Discovering drug–drug interactions: a text-mining and reasoning approach based on properties of drug metabolism
Identifieur interne : 000158 ( Ncbi/Merge ); 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
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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>
</div>
</front>
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<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Bioinformatics</journal-id>
<journal-id journal-id-type="publisher-id">bioinformatics</journal-id>
<journal-id journal-id-type="hwp">bioinfo</journal-id>
<journal-title-group><journal-title>Bioinformatics</journal-title>
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<issn pub-type="ppub">1367-4803</issn>
<issn pub-type="epub">1367-4811</issn>
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<article-meta><article-id pub-id-type="pmid">20823320</article-id>
<article-id pub-id-type="pmc">2935409</article-id>
<article-id pub-id-type="doi">10.1093/bioinformatics/btq382</article-id>
<article-id pub-id-type="publisher-id">btq382</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Eccb 2010 Conference Proceedings September 26 to September 29, 2010, Ghent, Belgium</subject>
<subj-group><subject>Original Papers</subject>
<subj-group><subject>Text Mining, Ontologies and Databases</subject>
</subj-group>
</subj-group>
</subj-group>
</article-categories>
<title-group><article-title>Discovering drug–drug interactions: a text-mining and reasoning approach based on properties of drug metabolism</article-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Tari</surname>
<given-names>Luis</given-names>
</name>
<xref ref-type="aff" rid="AFF1"><sup>1</sup>
</xref>
<xref ref-type="corresp" rid="COR1">*</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Anwar</surname>
<given-names>Saadat</given-names>
</name>
<xref ref-type="aff" rid="AFF1"><sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Liang</surname>
<given-names>Shanshan</given-names>
</name>
<xref ref-type="aff" rid="AFF1"><sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Cai</surname>
<given-names>James</given-names>
</name>
<xref ref-type="aff" rid="AFF1"><sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Baral</surname>
<given-names>Chitta</given-names>
</name>
<xref ref-type="aff" rid="AFF1"><sup>2</sup>
</xref>
</contrib>
</contrib-group>
<aff id="AFF1"><sup>1</sup>
Disease and Translational Informatics, Hoffmann-La Roche, Nutley, NJ 07110 and<sup>2</sup>
Department of Computer Science and Engineering, Arizona State University, Tempe, AZ 85287, USA</aff>
<author-notes><corresp id="COR1">* To whom correspondence should be addressed.</corresp>
</author-notes>
<pub-date pub-type="ppub"><day>15</day>
<month>9</month>
<year>2010</year>
</pub-date>
<pub-date pub-type="epub"><day>4</day>
<month>9</month>
<year>2010</year>
</pub-date>
<pub-date pub-type="pmc-release"><day>4</day>
<month>9</month>
<year>2010</year>
</pub-date>
<pmc-comment> PMC Release delay is 0 months and 0 days and was based on the
. </pmc-comment>
<volume>26</volume>
<issue>18</issue>
<fpage>i547</fpage>
<lpage>i553</lpage>
<permissions><copyright-statement>© The Author(s) 2010. Published by Oxford University Press.</copyright-statement>
<copyright-year>2010</copyright-year>
<license license-type="creative-commons" xlink:href="http://creativecommons.org/licenses/by-nc/2.0/uk/"><license-p><pmc-comment>CREATIVE COMMONS</pmc-comment>
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by-nc/2.5">http://creativecommons.org/licenses/by-nc/2.5</ext-link>
), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
</license>
</permissions>
<abstract><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>
</abstract>
</article-meta>
</front>
</pmc>
<affiliations><list><country><li>États-Unis</li>
</country>
<region><li>Arizona</li>
<li>New Jersey</li>
</region>
</list>
<tree><country name="États-Unis"><region name="New Jersey"><name sortKey="Tari, Luis" sort="Tari, Luis" uniqKey="Tari L" first="Luis" last="Tari">Luis Tari</name>
</region>
<name sortKey="Anwar, Saadat" sort="Anwar, Saadat" uniqKey="Anwar S" first="Saadat" last="Anwar">Saadat Anwar</name>
<name sortKey="Baral, Chitta" sort="Baral, Chitta" uniqKey="Baral C" first="Chitta" last="Baral">Chitta Baral</name>
<name sortKey="Cai, James" sort="Cai, James" uniqKey="Cai J" first="James" last="Cai">James Cai</name>
<name sortKey="Liang, Shanshan" sort="Liang, Shanshan" uniqKey="Liang S" first="Shanshan" last="Liang">Shanshan Liang</name>
</country>
</tree>
</affiliations>
</record>
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