different types of information from text in the biomedical domain. They have the potential to be extended to allow the extraction of information relating to phenotypes from free text. To stimulate the development of TM systems that are able to extract phenotypic information from text, we have created a new corpus (PhenoCHF) that is annotated by domain experts with several types of phenotypic information relating to congestive heart failure. To ensure that systems developed using the corpus are robust to multiple text types, it integrates text from heterogeneous sources, i.e., electronic health records (EHRs) and scientific articles from the literature. We have developed several different phenotype extraction methods to demonstrate the utility of the corpus, and tested these methods on a further corpus, i.e., ShARe/CLEF 2013. Evaluation of our automated methods showed that PhenoCHF can facilitate the training of reliable phenotype extraction systems, which are robust to variations in text type. These results have been reinforced by evaluating our trained systems on the ShARe/CLEF corpus, which contains clinical records of various types. Like other studies within the biomedical domain, we found that solutions based on conditional random fields produced the best results, when coupled with a rich feature set. PhenoCHF is the first annotated corpus aimed at encoding detailed phenotypic information. The unique heterogeneous composition of the corpus has been shown to be advantageous in the training of systems that can accurately extract phenotypic information from a range of different text types. Although the scope of our annotation is currently limited to a single disease, the promising results achieved can stimulate further work into the extraction of phenotypic information for other diseases. The PhenoCHF annotation guidelines and annotations are publicly available at https://code.google.com/p/phenochf-corpus.

Regional and temporal variability of the isotope composition (O, S) of atmospheric sulphate in the region of Freiberg, Germany, and consequences for dissolved sulphate in groundwater and river water.

PubMed

Tichomirowa, Marion; Heidel, Claudia

2012-01-01

The isotope composition of dissolved sulphate and strontium in atmospheric deposition, groundwater, mine water and river water in the region of Freiberg was investigated to better understand the fate of these components in the regional and global water cycle. Most of the isotope variations of dissolved sulphates in atmospheric deposition from three locations sampled bi- or tri-monthly can be explained by fractionation processes leading to lower [Formula: see text] (of about 2-3‰) and higher [Formula: see text] (of about 8-10‰) values in summer compared with the winter period. These samples showed a negative correlation between [Formula: see text] and [Formula: see text] values and a weak positive correlation between [Formula: see text] and [Formula: see text] values. They reflect the sulphate formed by aqueous oxidation from long-range transport in clouds. However, these isotope variations were superimposed by changes of the dominating atmospheric sulphate source. At two of the sampling points, large variations of mean annual [Formula: see text] values from atmospheric bulk deposition were recorded. From 2008 to 2009, the mean annual [Formula: see text] value increased by about 5‰; and decreased by about 4‰ from 2009 to 2010. A change in the dominating sulphate source or oxidation pathways of SO(2) in the atmosphere is proposed to cause these shifts. No changes were found in corresponding [Formula: see text] values. Groundwater, river water and some mine waters (where groundwater was the dominating sulphate source) also showed temporal shifts in their [Formula: see text] values corresponding to those of bulk atmospheric deposition, albeit to a lower degree. The mean transit time of atmospheric sulphur through the soil into the groundwater and river water was less than a year and therefore much shorter than previously suggested. Mining activities of about 800 years in the Freiberg region may have led to large subsurface areas with an enhanced groundwater flow along fractures and mined-refilled ore lodes which may shorten transit times of sulphate from precipitation through groundwater into river water.

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.

Application of Three Existing Stope Boundary Optimisation Methods in an Operating Underground Mine

NASA Astrophysics Data System (ADS)

Erdogan, Gamze; Yavuz, Mahmut

2017-12-01

The underground mine planning and design optimisation process have received little attention because of complexity and variability of problems in underground mines. Although a number of optimisation studies and software tools are available and some of them, in special, have been implemented effectively to determine the ultimate-pit limits in an open pit mine, there is still a lack of studies for optimisation of ultimate stope boundaries in underground mines. The proposed approaches for this purpose aim at maximizing the economic profit by selecting the best possible layout under operational, technical and physical constraints. In this paper, the existing three heuristic techniques including Floating Stope Algorithm, Maximum Value Algorithm and Mineable Shape Optimiser (MSO) are examined for optimisation of stope layout in a case study. Each technique is assessed in terms of applicability, algorithm capabilities and limitations considering the underground mine planning challenges. Finally, the results are evaluated and compared.

Study of application of ERTS-A imagery to fracture related mine safety hazards in the coal mining industry

NASA Technical Reports Server (NTRS)

Wier, C. E.; Wobber, F. J. (Principal Investigator); Russell, O. R.; Amato, R. V.

1973-01-01

The author has identified the following significant results. The utility of ERTS-1/high altitude aircraft imagery to detect underground mine hazards is strongly suggested. A 1:250,000 scale mined lands map of the Vincennes Quadrangle, Indiana has been prepared. This map is a prototype for a national mined lands inventory and will be distributed to State and Federal offices.

30 CFR 780.1 - Scope.

Code of Federal Regulations, 2010 CFR

2010-07-01

... RECLAMATION OPERATIONS PERMITS AND COAL EXPLORATION SYSTEMS UNDER REGULATORY PROGRAMS SURFACE MINING PERMIT APPLICATIONS-MINIMUM REQUIREMENTS FOR RECLAMATION AND OPERATION PLAN § 780.1 Scope. This part provides the... operations and reclamation plan portions of applications for permits for surface mining activities, except to...

Supporting Solar Physics Research via Data Mining

NASA Astrophysics Data System (ADS)

Angryk, Rafal; Banda, J.; Schuh, M.; Ganesan Pillai, K.; Tosun, H.; Martens, P.

2012-05-01

In this talk we will briefly introduce three pillars of data mining (i.e. frequent patterns discovery, classification, and clustering), and discuss some possible applications of known data mining techniques which can directly benefit solar physics research. In particular, we plan to demonstrate applicability of frequent patterns discovery methods for the verification of hypotheses about co-occurrence (in space and time) of filaments and sigmoids. We will also show how classification/machine learning algorithms can be utilized to verify human-created software modules to discover individual types of solar phenomena. Finally, we will discuss applicability of clustering techniques to image data processing.

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

Causes Of Low Efficiency Of Combined Ventilation System In Coal Mines In Resolving The Problem Of Air Leaks (Inflows) Between Levels And Surface

NASA Astrophysics Data System (ADS)

Popov, Valeriy; Filatov, Yuriy; Lee, Hee; Golik, Anatoliy

2017-11-01

The paper discusses the problem of the underground mining safety control. The long-term air intake to coal accumulations is reviewed as one of the reasons of endogenous fires during mining. The methods of combating air leaks (inflows) in order to prevent endogenous fires are analyzed. The calculations showing the discrepancy between the design calculations for the mine ventilation, disregarding a number of mining-andgeological and mining-engineering factors, and the actual conditions of mining are given. It is proved that the conversion of operating mines to combined (pressure and exhaust) ventilation system in order to reduce the endogenous fire hazard of underground mining is unreasonable due to impossibility of providing an optimal distribution of aerodynamic pressure in mines. The conversion does not exclude the entry of air into potentially hazardous zones of endogenous fires. The essence of the combined application of positive and negative control methods for the distribution of air pressure is revealed. It consists of air doors installation in easily ventilated airways and installation of pressure equalization chambers equipped with auxiliary fans near the stoppings, working sections and in parallel airways.The effectiveness of the combined application of negative and positive control methods for the air pressure distribution in order to reduce endogenous fire hazard of mining operations is proved.

Identification of candidate genes in Populus cell wall biosynthesis using text-mining, co-expression network and comparative genomics

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

Yang, Xiaohan; Ye, Chuyu; Bisaria, Anjali

2011-01-01

Populus is an important bioenergy crop for bioethanol production. A greater understanding of cell wall biosynthesis processes is critical in reducing biomass recalcitrance, a major hindrance in efficient generation of ethanol from lignocellulosic biomass. Here, we report the identification of candidate cell wall biosynthesis genes through the development and application of a novel bioinformatics pipeline. As a first step, via text-mining of PubMed publications, we obtained 121 Arabidopsis genes that had the experimental evidences supporting their involvement in cell wall biosynthesis or remodeling. The 121 genes were then used as bait genes to query an Arabidopsis co-expression database and additionalmore » genes were identified as neighbors of the bait genes in the network, increasing the number of genes to 548. The 548 Arabidopsis genes were then used to re-query the Arabidopsis co-expression database and re-construct a network that captured additional network neighbors, expanding to a total of 694 genes. The 694 Arabidopsis genes were computationally divided into 22 clusters. Queries of the Populus genome using the Arabidopsis genes revealed 817 Populus orthologs. Functional analysis of gene ontology and tissue-specific gene expression indicated that these Arabidopsis and Populus genes are high likelihood candidates for functional genomics in relation to cell wall biosynthesis.« less

Data Visualization in Information Retrieval and Data Mining (SIG VIS).

ERIC Educational Resources Information Center

Efthimiadis, Efthimis

2000-01-01

Presents abstracts that discuss using data visualization for information retrieval and data mining, including immersive information space and spatial metaphors; spatial data using multi-dimensional matrices with maps; TREC (Text Retrieval Conference) experiments; users' information needs in cartographic information retrieval; and users' relevance…

Enhancements for a Dynamic Data Warehousing and Mining System for Large-Scale Human Social Cultural Behavioral (HSBC) Data

DTIC Science & Technology

2016-09-26

Intelligent Automation Incorporated Enhancements for a Dynamic Data Warehousing and Mining ...Enhancements for a Dynamic Data Warehousing and Mining System for N00014-16-P-3014 Large-Scale Human Social Cultural Behavioral (HSBC) Data 5b. GRANT NUMBER...Representative Media Gallery View. We perform Scraawl’s NER algorithm to the text associated with YouTube post, which classifies the named entities into

Detecting Malicious Tweets in Twitter Using Runtime Monitoring With Hidden Information

DTIC Science & Technology

2016-06-01

text mining using Twitter streaming API and python [Online]. Available: http://adilmoujahid.com/posts/2014/07/twitter-analytics/ [22] M. Singh, B...sites with 645,750,000 registered users [3] and has open source public tweets for data mining . 2. Malicious Users and Tweets In the modern world...want to data mine in Twitter, and presents the natural language assertions and corresponding rule patterns. It then describes the steps performed using

Numerical linear algebra in data mining

NASA Astrophysics Data System (ADS)

Eldén, Lars

Ideas and algorithms from numerical linear algebra are important in several areas of data mining. We give an overview of linear algebra methods in text mining (information retrieval), pattern recognition (classification of handwritten digits), and PageRank computations for web search engines. The emphasis is on rank reduction as a method of extracting information from a data matrix, low-rank approximation of matrices using the singular value decomposition and clustering, and on eigenvalue methods for network analysis.

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

Towards the Geospatial Web: Media Platforms for Managing Geotagged Knowledge Repositories

NASA Astrophysics Data System (ADS)

Scharl, Arno

International media have recognized the visual appeal of geo-browsers such as NASA World Wind and Google Earth, for example, when Web and television coverage on Hurricane Katrina used interactive geospatial projections to illustrate its path and the scale of destruction in August 2005. Yet these early applications only hint at the true potential of geospatial technology to build and maintain virtual communities and to revolutionize the production, distribution and consumption of media products. This chapter investigates this potential by reviewing the literature and discussing the integration of geospatial and semantic reference systems, with an emphasis on extracting geospatial context from unstructured text. A content analysis of news coverage based on a suite of text mining tools (webLyzard) sheds light on the popularity and adoption of geospatial platforms.

Building an efficient curation workflow for the Arabidopsis literature corpus

PubMed Central

Li, Donghui; Berardini, Tanya Z.; Muller, Robert J.; Huala, Eva

2012-01-01

TAIR (The Arabidopsis Information Resource) is the model organism database (MOD) for Arabidopsis thaliana, a model plant with a literature corpus of about 39 000 articles in PubMed, with over 4300 new articles added in 2011. We have developed a literature curation workflow incorporating both automated and manual elements to cope with this flood of new research articles. The current workflow can be divided into two phases: article selection and curation. Structured controlled vocabularies, such as the Gene Ontology and Plant Ontology are used to capture free text information in the literature as succinct ontology-based annotations suitable for the application of computational analysis methods. We also describe our curation platform and the use of text mining tools in our workflow. Database URL: www.arabidopsis.org PMID:23221298

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.

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

Bacterial populations within copper mine tailings: long-term effects of amendment with Class A biosolids

USDA-ARS?s Scientific Manuscript database

This study evaluates the effect of surface application of dried Class A biosolids on microbial populations within copper mine tailings. Methods and Results: Mine tailing sites were established at ASARCO Mission Mine close to Sahuarita, Arizona. Site 1 (Dec. 1998) was amended with 248 tons ha-1 of C...

Applying Web Usage Mining for Personalizing Hyperlinks in Web-Based Adaptive Educational Systems

ERIC Educational Resources Information Center

Romero, Cristobal; Ventura, Sebastian; Zafra, Amelia; de Bra, Paul

2009-01-01

Nowadays, the application of Web mining techniques in e-learning and Web-based adaptive educational systems is increasing exponentially. In this paper, we propose an advanced architecture for a personalization system to facilitate Web mining. A specific Web mining tool is developed and a recommender engine is integrated into the AHA! system in…

Safety Psychology Applicating on Coal Mine Safety Management Based on Information System

NASA Astrophysics Data System (ADS)

Hou, Baoyue; Chen, Fei

In recent years, with the increase of intensity of coal mining, a great number of major accidents happen frequently, the reason mostly due to human factors, but human's unsafely behavior are affected by insecurity mental control. In order to reduce accidents, and to improve safety management, with the help of application security psychology, we analyse the cause of insecurity psychological factors from human perception, from personality development, from motivation incentive, from reward and punishment mechanism, and from security aspects of mental training , and put forward countermeasures to promote coal mine safety production,and to provide information for coal mining to improve the level of safety management.

The Application of LANDSAT Multi-Temporal Thermal Infrared Data to Identify Coal Fire in the Khanh Hoa Coal Mine, Thai Nguyen province, Vietnam

NASA Astrophysics Data System (ADS)

Trinh, Le Hung; Zablotskii, V. R.

2017-12-01

The Khanh Hoa coal mine is a surface coal mine in the Thai Nguyen province, which is one of the largest deposits of coal in the Vietnam. Numerous reasons such as improper mining techniques and policy, as well as unauthorized mining caused surface and subsurface coal fire in this area. Coal fire is a dangerous phenomenon which affects the environment seriously by releasing toxic fumes which causes forest fires, and subsidence of infrastructure surface. This article presents study on the application of LANDSAT multi-temporal thermal infrared images, which help to detect coal fire. The results obtained in this study can be used to monitor fire zones so as to give warnings and solutions to prevent coal fire.

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

30 CFR 795.12 - Applicant liability.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Mineral Resources OFFICE OF SURFACE MINING RECLAMATION AND ENFORCEMENT, DEPARTMENT OF THE INTERIOR SMALL OPERATOR ASSISTANCE PERMANENT REGULATORY PROGRAM-SMALL OPERATOR ASSISTANCE PROGRAM § 795.12 Applicant... 12 months immediately following the date on which the operator is issued the surface coal mining and...

Report: Congressionally Requested Information on the Status and Length of Review for Appalachian Surface Mining Permit Applications

EPA Pesticide Factsheets

Report #12-P-0083, November 21, 2011. After reconciling discrepancies and vetting information, we identified 185 surface mining permit applications to review from the list of 237 that we received from the senator.

30 CFR 15.4 - Application procedures and requirements.

Code of Federal Regulations, 2012 CFR

2012-07-01

..., Mine Safety and Health Administration, Approval and Certification Center, 765 Technology Drive... 30 Mineral Resources 1 2012-07-01 2012-07-01 false Application procedures and requirements. 15.4 Section 15.4 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR TESTING...

30 CFR 778.15 - Right-of-entry information.

Code of Federal Regulations, 2010 CFR

2010-07-01

... SURFACE COAL MINING AND RECLAMATION OPERATIONS PERMITS AND COAL EXPLORATION SYSTEMS UNDER REGULATORY PROGRAMS PERMIT APPLICATIONS-MINIMUM REQUIREMENTS FOR LEGAL, FINANCIAL, COMPLIANCE, AND RELATED INFORMATION... upon which the applicant bases his legal right to enter and begin surface coal mining and reclamation...

Database Constraints Applied to Metabolic Pathway Reconstruction Tools

PubMed Central

Vilaplana, Jordi; Solsona, Francesc; Teixido, Ivan; Usié, Anabel; Karathia, Hiren; Alves, Rui; Mateo, Jordi

2014-01-01

Our group developed two biological applications, Biblio-MetReS and Homol-MetReS, accessing the same database of organisms with annotated genes. Biblio-MetReS is a data-mining application that facilitates the reconstruction of molecular networks based on automated text-mining analysis of published scientific literature. Homol-MetReS allows functional (re)annotation of proteomes, to properly identify both the individual proteins involved in the process(es) of interest and their function. It also enables the sets of proteins involved in the process(es) in different organisms to be compared directly. The efficiency of these biological applications is directly related to the design of the shared database. We classified and analyzed the different kinds of access to the database. Based on this study, we tried to adjust and tune the configurable parameters of the database server to reach the best performance of the communication data link to/from the database system. Different database technologies were analyzed. We started the study with a public relational SQL database, MySQL. Then, the same database was implemented by a MapReduce-based database named HBase. The results indicated that the standard configuration of MySQL gives an acceptable performance for low or medium size databases. Nevertheless, tuning database parameters can greatly improve the performance and lead to very competitive runtimes. PMID:25202745

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.

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.

Exploratory analysis of textual data from the Mother and Child Handbook using a text mining method (II): Monthly changes in the words recorded by mothers.

PubMed

Tagawa, Miki; Matsuda, Yoshio; Manaka, Tomoko; Kobayashi, Makiko; Ohwada, Michitaka; Matsubara, Shigeki

2017-01-01

The aim of the study was to examine the possibility of converting subjective textual data written in the free column space of the Mother and Child Handbook (MCH) into objective information using text mining and to compare any monthly changes in the words written by the mothers. Pregnant women without complications (n = 60) were divided into two groups according to State-Trait Anxiety Inventory grade: low trait anxiety (group I, n = 39) and high trait anxiety (group II, n = 21). Exploratory analysis of the textual data from the MCH was conducted by text mining using the Word Miner software program. Using 1203 structural elements extracted after processing, a comparison of monthly changes in the words used in the mothers' comments was made between the two groups. The data was mainly analyzed by a correspondence analysis. The structural elements in groups I and II were divided into seven and six clusters, respectively, by cluster analysis. Correspondence analysis revealed clear monthly changes in the words used in the mothers' comments as the pregnancy progressed in group I, whereas the association was not clear in group II. The text mining method was useful for exploratory analysis of the textual data obtained from pregnant women, and the monthly change in the words used in the mothers' comments as pregnancy progressed differed according to their degree of unease. © 2016 Japan Society of Obstetrics and Gynecology.

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.

43 CFR 3861.1 - Surveys of mining claims.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 43 Public Lands: Interior 2 2011-10-01 2011-10-01 false Surveys of mining claims. 3861.1 Section... MANAGEMENT, DEPARTMENT OF THE INTERIOR MINERALS MANAGEMENT (3000) MINERAL PATENT APPLICATIONS Surveys and Plats § 3861.1 Surveys of mining claims. ...

43 CFR 3861.1 - Surveys of mining claims.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 43 Public Lands: Interior 2 2013-10-01 2013-10-01 false Surveys of mining claims. 3861.1 Section... MANAGEMENT, DEPARTMENT OF THE INTERIOR MINERALS MANAGEMENT (3000) MINERAL PATENT APPLICATIONS Surveys and Plats § 3861.1 Surveys of mining claims. ...

43 CFR 3861.1 - Surveys of mining claims.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 43 Public Lands: Interior 2 2014-10-01 2014-10-01 false Surveys of mining claims. 3861.1 Section... MANAGEMENT, DEPARTMENT OF THE INTERIOR MINERALS MANAGEMENT (3000) MINERAL PATENT APPLICATIONS Surveys and Plats § 3861.1 Surveys of mining claims. ...

30 CFR 779.1 - Scope.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 3 2011-07-01 2011-07-01 false Scope. 779.1 Section 779.1 Mineral Resources OFFICE OF SURFACE MINING RECLAMATION AND ENFORCEMENT, DEPARTMENT OF THE INTERIOR SURFACE COAL MINING AND... environmental resources contents of applications for surface mining activities. ...

30 CFR 779.1 - Scope.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 3 2010-07-01 2010-07-01 false Scope. 779.1 Section 779.1 Mineral Resources OFFICE OF SURFACE MINING RECLAMATION AND ENFORCEMENT, DEPARTMENT OF THE INTERIOR SURFACE COAL MINING AND... environmental resources contents of applications for surface mining activities. ...

Mining Health-Related Issues in Consumer Product Reviews by Using Scalable Text Analytics

PubMed Central

Torii, Manabu; Tilak, Sameer S.; Doan, Son; Zisook, Daniel S.; Fan, Jung-wei

2016-01-01

In an era when most of our life activities are digitized and recorded, opportunities abound to gain insights about population health. Online product reviews present a unique data source that is currently underexplored. Health-related information, although scarce, can be systematically mined in online product reviews. Leveraging natural language processing and machine learning tools, we were able to mine 1.3 million grocery product reviews for health-related information. The objectives of the study were as follows: (1) conduct quantitative and qualitative analysis on the types of health issues found in consumer product reviews; (2) develop a machine learning classifier to detect reviews that contain health-related issues; and (3) gain insights about the task characteristics and challenges for text analytics to guide future research. PMID:27375358

Mining Health-Related Issues in Consumer Product Reviews by Using Scalable Text Analytics.

PubMed

Torii, Manabu; Tilak, Sameer S; Doan, Son; Zisook, Daniel S; Fan, Jung-Wei

2016-01-01

In an era when most of our life activities are digitized and recorded, opportunities abound to gain insights about population health. Online product reviews present a unique data source that is currently underexplored. Health-related information, although scarce, can be systematically mined in online product reviews. Leveraging natural language processing and machine learning tools, we were able to mine 1.3 million grocery product reviews for health-related information. The objectives of the study were as follows: (1) conduct quantitative and qualitative analysis on the types of health issues found in consumer product reviews; (2) develop a machine learning classifier to detect reviews that contain health-related issues; and (3) gain insights about the task characteristics and challenges for text analytics to guide future research.

30 CFR 900.15 - Federal lands program cooperative agreements.

Code of Federal Regulations, 2010 CFR

2010-07-01

.... 900.15 Section 900.15 Mineral Resources OFFICE OF SURFACE MINING RECLAMATION AND ENFORCEMENT, DEPARTMENT OF THE INTERIOR PROGRAMS FOR THE CONDUCT OF SURFACE MINING OPERATIONS WITHIN EACH STATE INTRODUCTION § 900.15 Federal lands program cooperative agreements. The full text of any State and Federal...

Literature Mining Methods for Toxicology and Construction of ...

EPA Pesticide Factsheets

Webinar Presentation on text-mining methodologies in use at NCCT and how they can be used to assist with the OECD Retinoid project. Presentation to 1st Workshop/Scientific Expert Group meeting on the OECD Retinoid Project - April 26, 2016 –Brussels, Presented remotely via web.