Ontology-guided data preparation for discovering genotype-phenotype relationships
Identifieur interne : 000081 ( Pmc/Checkpoint ); précédent : 000080; suivant : 000082Ontology-guided data preparation for discovering genotype-phenotype relationships
Auteurs : Adrien Coulet [France] ; Malika Smaïl-Tabbone [France] ; Pascale Benlian [France] ; Amedeo Napoli [France] ; Marie-Dominique Devignes [France]Source :
- BMC Bioinformatics [ 1471-2105 ] ; 2008.
Abstract
Complexity and amount of post-genomic data constitute two major factors limiting the application of Knowledge Discovery in Databases (KDD) methods in life sciences. Bio-ontologies may nowadays play key roles in knowledge discovery in life science providing semantics to data and to extracted units, by taking advantage of the progress of Semantic Web technologies concerning the understanding and availability of tools for knowledge representation, extraction, and reasoning.
This paper presents a method that exploits bio-ontologies for guiding data selection within the preparation step of the KDD process. We propose three scenarios in which domain knowledge and ontology elements such as subsumption, properties, class descriptions, are taken into account for data selection, before the data mining step. Each of these scenarios is illustrated within a case-study relative to the search of genotype-phenotype relationships in a familial hypercholesterolemia dataset. The guiding of data selection based on domain knowledge is analysed and shows a direct influence on the volume and significance of the data mining results.
The method proposed in this paper is an efficient alternative to numerical methods for data selection based on domain knowledge. In turn, the results of this study may be reused in ontology modelling and data integration.
Url:
DOI: 10.1186/1471-2105-9-S4-S3
PubMed: 18460176
PubMed Central: 2367630
Affiliations:
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PMC:2367630Le document en format XML
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>Complexity and amount of post-genomic data constitute two major factors limiting the application of Knowledge Discovery in Databases (KDD) methods in life sciences. Bio-ontologies may nowadays play key roles in knowledge discovery in life science providing semantics to data and to extracted units, by taking advantage of the progress of Semantic Web technologies concerning the understanding and availability of tools for knowledge representation, extraction, and reasoning.</p>
</sec>
<sec><title>Results</title>
<p>This paper presents a method that exploits bio-ontologies for guiding data selection within the preparation step of the KDD process. We propose three scenarios in which domain knowledge and ontology elements such as subsumption, properties, class descriptions, are taken into account for data selection, before the data mining step. Each of these scenarios is illustrated within a case-study relative to the search of genotype-phenotype relationships in a familial hypercholesterolemia dataset. The guiding of data selection based on domain knowledge is analysed and shows a direct influence on the volume and significance of the data mining results.</p>
</sec>
<sec><title>Conclusions</title>
<p>The method proposed in this paper is an efficient alternative to numerical methods for data selection based on domain knowledge. In turn, the results of this study may be reused in ontology modelling and data integration.</p>
</sec>
</div>
</front>
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<front><journal-meta><journal-id journal-id-type="nlm-ta">BMC Bioinformatics</journal-id>
<journal-title>BMC Bioinformatics</journal-title>
<issn pub-type="epub">1471-2105</issn>
<publisher><publisher-name>BioMed Central</publisher-name>
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<title-group><article-title>Ontology-guided data preparation for discovering genotype-phenotype relationships</article-title>
</title-group>
<contrib-group><contrib id="A1" corresp="yes" contrib-type="author"><name><surname>Coulet</surname>
<given-names>Adrien</given-names>
</name>
<xref ref-type="aff" rid="I1">1</xref>
<xref ref-type="aff" rid="I2">2</xref>
<email>adrien.coulet@loria.fr</email>
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<contrib id="A2" contrib-type="author"><name><surname>Smaïl-Tabbone</surname>
<given-names>Malika</given-names>
</name>
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<email>malika.smail@loria.fr</email>
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<contrib id="A3" contrib-type="author"><name><surname>Benlian</surname>
<given-names>Pascale</given-names>
</name>
<xref ref-type="aff" rid="I3">3</xref>
<email>pascale.benlian@sat.ap-hop-paris.fr</email>
</contrib>
<contrib id="A4" contrib-type="author"><name><surname>Napoli</surname>
<given-names>Amedeo</given-names>
</name>
<xref ref-type="aff" rid="I2">2</xref>
<email>amedeo.napoli@loria.fr</email>
</contrib>
<contrib id="A5" contrib-type="author"><name><surname>Devignes</surname>
<given-names>Marie-Dominique</given-names>
</name>
<xref ref-type="aff" rid="I2">2</xref>
<email>marie-dominique.devignes@loria.fr</email>
</contrib>
</contrib-group>
<aff id="I1"><label>1</label>
KIKA Medical, Paris, F-75012, France</aff>
<aff id="I2"><label>2</label>
LORIA (UMR 7503 CNRS-INPL-INRIA-Nancy2-UHP), Vandoeuvre-lès-Nancy, F- 54506, France</aff>
<aff id="I3"><label>3</label>
Université Pierre et Marie Curie - Paris6, INSERM UMRS 538 Biochimie-Biologie Moléculaire, Paris, F-75571, France</aff>
<pub-date pub-type="collection"><year>2008</year>
</pub-date>
<pub-date pub-type="epub"><day>25</day>
<month>4</month>
<year>2008</year>
</pub-date>
<volume>9</volume>
<issue>Suppl 4</issue>
<supplement><named-content content-type="supplement-title">A Semantic Web for Bioinformatics: Goals, Tools, Systems, Applications</named-content>
<named-content content-type="supplement-editor">Paolo Romano, Michael Schroeder, Nicola Cannata and Roberto Marangoni</named-content>
</supplement>
<fpage>S3</fpage>
<lpage>S3</lpage>
<ext-link ext-link-type="uri" xlink:href="http://www.biomedcentral.com/1471-2105/9/S4/S3"></ext-link>
<permissions><copyright-statement>Copyright © 2008 Coulet et al.; licensee BioMed Central Ltd.</copyright-statement>
<copyright-year>2008</copyright-year>
<copyright-holder>Coulet et al.; licensee BioMed Central Ltd.</copyright-holder>
<license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/2.0"><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"></ext-link>
), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>
<pmc-comment>
Coulet
Adrien
adrien.coulet@loria.fr
Ontology-guided data preparation for discovering genotype-phenotype relationships
2008 BMC Bioinformatics 9(Suppl 4): S3-. (2008) 1471-2105(2008)9:Suppl 4 urn:ISSN:1471-2105 </pmc-comment>
</license>
</permissions>
<abstract><sec><title>Background</title>
<p>Complexity and amount of post-genomic data constitute two major factors limiting the application of Knowledge Discovery in Databases (KDD) methods in life sciences. Bio-ontologies may nowadays play key roles in knowledge discovery in life science providing semantics to data and to extracted units, by taking advantage of the progress of Semantic Web technologies concerning the understanding and availability of tools for knowledge representation, extraction, and reasoning.</p>
</sec>
<sec><title>Results</title>
<p>This paper presents a method that exploits bio-ontologies for guiding data selection within the preparation step of the KDD process. We propose three scenarios in which domain knowledge and ontology elements such as subsumption, properties, class descriptions, are taken into account for data selection, before the data mining step. Each of these scenarios is illustrated within a case-study relative to the search of genotype-phenotype relationships in a familial hypercholesterolemia dataset. The guiding of data selection based on domain knowledge is analysed and shows a direct influence on the volume and significance of the data mining results.</p>
</sec>
<sec><title>Conclusions</title>
<p>The method proposed in this paper is an efficient alternative to numerical methods for data selection based on domain knowledge. In turn, the results of this study may be reused in ontology modelling and data integration.</p>
</sec>
</abstract>
<conference><conf-date>12–15 June 2007</conf-date>
<conf-name>Seventh International Workshop on Network Tools and Applications in Biology (NETTAB 2007)</conf-name>
<conf-loc>Pisa, Italy</conf-loc>
</conference>
</article-meta>
</front>
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<name sortKey="Smail Tabbone, Malika" sort="Smail Tabbone, Malika" uniqKey="Smail Tabbone M" first="Malika" last="Smaïl-Tabbone">Malika Smaïl-Tabbone</name>
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