WENDI: A tool for finding non-obvious relationships between compounds and biological properties, genes, diseases and scholarly publications
Identifieur interne : 000154 ( Ncbi/Merge ); précédent : 000153; suivant : 000155WENDI: A tool for finding non-obvious relationships between compounds and biological properties, genes, diseases and scholarly publications
Auteurs : Qian Zhu [États-Unis] ; Michael S. Lajiness [États-Unis] ; Ying Ding [États-Unis] ; David J. Wild [États-Unis]Source :
- Journal of Cheminformatics [ 1758-2946 ] ; 2010.
Abstract
In recent years, there has been a huge increase in the amount of publicly-available and proprietary information pertinent to drug discovery. However, there is a distinct lack of data mining tools available to harness this information, and in particular for knowledge discovery across multiple information sources. At Indiana University we have an ongoing project with Eli Lilly to develop web-service based tools for integrative mining of chemical and biological information. In this paper, we report on the first of these tools, called WENDI (Web Engine for Non-obvious Drug Information) that attempts to find non-obvious relationships between a query compound and scholarly publications, biological properties, genes and diseases using multiple information sources.
We have created an aggregate web service that takes a query compound as input, calls multiple web services for computation and database search, and returns an XML file that aggregates this information. We have also developed a client application that provides an easy-to-use interface to this web service. Both the service and client are publicly available.
Initial testing indicates this tool is useful in identifying potential biological applications of compounds that are not obvious, and in identifying corroborating and conflicting information from multiple sources. We encourage feedback on the tool to help us refine it further. We are now developing further tools based on this model.
Url:
DOI: 10.1186/1758-2946-2-6
PubMed: 20727184
PubMed Central: 2933596
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>In recent years, there has been a huge increase in the amount of publicly-available and proprietary information pertinent to drug discovery. However, there is a distinct lack of data mining tools available to harness this information, and in particular for knowledge discovery across multiple information sources. At Indiana University we have an ongoing project with Eli Lilly to develop web-service based tools for integrative mining of chemical and biological information. In this paper, we report on the first of these tools, called WENDI (Web Engine for Non-obvious Drug Information) that attempts to find non-obvious relationships between a query compound and scholarly publications, biological properties, genes and diseases using multiple information sources.</p>
</sec>
<sec><title>Results</title>
<p>We have created an aggregate web service that takes a query compound as input, calls multiple web services for computation and database search, and returns an XML file that aggregates this information. We have also developed a client application that provides an easy-to-use interface to this web service. Both the service and client are publicly available.</p>
</sec>
<sec><title>Conclusions</title>
<p>Initial testing indicates this tool is useful in identifying potential biological applications of compounds that are not obvious, and in identifying corroborating and conflicting information from multiple sources. We encourage feedback on the tool to help us refine it further. We are now developing further tools based on this model.</p>
</sec>
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<front><journal-meta><journal-id journal-id-type="nlm-ta">J Cheminform</journal-id>
<journal-title-group><journal-title>Journal of Cheminformatics</journal-title>
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<issn pub-type="epub">1758-2946</issn>
<publisher><publisher-name>BioMed Central</publisher-name>
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<article-id pub-id-type="pmc">2933596</article-id>
<article-id pub-id-type="publisher-id">1758-2946-2-6</article-id>
<article-id pub-id-type="doi">10.1186/1758-2946-2-6</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Software</subject>
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<title-group><article-title>WENDI: A tool for finding non-obvious relationships between compounds and biological properties, genes, diseases and scholarly publications</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" id="A1"><name><surname>Zhu</surname>
<given-names>Qian</given-names>
</name>
<xref ref-type="aff" rid="I1">1</xref>
<email>qianzhu@indiana.edu</email>
</contrib>
<contrib contrib-type="author" id="A2"><name><surname>Lajiness</surname>
<given-names>Michael S</given-names>
</name>
<xref ref-type="aff" rid="I2">2</xref>
<email>lajiness_michael_s@lilly.com</email>
</contrib>
<contrib contrib-type="author" id="A3"><name><surname>Ding</surname>
<given-names>Ying</given-names>
</name>
<xref ref-type="aff" rid="I3">3</xref>
<email>dingying@indiana.edu</email>
</contrib>
<contrib contrib-type="author" corresp="yes" id="A4"><name><surname>Wild</surname>
<given-names>David J</given-names>
</name>
<xref ref-type="aff" rid="I1">1</xref>
<email>djwild@indiana.edu</email>
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<aff id="I1"><label>1</label>
School of Informatics and Computing, Indiana University, Bloomington, IN, USA</aff>
<aff id="I2"><label>2</label>
Eli Lilly and Company, Indianapolis, IN, USA</aff>
<aff id="I3"><label>3</label>
School of Library & Information Science, Indiana University, Bloomington, IN, USA</aff>
<pub-date pub-type="collection"><year>2010</year>
</pub-date>
<pub-date pub-type="epub"><day>20</day>
<month>8</month>
<year>2010</year>
</pub-date>
<volume>2</volume>
<fpage>6</fpage>
<lpage>6</lpage>
<history><date date-type="received"><day>22</day>
<month>6</month>
<year>2010</year>
</date>
<date date-type="accepted"><day>20</day>
<month>8</month>
<year>2010</year>
</date>
</history>
<permissions><copyright-statement>Copyright ©2010 Zhu et al; licensee BioMed Central Ltd.</copyright-statement>
<copyright-year>2010</copyright-year>
<copyright-holder>Zhu et al; licensee BioMed Central Ltd.</copyright-holder>
<license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/2.0"><license-p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/2.0">http://creativecommons.org/licenses/by/2.0</ext-link>
), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
</license>
</permissions>
<self-uri xlink:href="http://www.jcheminf.com/content/2/1/6"></self-uri>
<abstract><sec><title>Background</title>
<p>In recent years, there has been a huge increase in the amount of publicly-available and proprietary information pertinent to drug discovery. However, there is a distinct lack of data mining tools available to harness this information, and in particular for knowledge discovery across multiple information sources. At Indiana University we have an ongoing project with Eli Lilly to develop web-service based tools for integrative mining of chemical and biological information. In this paper, we report on the first of these tools, called WENDI (Web Engine for Non-obvious Drug Information) that attempts to find non-obvious relationships between a query compound and scholarly publications, biological properties, genes and diseases using multiple information sources.</p>
</sec>
<sec><title>Results</title>
<p>We have created an aggregate web service that takes a query compound as input, calls multiple web services for computation and database search, and returns an XML file that aggregates this information. We have also developed a client application that provides an easy-to-use interface to this web service. Both the service and client are publicly available.</p>
</sec>
<sec><title>Conclusions</title>
<p>Initial testing indicates this tool is useful in identifying potential biological applications of compounds that are not obvious, and in identifying corroborating and conflicting information from multiple sources. We encourage feedback on the tool to help us refine it further. We are now developing further tools based on this model.</p>
</sec>
</abstract>
</article-meta>
</front>
</pmc>
<affiliations><list><country><li>États-Unis</li>
</country>
<region><li>Indiana</li>
</region>
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<tree><country name="États-Unis"><region name="Indiana"><name sortKey="Zhu, Qian" sort="Zhu, Qian" uniqKey="Zhu Q" first="Qian" last="Zhu">Qian Zhu</name>
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<name sortKey="Ding, Ying" sort="Ding, Ying" uniqKey="Ding Y" first="Ying" last="Ding">Ying Ding</name>
<name sortKey="Lajiness, Michael S" sort="Lajiness, Michael S" uniqKey="Lajiness M" first="Michael S" last="Lajiness">Michael S. Lajiness</name>
<name sortKey="Wild, David J" sort="Wild, David J" uniqKey="Wild D" first="David J" last="Wild">David J. Wild</name>
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