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Discovering drug–drug interactions: a text-mining and reasoning approach based on properties of drug metabolism

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Discovering 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.

RBID : PMC:2935409

Abstract

Motivation: 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.

Results: 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.

Contact: luis.tari@roche.com

Url:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2935409

DOI: 10.1093/bioinformatics/btq382
PubMed: 20823320
PubMed Central: 2935409

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Le document en format XML

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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>
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 <email>luis.tari@roche.com</email>
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Discovering drug–drug interactions: a text-mining and reasoning approach based on properties of drug metabolism

Discovering drug–drug interactions: a text-mining and reasoning approach based on properties of drug metabolism

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Abstract

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