IntelliGO: a new vector-based semantic similarity measure including annotation origin
Identifieur interne : 000102 ( Ncbi/Merge ); précédent : 000101; suivant : 000103IntelliGO: a new vector-based semantic similarity measure including annotation origin
Auteurs : Sidahmed Benabderrahmane [France] ; Malika Smail-Tabbone [France] ; Olivier Poch [France] ; Amedeo Napoli [France] ; Marie-Dominique Devignes [France]Source :
- BMC Bioinformatics [ 1471-2105 ] ; 2010.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- chemical , genetics : Proteins.
- genetics : Saccharomyces cerevisiae.
- methods : Computational Biology.
- Algorithms, Cluster Analysis, Humans, Molecular Sequence Annotation, Semantics, Software, Terminology as Topic, Vocabulary, Controlled.
Abstract
The Gene Ontology (GO) is a well known controlled vocabulary describing the
We present here a new semantic similarity measure called
The
An on-line version of the
Url:
DOI: 10.1186/1471-2105-11-588
PubMed: 21122125
PubMed Central: 3098105
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PMC:3098105Le document en format XML
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<series><title level="j">BMC Bioinformatics</title>
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<imprint><date when="2010">2010</date>
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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term>
<term>Cluster Analysis</term>
<term>Computational Biology (methods)</term>
<term>Humans</term>
<term>Molecular Sequence Annotation</term>
<term>Proteins (genetics)</term>
<term>Saccharomyces cerevisiae (genetics)</term>
<term>Semantics</term>
<term>Software</term>
<term>Terminology as Topic</term>
<term>Vocabulary, Controlled</term>
</keywords>
<keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes</term>
<term>Analyse de regroupements</term>
<term>Annotation de séquence moléculaire</term>
<term>Biologie informatique ()</term>
<term>Humains</term>
<term>Logiciel</term>
<term>Protéines (génétique)</term>
<term>Saccharomyces cerevisiae (génétique)</term>
<term>Sémantique</term>
<term>Terminologie comme sujet</term>
<term>Vocabulaire contrôlé</term>
</keywords>
<keywords scheme="MESH" type="chemical" qualifier="genetics" xml:lang="en"><term>Proteins</term>
</keywords>
<keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Saccharomyces cerevisiae</term>
</keywords>
<keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Protéines</term>
<term>Saccharomyces cerevisiae</term>
</keywords>
<keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Computational Biology</term>
</keywords>
<keywords scheme="MESH" xml:lang="en"><term>Algorithms</term>
<term>Cluster Analysis</term>
<term>Humans</term>
<term>Molecular Sequence Annotation</term>
<term>Semantics</term>
<term>Software</term>
<term>Terminology as Topic</term>
<term>Vocabulary, Controlled</term>
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<term>Analyse de regroupements</term>
<term>Annotation de séquence moléculaire</term>
<term>Biologie informatique</term>
<term>Humains</term>
<term>Logiciel</term>
<term>Sémantique</term>
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>The Gene Ontology (GO) is a well known controlled vocabulary describing the <italic>biological process</italic>
, <italic>molecular function </italic>
and <italic>cellular component </italic>
aspects of gene annotation. It has become a widely used knowledge source in bioinformatics for annotating genes and measuring their semantic similarity. These measures generally involve the GO graph structure, the information content of GO aspects, or a combination of both. However, only a few of the semantic similarity measures described so far can handle GO annotations differently according to their origin (<italic>i.e</italic>
. their evidence codes).</p>
</sec>
<sec><title>Results</title>
<p>We present here a new semantic similarity measure called <italic>IntelliGO </italic>
which integrates several complementary properties in a novel vector space model. The coefficients associated with each GO term that annotates a given gene or protein include its information content as well as a customized value for each type of GO evidence code. The generalized cosine similarity measure, used for calculating the dot product between two vectors, has been rigorously adapted to the context of the GO graph. The <italic>IntelliGO </italic>
similarity measure is tested on two benchmark datasets consisting of KEGG pathways and Pfam domains grouped as clans, considering the GO <italic>biological process </italic>
and <italic>molecular function </italic>
terms, respectively, for a total of 683 yeast and human genes and involving more than 67,900 pair-wise comparisons. The ability of the <italic>IntelliGO </italic>
similarity measure to express the biological cohesion of sets of genes compares favourably to four existing similarity measures. For inter-set comparison, it consistently discriminates between distinct sets of genes. Furthermore, the <italic>IntelliGO </italic>
similarity measure allows the influence of weights assigned to evidence codes to be checked. Finally, the results obtained with a complementary reference technique give intermediate but correct correlation values with the sequence similarity, Pfam, and Enzyme classifications when compared to previously published measures.</p>
</sec>
<sec><title>Conclusions</title>
<p>The <italic>IntelliGO </italic>
similarity measure provides a customizable and comprehensive method for quantifying gene similarity based on GO annotations. It also displays a robust set-discriminating power which suggests it will be useful for functional clustering.</p>
</sec>
<sec><title>Availability</title>
<p>An on-line version of the <italic>IntelliGO </italic>
similarity measure is available at: <ext-link ext-link-type="uri" xlink:href="http://bioinfo.loria.fr/Members/benabdsi/intelligo_project/">http://bioinfo.loria.fr/Members/benabdsi/intelligo_project/</ext-link>
</p>
</sec>
</div>
</front>
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<series><title level="j">BMC Bioinformatics</title>
<idno type="eISSN">1471-2105</idno>
<imprint><date when="2010">2010</date>
</imprint>
</series>
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</sourceDesc>
</fileDesc>
<profileDesc><textClass></textClass>
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</teiHeader>
<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>The Gene Ontology (GO) is a well known controlled vocabulary describing the <italic>biological process</italic>
, <italic>molecular function </italic>
and <italic>cellular component </italic>
aspects of gene annotation. It has become a widely used knowledge source in bioinformatics for annotating genes and measuring their semantic similarity. These measures generally involve the GO graph structure, the information content of GO aspects, or a combination of both. However, only a few of the semantic similarity measures described so far can handle GO annotations differently according to their origin (<italic>i.e</italic>
. their evidence codes).</p>
</sec>
<sec><title>Results</title>
<p>We present here a new semantic similarity measure called <italic>IntelliGO </italic>
which integrates several complementary properties in a novel vector space model. The coefficients associated with each GO term that annotates a given gene or protein include its information content as well as a customized value for each type of GO evidence code. The generalized cosine similarity measure, used for calculating the dot product between two vectors, has been rigorously adapted to the context of the GO graph. The <italic>IntelliGO </italic>
similarity measure is tested on two benchmark datasets consisting of KEGG pathways and Pfam domains grouped as clans, considering the GO <italic>biological process </italic>
and <italic>molecular function </italic>
terms, respectively, for a total of 683 yeast and human genes and involving more than 67,900 pair-wise comparisons. The ability of the <italic>IntelliGO </italic>
similarity measure to express the biological cohesion of sets of genes compares favourably to four existing similarity measures. For inter-set comparison, it consistently discriminates between distinct sets of genes. Furthermore, the <italic>IntelliGO </italic>
similarity measure allows the influence of weights assigned to evidence codes to be checked. Finally, the results obtained with a complementary reference technique give intermediate but correct correlation values with the sequence similarity, Pfam, and Enzyme classifications when compared to previously published measures.</p>
</sec>
<sec><title>Conclusions</title>
<p>The <italic>IntelliGO </italic>
similarity measure provides a customizable and comprehensive method for quantifying gene similarity based on GO annotations. It also displays a robust set-discriminating power which suggests it will be useful for functional clustering.</p>
</sec>
<sec><title>Availability</title>
<p>An on-line version of the <italic>IntelliGO </italic>
similarity measure is available at: <ext-link ext-link-type="uri" xlink:href="http://bioinfo.loria.fr/Members/benabdsi/intelligo_project/">http://bioinfo.loria.fr/Members/benabdsi/intelligo_project/</ext-link>
</p>
</sec>
</div>
</front>
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</pmc>
<pubmed><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">IntelliGO: a new vector-based semantic similarity measure including annotation origin.</title>
<author><name sortKey="Benabderrahmane, Sidahmed" sort="Benabderrahmane, Sidahmed" uniqKey="Benabderrahmane S" first="Sidahmed" last="Benabderrahmane">Sidahmed Benabderrahmane</name>
<affiliation wicri:level="4"><nlm:affiliation>LORIA (CNRS, INRIA, Nancy-Université), Équipe Orpailleur, Bâtiment B, Campus scientifique, 54506 Vandoeuvre-lès-Nancy Cedex, France. benabdsi@loria.fr</nlm:affiliation>
<country xml:lang="fr">France</country>
<wicri:regionArea>LORIA (CNRS, INRIA, Nancy-Université), Équipe Orpailleur, Bâtiment B, Campus scientifique, 54506 Vandoeuvre-lès-Nancy Cedex</wicri:regionArea>
<placeName><region type="region" nuts="2">Grand Est</region>
<region type="old region" nuts="2">Lorraine (région)</region>
<settlement type="city">Vandœuvre-lès-Nancy</settlement>
</placeName>
<orgName type="university">Nancy-Université</orgName>
</affiliation>
</author>
<author><name sortKey="Smail Tabbone, Malika" sort="Smail Tabbone, Malika" uniqKey="Smail Tabbone M" first="Malika" last="Smail-Tabbone">Malika Smail-Tabbone</name>
</author>
<author><name sortKey="Poch, Olivier" sort="Poch, Olivier" uniqKey="Poch O" first="Olivier" last="Poch">Olivier Poch</name>
</author>
<author><name sortKey="Napoli, Amedeo" sort="Napoli, Amedeo" uniqKey="Napoli A" first="Amedeo" last="Napoli">Amedeo Napoli</name>
</author>
<author><name sortKey="Devignes, Marie Dominique" sort="Devignes, Marie Dominique" uniqKey="Devignes M" first="Marie-Dominique" last="Devignes">Marie-Dominique Devignes</name>
</author>
</titleStmt>
<publicationStmt><idno type="wicri:source">PubMed</idno>
<date when="2010">2010</date>
<idno type="doi">10.1186/1471-2105-11-588</idno>
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<sourceDesc><biblStruct><analytic><title xml:lang="en">IntelliGO: a new vector-based semantic similarity measure including annotation origin.</title>
<author><name sortKey="Benabderrahmane, Sidahmed" sort="Benabderrahmane, Sidahmed" uniqKey="Benabderrahmane S" first="Sidahmed" last="Benabderrahmane">Sidahmed Benabderrahmane</name>
<affiliation wicri:level="4"><nlm:affiliation>LORIA (CNRS, INRIA, Nancy-Université), Équipe Orpailleur, Bâtiment B, Campus scientifique, 54506 Vandoeuvre-lès-Nancy Cedex, France. benabdsi@loria.fr</nlm:affiliation>
<country xml:lang="fr">France</country>
<wicri:regionArea>LORIA (CNRS, INRIA, Nancy-Université), Équipe Orpailleur, Bâtiment B, Campus scientifique, 54506 Vandoeuvre-lès-Nancy Cedex</wicri:regionArea>
<placeName><region type="region" nuts="2">Grand Est</region>
<region type="old region" nuts="2">Lorraine (région)</region>
<settlement type="city">Vandœuvre-lès-Nancy</settlement>
</placeName>
<orgName type="university">Nancy-Université</orgName>
</affiliation>
</author>
<author><name sortKey="Smail Tabbone, Malika" sort="Smail Tabbone, Malika" uniqKey="Smail Tabbone M" first="Malika" last="Smail-Tabbone">Malika Smail-Tabbone</name>
</author>
<author><name sortKey="Poch, Olivier" sort="Poch, Olivier" uniqKey="Poch O" first="Olivier" last="Poch">Olivier Poch</name>
</author>
<author><name sortKey="Napoli, Amedeo" sort="Napoli, Amedeo" uniqKey="Napoli A" first="Amedeo" last="Napoli">Amedeo Napoli</name>
</author>
<author><name sortKey="Devignes, Marie Dominique" sort="Devignes, Marie Dominique" uniqKey="Devignes M" first="Marie-Dominique" last="Devignes">Marie-Dominique Devignes</name>
</author>
</analytic>
<series><title level="j">BMC bioinformatics</title>
<idno type="eISSN">1471-2105</idno>
<imprint><date when="2010" type="published">2010</date>
</imprint>
</series>
</biblStruct>
</sourceDesc>
</fileDesc>
<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term>
<term>Cluster Analysis</term>
<term>Computational Biology (methods)</term>
<term>Humans</term>
<term>Molecular Sequence Annotation</term>
<term>Proteins (genetics)</term>
<term>Saccharomyces cerevisiae (genetics)</term>
<term>Semantics</term>
<term>Software</term>
<term>Terminology as Topic</term>
<term>Vocabulary, Controlled</term>
</keywords>
<keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes</term>
<term>Analyse de regroupements</term>
<term>Annotation de séquence moléculaire</term>
<term>Biologie informatique ()</term>
<term>Humains</term>
<term>Logiciel</term>
<term>Protéines (génétique)</term>
<term>Saccharomyces cerevisiae (génétique)</term>
<term>Sémantique</term>
<term>Terminologie comme sujet</term>
<term>Vocabulaire contrôlé</term>
</keywords>
<keywords scheme="MESH" type="chemical" qualifier="genetics" xml:lang="en"><term>Proteins</term>
</keywords>
<keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Saccharomyces cerevisiae</term>
</keywords>
<keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Protéines</term>
<term>Saccharomyces cerevisiae</term>
</keywords>
<keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Computational Biology</term>
</keywords>
<keywords scheme="MESH" xml:lang="en"><term>Algorithms</term>
<term>Cluster Analysis</term>
<term>Humans</term>
<term>Molecular Sequence Annotation</term>
<term>Semantics</term>
<term>Software</term>
<term>Terminology as Topic</term>
<term>Vocabulary, Controlled</term>
</keywords>
<keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term>
<term>Analyse de regroupements</term>
<term>Annotation de séquence moléculaire</term>
<term>Biologie informatique</term>
<term>Humains</term>
<term>Logiciel</term>
<term>Sémantique</term>
<term>Terminologie comme sujet</term>
<term>Vocabulaire contrôlé</term>
</keywords>
</textClass>
</profileDesc>
</teiHeader>
<front><div type="abstract" xml:lang="en">The Gene Ontology (GO) is a well known controlled vocabulary describing the biological process, molecular function and cellular component aspects of gene annotation. It has become a widely used knowledge source in bioinformatics for annotating genes and measuring their semantic similarity. These measures generally involve the GO graph structure, the information content of GO aspects, or a combination of both. However, only a few of the semantic similarity measures described so far can handle GO annotations differently according to their origin (i.e. their evidence codes).</div>
</front>
</TEI>
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