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1. Coulet A, Garten Y, Dumontier M, Altman RB, Musen MA, Shah NH: Integration and publication of heterogeneous text-mined relationships on the Semantic Web. J Biomed Semantics; 2011 May 17;2 Suppl 2:S10
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Integration and publication of heterogeneous text-mined relationships on the Semantic Web.
  • BACKGROUND: Advances in Natural Language Processing (NLP) techniques enable the extraction of fine-grained relationships mentioned in biomedical text.
  • The variability and the complexity of natural language in expressing similar relationships causes the extracted relationships to be highly heterogeneous, which makes the construction of knowledge bases difficult and poses a challenge in using these for data mining or question answering.
  • RESULTS: We report on the semi-automatic construction of the PHARE relationship ontology (the PHArmacogenomic RElationships Ontology) consisting of 200 curated relations from over 40,000 heterogeneous relationships extracted via text-mining.
  • These heterogeneous relations are then mapped to the PHARE ontology using synonyms, entity descriptions and hierarchies of entities and roles.
  • Once mapped, relationships can be normalized and compared using the structure of the ontology to identify relationships that have similar semantics but different syntax.
  • We compare and contrast the manual procedure with a fully automated approach using WordNet to quantify the degree of integration enabled by iterative curation and refinement of the PHARE ontology.
  • The result of such integration is a repository of normalized biomedical relationships, named PHARE-KB, which can be queried using Semantic Web technologies such as SPARQL and can be visualized in the form of a biological network.
  • CONCLUSIONS: The PHARE ontology serves as a common semantic framework to integrate more than 40,000 relationships pertinent to pharmacogenomics.
  • The PHARE ontology forms the foundation of a knowledge base named PHARE-KB.
  • Once populated with relationships, PHARE-KB (i) can be visualized in the form of a biological network to guide human tasks such as database curation and (ii) can be queried programmatically to guide bioinformatics applications such as the prediction of molecular interactions.
  • PHARE is available at http://purl.bioontology.org/ontology/PHARE.

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  • (PMID = 21624156.001).
  • [ISSN] 2041-1480
  • [Journal-full-title] Journal of biomedical semantics
  • [ISO-abbreviation] J Biomed Semantics
  • [Language] eng
  • [Grant] United States / NHGRI NIH HHS / HG / U54 HG004028
  • [Publication-type] Journal Article
  • [Publication-country] England
  • [Other-IDs] NLM/ PMC3102890
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2. Fernald GH, Capriotti E, Daneshjou R, Karczewski KJ, Altman RB: Bioinformatics challenges for personalized medicine. Bioinformatics; 2011 Jul 1;27(13):1741-8
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Bioinformatics challenges for personalized medicine.
  • MOTIVATION: Widespread availability of low-cost, full genome sequencing will introduce new challenges for bioinformatics.
  • RESULTS: This review outlines recent developments in sequencing technologies and genome analysis methods for application in personalized medicine.
  • New methods are needed in four areas to realize the potential of personalized medicine: (i) processing large-scale robust genomic data;.
  • (ii) interpreting the functional effect and the impact of genomic variation;.
  • (iii) integrating systems data to relate complex genetic interactions with phenotypes; and (iv) translating these discoveries into medical practice.
  • CONTACT: russ.altman@stanford.edu

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  • [ErratumIn] Bioinformatics. 2011 Aug 15;27(16):2323
  • (PMID = 21596790.001).
  • [ISSN] 1367-4811
  • [Journal-full-title] Bioinformatics (Oxford, England)
  • [ISO-abbreviation] Bioinformatics
  • [Language] ENG
  • [Grant] United States / NLM NIH HHS / LM / R01 LM005652; United States / NIGMS NIH HHS / GM / U01GM61374; United States / NLM NIH HHS / LM / LM05652; United States / NLM NIH HHS / LM / T15 LM007033; United States / NIGMS NIH HHS / GM / U01 GM061374; United States / NLM NIH HHS / LM / LM007033
  • [Publication-type] Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
  • [Publication-country] England
  • [Other-IDs] NLM/ PMC3117361
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3. 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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