Predicting Human Immunodeficiency Virus (HIV) Drug Resistance Using Recurrent Neural Networks
Identifieur interne : 002641 ( Istex/Corpus ); précédent : 002640; suivant : 002642Predicting Human Immunodeficiency Virus (HIV) Drug Resistance Using Recurrent Neural Networks
Auteurs : Isis Bonet ; M. García ; Yvan Saeys ; Yves Van De Peer ; Ricardo GrauSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2007.
Abstract
Abstract: Predicting HIV resistance to drugs is one of many problems for which bioinformaticians have implemented and trained machine learning methods, such as neural networks. Predicting HIV resistance would be much easier if we could directly use the three-dimensional (3D) structure of the targeted protein sequences, but unfortunately we rarely have enough structural information available to train a neural network. Fur-thermore, prediction of the 3D structure of a protein is not straightforward. However, characteristics related to the 3D structure can be used to train a machine learning algorithm as an alternative to take into account the information of the protein folding in the 3D space. Here, starting from this philosophy, we select the amino acid energies as features to predict HIV drug resistance, using a specific topology of a neural network. In this paper, we demonstrate that the amino acid ener-gies are good features to represent the HIV genotype. In addi-tion, it was shown that Bidirectional Recurrent Neural Networks can be used as an efficient classification method for this prob-lem. The prediction performance that was obtained was greater than or at least comparable to results obtained previously. The accuracies vary between 81.3% and 94.7%.
Url:
DOI: 10.1007/978-3-540-73053-8_23
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Predicting HIV resistance would be much easier if we could directly use the three-dimensional (3D) structure of the targeted protein sequences, but unfortunately we rarely have enough structural information available to train a neural network. Fur-thermore, prediction of the 3D structure of a protein is not straightforward. However, characteristics related to the 3D structure can be used to train a machine learning algorithm as an alternative to take into account the information of the protein folding in the 3D space. Here, starting from this philosophy, we select the amino acid energies as features to predict HIV drug resistance, using a specific topology of a neural network. In this paper, we demonstrate that the amino acid ener-gies are good features to represent the HIV genotype. In addi-tion, it was shown that Bidirectional Recurrent Neural Networks can be used as an efficient classification method for this prob-lem. The prediction performance that was obtained was greater than or at least comparable to results obtained previously. The accuracies vary between 81.3% and 94.7%.</div></front></TEI><istex><corpusName>springer</corpusName><author><json:item><name>Isis Bonet</name><affiliations><json:string>Center of Studies on Informatics, Central University of Las Villas, Cuba</json:string><json:string>E-mail: isisb@uclv.edu.cu</json:string></affiliations></json:item><json:item><name>María M. García</name><affiliations><json:string>Center of Studies on Informatics, Central University of Las Villas, Cuba</json:string><json:string>E-mail: mmgarcia@uclv.edu.cu</json:string></affiliations></json:item><json:item><name>Yvan Saeys</name><affiliations><json:string>Department of Plant Systems Biology, Flanders Interuniversity Institute for Biotechnology (VIB), Ghent University, Belgium</json:string><json:string>E-mail: yvan.saeys@ugent.be</json:string></affiliations></json:item><json:item><name>Yves Van de Peer</name><affiliations><json:string>Department of Plant Systems Biology, Flanders Interuniversity Institute for Biotechnology (VIB), Ghent University, Belgium</json:string><json:string>E-mail: yves.vandepeer@psb.ugent.be</json:string></affiliations></json:item><json:item><name>Ricardo Grau</name><affiliations><json:string>Center of Studies on Informatics, Central University of Las Villas, Cuba</json:string><json:string>E-mail: rgrau@uclv.edu.cu</json:string></affiliations></json:item></author><language><json:string>eng</json:string></language><abstract>Abstract: Predicting HIV resistance to drugs is one of many problems for which bioinformaticians have implemented and trained machine learning methods, such as neural networks. Predicting HIV resistance would be much easier if we could directly use the three-dimensional (3D) structure of the targeted protein sequences, but unfortunately we rarely have enough structural information available to train a neural network. Fur-thermore, prediction of the 3D structure of a protein is not straightforward. However, characteristics related to the 3D structure can be used to train a machine learning algorithm as an alternative to take into account the information of the protein folding in the 3D space. Here, starting from this philosophy, we select the amino acid energies as features to predict HIV drug resistance, using a specific topology of a neural network. In this paper, we demonstrate that the amino acid ener-gies are good features to represent the HIV genotype. In addi-tion, it was shown that Bidirectional Recurrent Neural Networks can be used as an efficient classification method for this prob-lem. The prediction performance that was obtained was greater than or at least comparable to results obtained previously. 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Predicting HIV resistance would be much easier if we could directly use the three-dimensional (3D) structure of the targeted protein sequences, but unfortunately we rarely have enough structural information available to train a neural network. Fur-thermore, prediction of the 3D structure of a protein is not straightforward. However, characteristics related to the 3D structure can be used to train a machine learning algorithm as an alternative to take into account the information of the protein folding in the 3D space. Here, starting from this philosophy, we select the amino acid energies as features to predict HIV drug resistance, using a specific topology of a neural network. In this paper, we demonstrate that the amino acid ener-gies are good features to represent the HIV genotype. In addi-tion, it was shown that Bidirectional Recurrent Neural Networks can be used as an efficient classification method for this prob-lem. The prediction performance that was obtained was greater than or at least comparable to results obtained previously. The accuracies vary between 81.3% and 94.7%.</p></abstract><textClass><keywords scheme="Book Subject Collection"><list><label>SUCO11645</label><item><term>Computer Science</term></item></list></keywords></textClass><textClass><keywords scheme="Book Subject Group"><list><label>I</label><label>I16013</label><label>I16021</label><label>I21017</label><label>I22021</label><label>I2203X</label><label>I23050</label><item><term>Computer Science</term></item><item><term>Computation by Abstract Devices</term></item><item><term>Algorithm Analysis and Problem Complexity</term></item><item><term>Artificial Intelligence (incl. 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Robotics)</BookSubject><BookSubject Code="I22021" Priority="4" Type="Secondary">Image Processing and Computer Vision</BookSubject><BookSubject Code="I2203X" Priority="5" Type="Secondary">Pattern Recognition</BookSubject><BookSubject Code="I23050" Priority="6" Type="Secondary">Computational Biology/Bioinformatics</BookSubject><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup></BookInfo><BookHeader><EditorGroup><Editor><EditorName DisplayOrder="Western"><GivenName>José</GivenName><FamilyName>Mira</FamilyName></EditorName><Contact><Email>jmira@dia.uned.es</Email></Contact></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>José</GivenName><GivenName>R.</GivenName><FamilyName>Álvarez</FamilyName></EditorName><Contact><Email>jras@dia.uned.es</Email></Contact></Editor></EditorGroup></BookHeader><Chapter ID="Chap23" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingStyle="Unnumbered" TocLevels="0"><ChapterID>23</ChapterID><ChapterDOI>10.1007/978-3-540-73053-8_23</ChapterDOI><ChapterSequenceNumber>23</ChapterSequenceNumber><ChapterTitle Language="En">Predicting Human Immunodeficiency Virus (HIV) Drug Resistance Using Recurrent Neural Networks</ChapterTitle><ChapterFirstPage>234</ChapterFirstPage><ChapterLastPage>243</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2007</CopyrightYear></ChapterCopyright><ChapterGrants Type="Regular"><MetadataGrant Grant="OpenAccess"></MetadataGrant><AbstractGrant Grant="OpenAccess"></AbstractGrant><BodyPDFGrant Grant="Restricted"></BodyPDFGrant><BodyHTMLGrant Grant="Restricted"></BodyHTMLGrant><BibliographyGrant Grant="Restricted"></BibliographyGrant><ESMGrant Grant="Restricted"></ESMGrant></ChapterGrants></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff1"><AuthorName DisplayOrder="Western"><GivenName>Isis</GivenName><FamilyName>Bonet</FamilyName></AuthorName><Contact><Email>isisb@uclv.edu.cu</Email></Contact></Author><Author AffiliationIDS="Aff1"><AuthorName DisplayOrder="Western"><GivenName>María</GivenName><GivenName>M.</GivenName><FamilyName>García</FamilyName></AuthorName><Contact><Email>mmgarcia@uclv.edu.cu</Email></Contact></Author><Author AffiliationIDS="Aff2"><AuthorName DisplayOrder="Western"><GivenName>Yvan</GivenName><FamilyName>Saeys</FamilyName></AuthorName><Contact><Email>yvan.saeys@ugent.be</Email></Contact></Author><Author AffiliationIDS="Aff2"><AuthorName DisplayOrder="Western"><GivenName>Yves</GivenName><Particle>Van de</Particle><FamilyName>Peer</FamilyName></AuthorName><Contact><Email>yves.vandepeer@psb.ugent.be</Email></Contact></Author><Author AffiliationIDS="Aff1"><AuthorName DisplayOrder="Western"><GivenName>Ricardo</GivenName><FamilyName>Grau</FamilyName></AuthorName><Contact><Email>rgrau@uclv.edu.cu</Email></Contact></Author><Affiliation ID="Aff1"><OrgName>Center of Studies on Informatics, Central University of Las Villas</OrgName><OrgAddress><Country>Cuba</Country></OrgAddress></Affiliation><Affiliation ID="Aff2"><OrgName>Department of Plant Systems Biology, Flanders Interuniversity Institute for Biotechnology (VIB), Ghent University</OrgName><OrgAddress><Country>Belgium</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>Predicting HIV resistance to drugs is one of many problems for which bioinformaticians have implemented and trained machine learning methods, such as neural networks. Predicting HIV resistance would be much easier if we could directly use the three-dimensional (3D) structure of the targeted protein sequences, but unfortunately we rarely have enough structural information available to train a neural network. Fur-thermore, prediction of the 3D structure of a protein is not straightforward. However, characteristics related to the 3D structure can be used to train a machine learning algorithm as an alternative to take into account the information of the protein folding in the 3D space. Here, starting from this philosophy, we select the amino acid energies as features to predict HIV drug resistance, using a specific topology of a neural network. In this paper, we demonstrate that the amino acid ener-gies are good features to represent the HIV genotype. In addi-tion, it was shown that Bidirectional Recurrent Neural Networks can be used as an efficient classification method for this prob-lem. The prediction performance that was obtained was greater than or at least comparable to results obtained previously. The accuracies vary between 81.3% and 94.7%.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Predicting Human Immunodeficiency Virus (HIV) Drug Resistance Using Recurrent Neural Networks</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Predicting Human Immunodeficiency Virus (HIV) Drug Resistance Using Recurrent Neural Networks</title></titleInfo><name type="personal"><namePart type="given">Isis</namePart><namePart type="family">Bonet</namePart><affiliation>Center of Studies on Informatics, Central University of Las Villas, Cuba</affiliation><affiliation>E-mail: isisb@uclv.edu.cu</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">María</namePart><namePart type="given">M.</namePart><namePart type="family">García</namePart><affiliation>Center of Studies on Informatics, Central University of Las Villas, Cuba</affiliation><affiliation>E-mail: mmgarcia@uclv.edu.cu</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Yvan</namePart><namePart type="family">Saeys</namePart><affiliation>Department of Plant Systems Biology, Flanders Interuniversity Institute for Biotechnology (VIB), Ghent University, Belgium</affiliation><affiliation>E-mail: yvan.saeys@ugent.be</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Yves</namePart><namePart type="family">Van de Peer</namePart><affiliation>Department of Plant Systems Biology, Flanders Interuniversity Institute for Biotechnology (VIB), Ghent University, Belgium</affiliation><affiliation>E-mail: yves.vandepeer@psb.ugent.be</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Ricardo</namePart><namePart type="family">Grau</namePart><affiliation>Center of Studies on Informatics, Central University of Las Villas, Cuba</affiliation><affiliation>E-mail: rgrau@uclv.edu.cu</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="conference [eBooks]" displayLabel="OriginalPaper"></genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2007</dateIssued><copyrightDate encoding="w3cdtf">2007</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">Abstract: Predicting HIV resistance to drugs is one of many problems for which bioinformaticians have implemented and trained machine learning methods, such as neural networks. Predicting HIV resistance would be much easier if we could directly use the three-dimensional (3D) structure of the targeted protein sequences, but unfortunately we rarely have enough structural information available to train a neural network. Fur-thermore, prediction of the 3D structure of a protein is not straightforward. However, characteristics related to the 3D structure can be used to train a machine learning algorithm as an alternative to take into account the information of the protein folding in the 3D space. Here, starting from this philosophy, we select the amino acid energies as features to predict HIV drug resistance, using a specific topology of a neural network. In this paper, we demonstrate that the amino acid ener-gies are good features to represent the HIV genotype. In addi-tion, it was shown that Bidirectional Recurrent Neural Networks can be used as an efficient classification method for this prob-lem. The prediction performance that was obtained was greater than or at least comparable to results obtained previously. 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