Discovering drug–drug interactions: a text-mining and reasoning approach based on properties of drug metabolism
Identifieur interne : 000158 ( Ncbi/Checkpoint ); précédent : 000157; suivant : 000159Discovering drug–drug interactions: a text-mining and reasoning approach based on properties of drug metabolism
Auteurs : Luis Tari [États-Unis] ; Saadat Anwar [États-Unis] ; Shanshan Liang [États-Unis] ; James Cai [États-Unis] ; Chitta Baral [États-Unis]Source :
- Bioinformatics [ 1367-4803 ] ; 2010.
Abstract
Url:
DOI: 10.1093/bioinformatics/btq382
PubMed: 20823320
PubMed Central: 2935409
Affiliations:
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Discovering drug–drug interactions: a text-mining and reasoning approach based on properties of drug metabolism</title>
<author><name sortKey="Tari, Luis" sort="Tari, Luis" uniqKey="Tari L" first="Luis" last="Tari">Luis Tari</name>
<affiliation wicri:level="2"><nlm:aff wicri:cut=" and" id="AFF1">Disease and Translational Informatics, Hoffmann-La Roche, Nutley, NJ 07110</nlm:aff>
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<author><name sortKey="Anwar, Saadat" sort="Anwar, Saadat" uniqKey="Anwar S" first="Saadat" last="Anwar">Saadat Anwar</name>
<affiliation wicri:level="2"><nlm:aff id="AFF1">Department of Computer Science and Engineering, Arizona State University, Tempe, AZ 85287, USA</nlm:aff>
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<author><name sortKey="Liang, Shanshan" sort="Liang, Shanshan" uniqKey="Liang S" first="Shanshan" last="Liang">Shanshan Liang</name>
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<author><name sortKey="Cai, James" sort="Cai, James" uniqKey="Cai J" first="James" last="Cai">James Cai</name>
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<author><name sortKey="Baral, Chitta" sort="Baral, Chitta" uniqKey="Baral C" first="Chitta" last="Baral">Chitta Baral</name>
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<series><title level="j">Bioinformatics</title>
<idno type="ISSN">1367-4803</idno>
<idno type="eISSN">1367-4811</idno>
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<front><div type="abstract" xml:lang="en"><p><bold>Motivation:</bold>
Identifying drug–drug interactions (DDIs) is a critical process in drug administration and drug development. Clinical support tools often provide comprehensive lists of DDIs, but they usually lack the supporting scientific evidences and different tools can return inconsistent results. In this article, we propose a novel approach that integrates text mining and automated reasoning to derive DDIs. Through the extraction of various facts of drug metabolism, not only the DDIs that are explicitly mentioned in text can be extracted but also the potential interactions that can be inferred by reasoning.</p>
<p><bold>Results:</bold>
Our approach was able to find several potential DDIs that are not present in DrugBank. We manually evaluated these interactions based on their supporting evidences, and our analysis revealed that 81.3% of these interactions are determined to be correct. This suggests that our approach can uncover potential DDIs with scientific evidences explaining the mechanism of the interactions.</p>
<p><bold>Contact:</bold>
<email>luis.tari@roche.com</email>
</p>
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