Constructing Treatment Portfolios Using Affinity Propagation
Identifieur interne : 000B69 ( Istex/Corpus ); précédent : 000B68; suivant : 000B70Constructing Treatment Portfolios Using Affinity Propagation
Auteurs : Delbert Dueck ; Brendan J. Frey ; Nebojsa Jojic ; Vladimir Jojic ; Guri Giaever ; Andrew Emili ; Gabe Musso ; Robert HegeleSource :
- Lecture Notes in Computer Science [ 0302-9743 ]
Abstract
Abstract: A key problem of interest to biologists and medical researchers is the selection of a subset of queries or treatments that provide maximum utility for a population of targets. For example, when studying how gene deletion mutants respond to each of thousands of drugs, it is desirable to identify a small subset of genes that nearly uniquely define a drug ‘footprint’ that provides maximum predictability about the organism’s response to the drugs. As another example, when designing a cocktail of HIV genome sequences to be used as a vaccine, it is desirable to identify a small number of sequences that provide maximum immunological protection to a specified population of recipients. We refer to this task as ‘treatment portfolio design’ and formalize it as a facility location problem. Finding a treatment portfolio is NP-hard in the size of portfolio and number of targets, but a variety of greedy algorithms can be applied. We introduce a new algorithm for treatment portfolio design based on similar insights that made the recently-published affinity propagation algorithm work quite well for clustering tasks. We demonstrate this method using the two problems described above: selecting a subset of yeast genes that act as a drug-response footprint, and selecting a subset of vaccine sequences that provide maximum epitope coverage for an HIV genome population.
Url:
DOI: 10.1007/978-3-540-78839-3_31
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ISTEX:4E2DB7E9E3E0FA02EC2790BF840D404B35FFA7E1Le document en format XML
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Frey</name><affiliation><mods:affiliation>Electrical and Computer Engineering, University of Toronto, Canada</mods:affiliation></affiliation><affiliation><mods:affiliation>Center for Cellular and Biomolecular Research, University of Toronto, Canada</mods:affiliation></affiliation></author><author><name sortKey="Jojic, Nebojsa" sort="Jojic, Nebojsa" uniqKey="Jojic N" first="Nebojsa" last="Jojic">Nebojsa Jojic</name><affiliation><mods:affiliation>Machine Learning and Statistics, Microsoft Research, Redmond, USA</mods:affiliation></affiliation></author><author><name sortKey="Jojic, Vladimir" sort="Jojic, Vladimir" uniqKey="Jojic V" first="Vladimir" last="Jojic">Vladimir Jojic</name><affiliation><mods:affiliation>Electrical and Computer Engineering, University of Toronto, Canada</mods:affiliation></affiliation><affiliation><mods:affiliation>Machine Learning and Statistics, Microsoft Research, Redmond, USA</mods:affiliation></affiliation><affiliation><mods:affiliation>Computer Science, Stanford University, USA</mods:affiliation></affiliation></author><author><name sortKey="Giaever, Guri" sort="Giaever, Guri" uniqKey="Giaever G" first="Guri" last="Giaever">Guri Giaever</name><affiliation><mods:affiliation>Center for Cellular and Biomolecular Research, University of Toronto, Canada</mods:affiliation></affiliation></author><author><name sortKey="Emili, Andrew" sort="Emili, Andrew" uniqKey="Emili A" first="Andrew" last="Emili">Andrew Emili</name><affiliation><mods:affiliation>Center for Cellular and Biomolecular Research, University of Toronto, Canada</mods:affiliation></affiliation></author><author><name sortKey="Musso, Gabe" sort="Musso, Gabe" uniqKey="Musso G" first="Gabe" last="Musso">Gabe Musso</name><affiliation><mods:affiliation>Center for Cellular and Biomolecular Research, University of Toronto, Canada</mods:affiliation></affiliation></author><author><name sortKey="Hegele, Robert" sort="Hegele, Robert" uniqKey="Hegele R" first="Robert" last="Hegele">Robert Hegele</name><affiliation><mods:affiliation>Cardiovascular Genetics Laboratory, Robarts Research Institute, London, Canada</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="s" type="main" xml:lang="en">Lecture Notes in Computer Science</title><idno type="ISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="ISSN">0302-9743</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0302-9743</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: A key problem of interest to biologists and medical researchers is the selection of a subset of queries or treatments that provide maximum utility for a population of targets. For example, when studying how gene deletion mutants respond to each of thousands of drugs, it is desirable to identify a small subset of genes that nearly uniquely define a drug ‘footprint’ that provides maximum predictability about the organism’s response to the drugs. As another example, when designing a cocktail of HIV genome sequences to be used as a vaccine, it is desirable to identify a small number of sequences that provide maximum immunological protection to a specified population of recipients. We refer to this task as ‘treatment portfolio design’ and formalize it as a facility location problem. Finding a treatment portfolio is NP-hard in the size of portfolio and number of targets, but a variety of greedy algorithms can be applied. We introduce a new algorithm for treatment portfolio design based on similar insights that made the recently-published affinity propagation algorithm work quite well for clustering tasks. We demonstrate this method using the two problems described above: selecting a subset of yeast genes that act as a drug-response footprint, and selecting a subset of vaccine sequences that provide maximum epitope coverage for an HIV genome population.</div></front></TEI><istex><corpusName>springer-ebooks</corpusName><keywords><teeft><json:string>affinity propagation</json:string><json:string>portfolio</json:string><json:string>algorithm</json:string><json:string>epitope</json:string><json:string>vaccine</json:string><json:string>subset</json:string><json:string>immune</json:string><json:string>treatment portfolio</json:string><json:string>immune system</json:string><json:string>dueck</json:string><json:string>datum</json:string><json:string>treatment portfolio design</json:string><json:string>affinity propagation algorithm</json:string><json:string>facility location problem</json:string><json:string>natural strain</json:string><json:string>datum point</json:string><json:string>vertex substitution heuristic</json:string><json:string>exemplar</json:string><json:string>query</json:string><json:string>greedy method</json:string><json:string>treatment cost</json:string><json:string>affinity</json:string><json:string>computational</json:string><json:string>random restarts</json:string><json:string>natural strain cocktail</json:string><json:string>exposure time</json:string><json:string>other point</json:string><json:string>medical researcher</json:string><json:string>vaccine design</json:string><json:string>approximation algorithm</json:string><json:string>propagation</json:string><json:string>test drug</json:string><json:string>yeast gene</json:string><json:string>greedy algorithm</json:string><json:string>potential cluster center</json:string><json:string>small number</json:string><json:string>potential exemplar</json:string><json:string>high utility</json:string><json:string>yeast strain</json:string><json:string>laboratory procedure</json:string><json:string>different dosage level</json:string><json:string>utility function</json:string><json:string>immunological protection</json:string><json:string>total number</json:string><json:string>test datum</json:string><json:string>good solution</json:string><json:string>linear program</json:string><json:string>disease condition</json:string><json:string>epitope recognizable</json:string><json:string>single patient</json:string><json:string>single color</json:string><json:string>high coverage</json:string><json:string>vaccine component</json:string><json:string>facility location</json:string><json:string>higher coverage</json:string><json:string>large number</json:string><json:string>random initialization</json:string><json:string>other treatment</json:string></teeft></keywords><author><json:item><name>Delbert Dueck</name><affiliations><json:string>Electrical and Computer Engineering, University of Toronto, Canada</json:string></affiliations></json:item><json:item><name>Brendan J. Frey</name><affiliations><json:string>Electrical and Computer Engineering, University of Toronto, Canada</json:string><json:string>Center for Cellular and Biomolecular Research, University of Toronto, Canada</json:string></affiliations></json:item><json:item><name>Nebojsa Jojic</name><affiliations><json:string>Machine Learning and Statistics, Microsoft Research, Redmond, USA</json:string></affiliations></json:item><json:item><name>Vladimir Jojic</name><affiliations><json:string>Electrical and Computer Engineering, University of Toronto, Canada</json:string><json:string>Machine Learning and Statistics, Microsoft Research, Redmond, USA</json:string><json:string>Computer Science, Stanford University, USA</json:string></affiliations></json:item><json:item><name>Guri Giaever</name><affiliations><json:string>Center for Cellular and Biomolecular Research, University of Toronto, Canada</json:string></affiliations></json:item><json:item><name>Andrew Emili</name><affiliations><json:string>Center for Cellular and Biomolecular Research, University of Toronto, Canada</json:string></affiliations></json:item><json:item><name>Gabe Musso</name><affiliations><json:string>Center for Cellular and Biomolecular Research, University of Toronto, Canada</json:string></affiliations></json:item><json:item><name>Robert Hegele</name><affiliations><json:string>Cardiovascular Genetics Laboratory, Robarts Research Institute, London, Canada</json:string></affiliations></json:item></author><arkIstex>ark:/67375/HCB-SSMVNFLT-L</arkIstex><language><json:string>eng</json:string></language><originalGenre><json:string>OriginalPaper</json:string></originalGenre><abstract>Abstract: A key problem of interest to biologists and medical researchers is the selection of a subset of queries or treatments that provide maximum utility for a population of targets. For example, when studying how gene deletion mutants respond to each of thousands of drugs, it is desirable to identify a small subset of genes that nearly uniquely define a drug ‘footprint’ that provides maximum predictability about the organism’s response to the drugs. As another example, when designing a cocktail of HIV genome sequences to be used as a vaccine, it is desirable to identify a small number of sequences that provide maximum immunological protection to a specified population of recipients. We refer to this task as ‘treatment portfolio design’ and formalize it as a facility location problem. Finding a treatment portfolio is NP-hard in the size of portfolio and number of targets, but a variety of greedy algorithms can be applied. We introduce a new algorithm for treatment portfolio design based on similar insights that made the recently-published affinity propagation algorithm work quite well for clustering tasks. 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when="2008">2008</date></publicationStmt><notesStmt><note type="conference" source="proceedings" scheme="https://content-type.data.istex.fr/ark:/67375/XTP-BFHXPBJJ-3">conference</note><note type="publication-type" subtype="book-series" scheme="https://publication-type.data.istex.fr/ark:/67375/JMC-0G6R5W5T-Z">book-series</note></notesStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">Constructing Treatment Portfolios Using Affinity Propagation</title><author><persName><forename type="first">Delbert</forename><surname>Dueck</surname></persName><affiliation><orgName type="department">Electrical and Computer Engineering</orgName><orgName type="institution">University of Toronto</orgName><address><country key="CA">CANADA</country></address></affiliation></author><author><persName><forename type="first">Brendan</forename><forename type="first">J.</forename><surname>Frey</surname></persName><affiliation><orgName type="department">Electrical and Computer Engineering</orgName><orgName type="institution">University of Toronto</orgName><address><country key="CA">CANADA</country></address></affiliation><affiliation><orgName type="department">Center for Cellular and Biomolecular Research</orgName><orgName type="institution">University of Toronto</orgName><address><country key="CA">CANADA</country></address></affiliation></author><author><persName><forename type="first">Nebojsa</forename><surname>Jojic</surname></persName><affiliation><orgName type="department">Machine Learning and Statistics</orgName><orgName type="institution">Microsoft Research</orgName><address><settlement>Redmond</settlement><country key="US">UNITED STATES</country></address></affiliation></author><author><persName><forename type="first">Vladimir</forename><surname>Jojic</surname></persName><affiliation><orgName type="department">Electrical and Computer Engineering</orgName><orgName type="institution">University of Toronto</orgName><address><country key="CA">CANADA</country></address></affiliation><affiliation><orgName type="department">Machine Learning and Statistics</orgName><orgName type="institution">Microsoft Research</orgName><address><settlement>Redmond</settlement><country key="US">UNITED STATES</country></address></affiliation><affiliation><orgName type="department">Computer Science</orgName><orgName type="institution">Stanford University</orgName><address><country key="US">UNITED STATES</country></address></affiliation></author><author><persName><forename type="first">Guri</forename><surname>Giaever</surname></persName><affiliation><orgName type="department">Center for Cellular and Biomolecular Research</orgName><orgName type="institution">University of Toronto</orgName><address><country key="CA">CANADA</country></address></affiliation></author><author><persName><forename type="first">Andrew</forename><surname>Emili</surname></persName><affiliation><orgName type="department">Center for Cellular and Biomolecular 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For example, when studying how gene deletion mutants respond to each of thousands of drugs, it is desirable to identify a small subset of genes that nearly uniquely define a drug ‘footprint’ that provides maximum predictability about the organism’s response to the drugs. As another example, when designing a cocktail of HIV genome sequences to be used as a vaccine, it is desirable to identify a small number of sequences that provide maximum immunological protection to a specified population of recipients. We refer to this task as ‘treatment portfolio design’ and formalize it as a facility location problem. Finding a treatment portfolio is NP-hard in the size of portfolio and number of targets, but a variety of greedy algorithms can be applied. We introduce a new algorithm for treatment portfolio design based on similar insights that made the recently-published affinity propagation algorithm work quite well for clustering tasks. We demonstrate this method using the two problems described above: selecting a subset of yeast genes that act as a drug-response footprint, and selecting a subset of vaccine sequences that provide maximum epitope coverage for an HIV genome population.</p></abstract><textClass ana="subject"><keywords scheme="book-subject-collection"><list><label>SUCO11645</label><item><term>Computer Science</term></item></list></keywords></textClass><textClass ana="subject"><keywords scheme="book-subject"><list><label>I</label><item><term type="Primary">Computer Science</term></item><label>I16021</label><item><term type="Secondary" subtype="priority-1">Algorithm Analysis and Problem Complexity</term></item><label>I15017</label><item><term type="Secondary" subtype="priority-2">Data Structures</term></item><label>I17028</label><item><term type="Secondary" subtype="priority-3">Discrete Mathematics in Computer Science</term></item><label>I18024</label><item><term type="Secondary" subtype="priority-4">Database Management</term></item><label>I21017</label><item><term type="Secondary" subtype="priority-5">Artificial Intelligence (incl. 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Proceedings</BookSubTitle><BookVolumeNumber>4955</BookVolumeNumber><BookSequenceNumber>4955</BookSequenceNumber><BookDOI>10.1007/978-3-540-78839-3</BookDOI><BookTitleID>160874</BookTitleID><BookPrintISBN>978-3-540-78838-6</BookPrintISBN><BookElectronicISBN>978-3-540-78839-3</BookElectronicISBN><BookChapterCount>41</BookChapterCount><BookCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2008</CopyrightYear></BookCopyright><BookSubjectGroup><BookSubject Code="I" Type="Primary">Computer Science</BookSubject><BookSubject Code="I16021" Priority="1" Type="Secondary">Algorithm Analysis and Problem Complexity</BookSubject><BookSubject Code="I15017" Priority="2" Type="Secondary">Data Structures</BookSubject><BookSubject Code="I17028" Priority="3" Type="Secondary">Discrete Mathematics in Computer Science</BookSubject><BookSubject Code="I18024" Priority="4" Type="Secondary">Database Management</BookSubject><BookSubject Code="I21017" Priority="5" Type="Secondary">Artificial Intelligence (incl. Robotics)</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>Martin</GivenName><FamilyName>Vingron</FamilyName></EditorName><Contact><Email>martin.vingron@molgen.mpg.de</Email></Contact></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>Limsoon</GivenName><FamilyName>Wong</FamilyName></EditorName><Contact><Email>wongls@comp.nus.edu.sg</Email></Contact></Editor></EditorGroup></BookHeader><Chapter ID="Chap31" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingStyle="Unnumbered" TocLevels="0"><ChapterID>31</ChapterID><ChapterDOI>10.1007/978-3-540-78839-3_31</ChapterDOI><ChapterSequenceNumber>31</ChapterSequenceNumber><ChapterTitle Language="En">Constructing Treatment Portfolios Using Affinity Propagation</ChapterTitle><ChapterFirstPage>360</ChapterFirstPage><ChapterLastPage>371</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2008</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><ChapterContext><SeriesID>558</SeriesID><BookID>978-3-540-78839-3</BookID><BookTitle>Research in Computational Molecular Biology</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff1"><AuthorName DisplayOrder="Western"><GivenName>Delbert</GivenName><FamilyName>Dueck</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff1 Aff2"><AuthorName DisplayOrder="Western"><GivenName>Brendan</GivenName><GivenName>J.</GivenName><FamilyName>Frey</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff3"><AuthorName DisplayOrder="Western"><GivenName>Nebojsa</GivenName><FamilyName>Jojic</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff1 Aff3 Aff4"><AuthorName DisplayOrder="Western"><GivenName>Vladimir</GivenName><FamilyName>Jojic</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff2"><AuthorName DisplayOrder="Western"><GivenName>Guri</GivenName><FamilyName>Giaever</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff2"><AuthorName DisplayOrder="Western"><GivenName>Andrew</GivenName><FamilyName>Emili</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff2"><AuthorName DisplayOrder="Western"><GivenName>Gabe</GivenName><FamilyName>Musso</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff5"><AuthorName DisplayOrder="Western"><GivenName>Robert</GivenName><FamilyName>Hegele</FamilyName></AuthorName></Author><Affiliation ID="Aff1"><OrgDivision>Electrical and Computer Engineering</OrgDivision><OrgName>University of Toronto</OrgName><OrgAddress><Country>Canada</Country></OrgAddress></Affiliation><Affiliation ID="Aff2"><OrgDivision>Center for Cellular and Biomolecular Research</OrgDivision><OrgName>University of Toronto</OrgName><OrgAddress><Country>Canada</Country></OrgAddress></Affiliation><Affiliation ID="Aff3"><OrgDivision>Machine Learning and Statistics</OrgDivision><OrgName>Microsoft Research</OrgName><OrgAddress><City>Redmond</City><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff4"><OrgDivision>Computer Science</OrgDivision><OrgName>Stanford University</OrgName><OrgAddress><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff5"><OrgDivision>Cardiovascular Genetics Laboratory</OrgDivision><OrgName>Robarts Research Institute</OrgName><OrgAddress><City>London</City><Country>Canada</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>A key problem of interest to biologists and medical researchers is the selection of a subset of queries or treatments that provide maximum utility for a population of targets. For example, when studying how gene deletion mutants respond to each of thousands of drugs, it is desirable to identify a small subset of genes that nearly uniquely define a drug ‘footprint’ that provides maximum predictability about the organism’s response to the drugs. As another example, when designing a cocktail of HIV genome sequences to be used as a vaccine, it is desirable to identify a small number of sequences that provide maximum immunological protection to a specified population of recipients. We refer to this task as ‘treatment portfolio design’ and formalize it as a facility location problem. Finding a treatment portfolio is NP-hard in the size of portfolio and number of targets, but a variety of greedy algorithms can be applied. We introduce a new algorithm for treatment portfolio design based on similar insights that made the recently-published affinity propagation algorithm work quite well for clustering tasks. We demonstrate this method using the two problems described above: selecting a subset of yeast genes that act as a drug-response footprint, and selecting a subset of vaccine sequences that provide maximum epitope coverage for an HIV genome population.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Constructing Treatment Portfolios Using Affinity Propagation</title></titleInfo><titleInfo type="alternative" contentType="CDATA"><title>Constructing Treatment Portfolios Using Affinity Propagation</title></titleInfo><name type="personal"><namePart type="given">Delbert</namePart><namePart type="family">Dueck</namePart><affiliation>Electrical and Computer Engineering, University of Toronto, Canada</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Brendan</namePart><namePart type="given">J.</namePart><namePart type="family">Frey</namePart><affiliation>Electrical and Computer Engineering, University of Toronto, Canada</affiliation><affiliation>Center for Cellular and Biomolecular Research, University of Toronto, Canada</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Nebojsa</namePart><namePart type="family">Jojic</namePart><affiliation>Machine Learning and Statistics, Microsoft Research, Redmond, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Vladimir</namePart><namePart type="family">Jojic</namePart><affiliation>Electrical and Computer Engineering, University of Toronto, Canada</affiliation><affiliation>Machine Learning and Statistics, Microsoft Research, Redmond, USA</affiliation><affiliation>Computer Science, Stanford University, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Guri</namePart><namePart type="family">Giaever</namePart><affiliation>Center for Cellular and Biomolecular Research, University of Toronto, Canada</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Andrew</namePart><namePart type="family">Emili</namePart><affiliation>Center for Cellular and Biomolecular Research, University of Toronto, Canada</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Gabe</namePart><namePart type="family">Musso</namePart><affiliation>Center for Cellular and Biomolecular Research, University of Toronto, Canada</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Robert</namePart><namePart type="family">Hegele</namePart><affiliation>Cardiovascular Genetics Laboratory, Robarts Research Institute, London, Canada</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre displayLabel="OriginalPaper" authority="ISTEX" authorityURI="https://content-type.data.istex.fr" type="conference" valueURI="https://content-type.data.istex.fr/ark:/67375/XTP-BFHXPBJJ-3">conference</genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2008</dateIssued><copyrightDate encoding="w3cdtf">2008</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><abstract lang="en">Abstract: A key problem of interest to biologists and medical researchers is the selection of a subset of queries or treatments that provide maximum utility for a population of targets. For example, when studying how gene deletion mutants respond to each of thousands of drugs, it is desirable to identify a small subset of genes that nearly uniquely define a drug ‘footprint’ that provides maximum predictability about the organism’s response to the drugs. As another example, when designing a cocktail of HIV genome sequences to be used as a vaccine, it is desirable to identify a small number of sequences that provide maximum immunological protection to a specified population of recipients. We refer to this task as ‘treatment portfolio design’ and formalize it as a facility location problem. Finding a treatment portfolio is NP-hard in the size of portfolio and number of targets, but a variety of greedy algorithms can be applied. We introduce a new algorithm for treatment portfolio design based on similar insights that made the recently-published affinity propagation algorithm work quite well for clustering tasks. We demonstrate this method using the two problems described above: selecting a subset of yeast genes that act as a drug-response footprint, and selecting a subset of vaccine sequences that provide maximum epitope coverage for an HIV genome population.</abstract><relatedItem type="host"><titleInfo><title>Research in Computational Molecular Biology</title><subTitle>12th Annual International Conference, RECOMB 2008, Singapore, March 30 - April 2, 2008. Proceedings</subTitle></titleInfo><name type="personal"><namePart type="given">Martin</namePart><namePart type="family">Vingron</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Limsoon</namePart><namePart type="family">Wong</namePart><role><roleTerm type="text">editor</roleTerm></role></name><genre type="book-series" authority="ISTEX" authorityURI="https://publication-type.data.istex.fr" valueURI="https://publication-type.data.istex.fr/ark:/67375/JMC-0G6R5W5T-Z">book-series</genre><originInfo><publisher>Springer</publisher><copyrightDate encoding="w3cdtf">2008</copyrightDate><issuance>monographic</issuance></originInfo><subject><genre>Book-Subject-Collection</genre><topic authority="SpringerSubjectCodes" authorityURI="SUCO11645">Computer Science</topic></subject><subject><genre>Book-Subject-Group</genre><topic authority="SpringerSubjectCodes" authorityURI="I">Computer Science</topic><topic authority="SpringerSubjectCodes" authorityURI="I16021">Algorithm Analysis and Problem Complexity</topic><topic authority="SpringerSubjectCodes" authorityURI="I15017">Data Structures</topic><topic authority="SpringerSubjectCodes" authorityURI="I17028">Discrete Mathematics in Computer Science</topic><topic authority="SpringerSubjectCodes" authorityURI="I18024">Database Management</topic><topic authority="SpringerSubjectCodes" authorityURI="I21017">Artificial Intelligence (incl. Robotics)</topic><topic authority="SpringerSubjectCodes" authorityURI="I23050">Computational Biology/Bioinformatics</topic></subject><identifier type="DOI">10.1007/978-3-540-78839-3</identifier><identifier type="ISBN">978-3-540-78838-6</identifier><identifier type="eISBN">978-3-540-78839-3</identifier><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="BookTitleID">160874</identifier><identifier type="BookID">978-3-540-78839-3</identifier><identifier type="BookChapterCount">41</identifier><identifier type="BookVolumeNumber">4955</identifier><identifier type="BookSequenceNumber">4955</identifier><part><date>2008</date><detail type="volume"><number>4955</number><caption>vol.</caption></detail><extent unit="pages"><start>360</start><end>371</end></extent></part><recordInfo><recordOrigin>Springer-Verlag Berlin Heidelberg, 2008</recordOrigin></recordInfo></relatedItem><relatedItem type="series"><titleInfo><title>Lecture Notes in Computer Science</title></titleInfo><originInfo><publisher>Springer</publisher><copyrightDate encoding="w3cdtf">2008</copyrightDate><issuance>serial</issuance></originInfo><relatedItem type="constituent"><titleInfo><title>Lecture Notes in Bioinformatics</title></titleInfo><name type="personal"><namePart type="given">Sorin</namePart><namePart type="family">Istrail</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Pavel</namePart><namePart type="family">Pevzner</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Michael</namePart><namePart type="given">S.</namePart><namePart type="family">Waterman</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Martin</namePart><namePart type="family">Vingron</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Limsoon</namePart><namePart type="family">Wong</namePart><role><roleTerm type="text">editor</roleTerm></role></name><genre type="sub-series"></genre><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="SubSeriesID">5381</identifier></relatedItem><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="SeriesID">558</identifier><recordInfo><recordOrigin>Springer-Verlag Berlin Heidelberg, 2008</recordOrigin></recordInfo></relatedItem><identifier type="istex">4E2DB7E9E3E0FA02EC2790BF840D404B35FFA7E1</identifier><identifier type="ark">ark:/67375/HCB-SSMVNFLT-L</identifier><identifier type="DOI">10.1007/978-3-540-78839-3_31</identifier><identifier type="ChapterID">31</identifier><identifier type="ChapterID">Chap31</identifier><accessCondition type="use and reproduction" contentType="copyright">Springer-Verlag Berlin Heidelberg, 2008</accessCondition><recordInfo><recordContentSource authority="ISTEX" authorityURI="https://loaded-corpus.data.istex.fr" valueURI="https://loaded-corpus.data.istex.fr/ark:/67375/XBH-RLRX46XW-4">springer</recordContentSource><recordOrigin>Springer-Verlag Berlin Heidelberg, 2008</recordOrigin></recordInfo></mods><json:item><extension>json</extension><original>false</original><mimetype>application/json</mimetype><uri>https://api.istex.fr/ark:/67375/HCB-SSMVNFLT-L/record.json</uri></json:item></metadata></istex></record>Pour manipuler ce document sous Unix (Dilib)
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