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Items 1 to 9 of about 9
1. Rodriguez-Esteban R: Biomedical text mining and its applications. PLoS Comput Biol; 2009 Dec;5(12):e1000597
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Biomedical text mining and its applications.
  • [MeSH-major] Biomedical Research / methods. Data Mining / methods. Internet. Natural Language Processing. Search Engine / methods. Software

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  • (PMID = 20041219.001).
  • [ISSN] 1553-7358
  • [Journal-full-title] PLoS computational biology
  • [ISO-abbreviation] PLoS Comput. Biol.
  • [Language] eng
  • [Publication-type] Journal Article
  • [Publication-country] United States
  • [Other-IDs] NLM/ PMC2791166
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2. Kocbek S, Sætre R, Stiglic G, Kim JD, Pernek I, Tsuruoka Y, Kokol P, Ananiadou S, Tsujii J: AGRA: analysis of gene ranking algorithms. Bioinformatics; 2011 Apr 15;27(8):1185-6
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] AGRA: analysis of gene ranking algorithms.
  • Often, the most informative genes have to be selected from different gene sets and several computer gene ranking algorithms have been developed to cope with the problem.
  • To help researchers decide which algorithm to use, we developed the analysis of gene ranking algorithms (AGRA) system that offers a novel technique for comparing ranked lists of genes.
  • The most important feature of AGRA is that no previous knowledge of gene ranking algorithms is needed for their comparison.
  • Using the text mining system finding-associated concepts with text analysis.
  • AGRA defines what we call biomedical concept space (BCS) for each gene list and offers a comparison of the gene lists in six different BCS categories.
  • The uploaded gene lists can be compared using two different methods.
  • In the first method, the overlap between each pair of two gene lists of BCSs is calculated.
  • The second method offers a text field where a specific biomedical concept can be entered.
  • AGRA searches for this concept in each gene lists' BCS, highlights the rank of the concept and offers a visual representation of concepts ranked above and below it.
  • AVAILABILITY AND IMPLEMENTATION: Available at http://agra.fzv.uni-mb.si/, implemented in Java and running on the Glassfish server.
  • CONTACT: simon.kocbek@uni-mb.si.
  • [MeSH-major] Algorithms. Genes
  • [MeSH-minor] Data Mining. Software

  • MedlinePlus Health Information. consumer health - Genes and Gene Therapy.
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  • (PMID = 21349873.001).
  • [ISSN] 1367-4811
  • [Journal-full-title] Bioinformatics (Oxford, England)
  • [ISO-abbreviation] Bioinformatics
  • [Language] eng
  • [Grant] United Kingdom / Biotechnology and Biological Sciences Research Council / / BB/E004431/1; United Kingdom / Biotechnology and Biological Sciences Research Council / / BB/G013160/1
  • [Publication-type] Journal Article; Research Support, Non-U.S. Gov't
  • [Publication-country] England
  • [Other-IDs] NLM/ PMC3072556
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3. Li J, Zhu X, Chen JY: Building disease-specific drug-protein connectivity maps from molecular interaction networks and PubMed abstracts. PLoS Comput Biol; 2009 Jul;5(7):e1000450
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  • [Title] Building disease-specific drug-protein connectivity maps from molecular interaction networks and PubMed abstracts.
  • The recently proposed concept of molecular connectivity maps enables researchers to integrate experimental measurements of genes, proteins, metabolites, and drug compounds under similar biological conditions.
  • The study of these maps provides opportunities for future toxicogenomics and drug discovery applications.
  • We developed a computational framework to build disease-specific drug-protein connectivity maps.
  • We integrated gene/protein and drug connectivity information based on protein interaction networks and literature mining, without requiring gene expression profile information derived from drug perturbation experiments on disease samples.
  • We described the development and application of this computational framework using Alzheimer's Disease (AD) as a primary example in three steps.
  • First, molecular interaction networks were incorporated to reduce bias and improve relevance of AD seed proteins.
  • Second, PubMed abstracts were used to retrieve enriched drug terms that are indirectly associated with AD through molecular mechanistic studies.
  • Third and lastly, a comprehensive AD connectivity map was created by relating enriched drugs and related proteins in literature.
  • We showed that this molecular connectivity map development approach outperformed both curated drug target databases and conventional information retrieval systems.
  • Our initial explorations of the AD connectivity map yielded a new hypothesis that diltiazem and quinidine may be investigated as candidate drugs for AD treatment.
  • Molecular connectivity maps derived computationally can help study molecular signature differences between different classes of drugs in specific disease contexts.
  • To achieve overall good data coverage and quality, a series of statistical methods have been developed to overcome high levels of data noise in biological networks and literature mining results.
  • Further development of computational molecular connectivity maps to cover major disease areas will likely set up a new model for drug development, in which therapeutic/toxicological profiles of candidate drugs can be checked computationally before costly clinical trials begin.
  • [MeSH-major] Databases, Protein. Genomics / methods. Pharmaceutical Preparations / chemistry. Pharmacology. Proteins / chemistry. PubMed. Systems Biology / methods
  • [MeSH-minor] Algorithms. Alzheimer Disease / drug therapy. Animals. Cluster Analysis. Drug Therapy. Genes / drug effects. Humans. ROC Curve. Reproducibility of Results. Sensitivity and Specificity


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4. Tsuruoka Y, Miwa M, Hamamoto K, Tsujii J, Ananiadou S: Discovering and visualizing indirect associations between biomedical concepts. Bioinformatics; 2011 Jul 1;27(13):i111-9
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Discovering and visualizing indirect associations between biomedical concepts.
  • 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.

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  • (PMID = 21685059.001).
  • [ISSN] 1367-4811
  • [Journal-full-title] Bioinformatics (Oxford, England)
  • [ISO-abbreviation] Bioinformatics
  • [Language] ENG
  • [Grant] United Kingdom / Biotechnology and Biological Sciences Research Council / / BB/G013160/1
  • [Publication-type] Journal Article; Research Support, Non-U.S. Gov't
  • [Publication-country] England
  • [Other-IDs] NLM/ PMC3117364
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5. Fontaine JF, Priller F, Barbosa-Silva A, Andrade-Navarro MA: Génie: literature-based gene prioritization at multi genomic scale. Nucleic Acids Res; 2011 Jul;39(Web Server issue):W455-61
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  • [Title] Génie: literature-based gene prioritization at multi genomic scale.
  • Biomedical literature is traditionally used as a way to inform scientists of the relevance of genes in relation to a research topic.
  • However many genes, especially from poorly studied organisms, are not discussed in the literature.
  • Moreover, a manual and comprehensive summarization of the literature attached to the genes of an organism is in general impossible due to the high number of genes and abstracts involved.
  • We introduce the novel Génie algorithm that overcomes these problems by evaluating the literature attached to all genes in a genome and to their orthologs according to a selected topic.
  • Génie showed high precision (up to 100%) and the best performance in comparison to other algorithms in most of the benchmarks, especially when high sensitivity was required.
  • Moreover, the prioritization of zebrafish genes involved in heart development, using human and mouse orthologs, showed high enrichment in differentially expressed genes from microarray experiments.
  • The Génie web server supports hundreds of species, millions of genes and offers novel functionalities.
  • Common run times below a minute, even when analyzing the human genome with hundreds of thousands of literature records, allows the use of Génie in routine lab work.
  • AVAILABILITY: http://cbdm.mdc-berlin.de/tools/genie/.
  • [MeSH-major] Genes. Software
  • [MeSH-minor] Algorithms. Animals. Gene Expression Profiling. Genomics. Heart / embryology. Humans. Internet. MEDLINE. Mice. Models, Animal. Zebrafish / embryology. Zebrafish / genetics

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  • (PMID = 21609954.001).
  • [ISSN] 1362-4962
  • [Journal-full-title] Nucleic acids research
  • [ISO-abbreviation] Nucleic Acids Res.
  • [Language] eng
  • [Publication-type] Journal Article; Research Support, Non-U.S. Gov't
  • [Publication-country] England
  • [Other-IDs] NLM/ PMC3125729
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6. Matos S, Arrais JP, Maia-Rodrigues J, Oliveira JL: Concept-based query expansion for retrieving gene related publications from MEDLINE. BMC Bioinformatics; 2010;11:212
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Concept-based query expansion for retrieving gene related publications from MEDLINE.
  • BACKGROUND: Advances in biotechnology and in high-throughput methods for gene analysis have contributed to an exponential increase in the number of scientific publications in these fields of study.
  • While much of the data and results described in these articles are entered and annotated in the various existing biomedical databases, the scientific literature is still the major source of information.
  • There is, therefore, a growing need for text mining and information retrieval tools to help researchers find the relevant articles for their study.
  • To tackle this, several tools have been proposed to provide alternative solutions for specific user requests.
  • RESULTS: This paper presents QuExT, a new PubMed-based document retrieval and prioritization tool that, from a given list of genes, searches for the most relevant results from the literature.
  • QuExT follows a concept-oriented query expansion methodology to find documents containing concepts related to the genes in the user input, such as protein and pathway names.
  • The retrieved documents are ranked according to user-definable weights assigned to each concept class.
  • By changing these weights, users can modify the ranking of the results in order to focus on documents dealing with a specific concept.
  • The method's performance was evaluated using data from the 2004 TREC genomics track, producing a mean average precision of 0.425, with an average of 4.8 and 31.3 relevant documents within the top 10 and 100 retrieved abstracts, respectively.
  • CONCLUSIONS: QuExT implements a concept-based query expansion scheme that leverages gene-related information available on a variety of biological resources.
  • The main advantage of the system is to give the user control over the ranking of the results by means of a simple weighting scheme.
  • Using this approach, researchers can effortlessly explore the literature regarding a group of genes and focus on the different aspects relating to these genes.
  • [MeSH-major] Genomics / methods. Information Storage and Retrieval / methods. MEDLINE. PubMed
  • [MeSH-minor] Abstracting and Indexing as Topic. Data Mining. Databases, Factual. Genes. Publications. United States

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  • (PMID = 20426836.001).
  • [ISSN] 1471-2105
  • [Journal-full-title] BMC bioinformatics
  • [ISO-abbreviation] BMC Bioinformatics
  • [Language] eng
  • [Publication-type] Journal Article; Research Support, Non-U.S. Gov't
  • [Publication-country] England
  • [Other-IDs] NLM/ PMC2873540
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7. Handcock J, Deutsch EW, Boyle J: mspecLINE: bridging knowledge of human disease with the proteome. BMC Med Genomics; 2010 Mar 10;3:7
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] mspecLINE: bridging knowledge of human disease with the proteome.
  • BACKGROUND: Public proteomics databases such as PeptideAtlas contain peptides and proteins identified in mass spectrometry experiments.
  • However, these databases lack information about human disease for researchers studying disease-related proteins.
  • We have developed mspecLINE, a tool that combines knowledge about human disease in MEDLINE with empirical data about the detectable human proteome in PeptideAtlas. mspecLINE associates diseases with proteins by calculating the semantic distance between annotated terms from a controlled biomedical vocabulary.
  • We used an established semantic distance measure that is based on the co-occurrence of disease and protein terms in the MEDLINE bibliographic database.
  • RESULTS: The mspecLINE web application allows researchers to explore relationships between human diseases and parts of the proteome that are detectable using a mass spectrometer.
  • Given a disease, the tool will display proteins and peptides from PeptideAtlas that may be associated with the disease.
  • It will also display relevant literature from MEDLINE.
  • Furthermore, mspecLINE allows researchers to select proteotypic peptides for specific protein targets in a mass spectrometry assay.
  • CONCLUSIONS: Although mspecLINE applies an information retrieval technique to the MEDLINE database, it is distinct from previous MEDLINE query tools in that it combines the knowledge expressed in scientific literature with empirical proteomics data.
  • The tool provides valuable information about candidate protein targets to researchers studying human disease and is freely available on a public web server.

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  • (PMID = 20219133.001).
  • [ISSN] 1755-8794
  • [Journal-full-title] BMC medical genomics
  • [ISO-abbreviation] BMC Med Genomics
  • [Language] ENG
  • [Grant] United States / NCI NIH HHS / CA / R01 CA137442; United States / NCI NIH HHS / CA / 1R011CA1374422
  • [Publication-type] Journal Article; Research Support, N.I.H., Extramural
  • [Publication-country] England
  • [Chemical-registry-number] 0 / Peptides; 0 / Proteins; 0 / Proteome
  • [Other-IDs] NLM/ PMC2845087
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8. Garten Y, Coulet A, Altman RB: Recent progress in automatically extracting information from the pharmacogenomic literature. Pharmacogenomics; 2010 Oct;11(10):1467-89
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Recent progress in automatically extracting information from the pharmacogenomic literature.
  • 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.

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  • (PMID = 21047206.001).
  • [ISSN] 1744-8042
  • [Journal-full-title] Pharmacogenomics
  • [ISO-abbreviation] Pharmacogenomics
  • [Language] ENG
  • [Grant] United States / NIGMS NIH HHS / GM / R24 GM061374; United States / NLM NIH HHS / LM / LM07033; United States / NLM NIH HHS / LM / R01 LM005652; United States / NHGRI NIH HHS / HG / U54 HG004028; United States / NLM NIH HHS / LM / LM05652; United States / NIGMS NIH HHS / GM / GM061374-10S1; United States / NIGMS NIH HHS / GM / U01 GM061374-10S1; United States / NIGMS NIH HHS / GM / GM61374; United States / NLM NIH HHS / LM / T15 LM007033; United States / NIGMS NIH HHS / GM / U01 GM061374
  • [Publication-type] Journal Article; Research Support, N.I.H., Extramural; Review
  • [Publication-country] England
  • [Other-IDs] NLM/ NIHMS268321; NLM/ PMC3035632
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9. Fleuren WW, Verhoeven S, Frijters R, Heupers B, Polman J, van Schaik R, de Vlieg J, Alkema W: CoPub update: CoPub 5.0 a text mining system to answer biological questions. Nucleic Acids Res; 2011 Jul;39(Web Server issue):W450-4
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] CoPub update: CoPub 5.0 a text mining system to answer biological questions.
  • In this article, we present CoPub 5.0, a publicly available text mining system, which uses Medline abstracts to calculate robust statistics for keyword co-occurrences.
  • CoPub was initially developed for the analysis of microarray data, but we broadened the scope by implementing new technology and new thesauri.
  • In CoPub 5.0, we integrated existing CoPub technology with new features, and provided a new advanced interface, which can be used to answer a variety of biological questions.
  • CoPub 5.0 allows searching for keywords of interest and its relations to curated thesauri and provides highlighting and sorting mechanisms, using its statistics, to retrieve the most important abstracts in which the terms co-occur.
  • It also provides a way to search for indirect relations between genes, drugs, pathways and diseases, following an ABC principle, in which A and C have no direct connection but are connected via shared B intermediates.
  • With CoPub 5.0, it is possible to create, annotate and analyze networks using the layout and highlight options of Cytoscape web, allowing for literature based systems biology.
  • Finally, operations of the CoPub 5.0 Web service enable to implement the CoPub technology in bioinformatics workflows.
  • CoPub 5.0 can be accessed through the CoPub portal http://www.copub.org.
  • [MeSH-major] Data Mining / methods. Software
  • [MeSH-minor] Gene Regulatory Networks. Internet. PubMed

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  • (PMID = 21622961.001).
  • [ISSN] 1362-4962
  • [Journal-full-title] Nucleic acids research
  • [ISO-abbreviation] Nucleic Acids Res.
  • [Language] eng
  • [Publication-type] Journal Article; Research Support, Non-U.S. Gov't
  • [Publication-country] England
  • [Other-IDs] NLM/ PMC3125746
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