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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">A Parallel Genetic Algorithm to Discover Patterns in Genetic Markers that Indicate Predisposition to Multifactorial Disease</title><author><name sortKey="Rausch, Tobias" sort="Rausch, Tobias" uniqKey="Rausch T" first="Tobias" last="Rausch">Tobias Rausch</name><affiliation><nlm:aff id="A1">Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT 84112, USA</nlm:aff></affiliation><affiliation><nlm:aff id="A2">Hasso-Plattner-Institute, University of Potsdam, 14482 Potsdam, Germany UT 84112, USA</nlm:aff></affiliation></author><author><name sortKey="Thomas, Alun" sort="Thomas, Alun" uniqKey="Thomas A" first="Alun" last="Thomas">Alun Thomas</name><affiliation><nlm:aff id="A1">Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT 84112, USA</nlm:aff></affiliation></author><author><name sortKey="Camp, Nicola J" sort="Camp, Nicola J" uniqKey="Camp N" first="Nicola J." last="Camp">Nicola J. Camp</name><affiliation><nlm:aff id="A1">Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT 84112, USA</nlm:aff></affiliation></author><author><name sortKey="Cannon Albright, Lisa A" sort="Cannon Albright, Lisa A" uniqKey="Cannon Albright L" first="Lisa A." last="Cannon-Albright">Lisa A. Cannon-Albright</name><affiliation><nlm:aff id="A1">Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT 84112, USA</nlm:aff></affiliation></author><author><name sortKey="Facelli, Julio C" sort="Facelli, Julio C" uniqKey="Facelli J" first="Julio C." last="Facelli">Julio C. Facelli</name><affiliation><nlm:aff id="A1">Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT 84112, USA</nlm:aff></affiliation><affiliation><nlm:aff id="A3">Center for High Performance Computing, University of Utah, Salt Lake City, UT 84112, USA</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">18547558</idno><idno type="pmc">2532987</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2532987</idno><idno type="RBID">PMC:2532987</idno><idno type="doi">10.1016/j.compbiomed.2008.04.011</idno><date when="2008">2008</date><idno type="wicri:Area/Pmc/Corpus">000151</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">A Parallel Genetic Algorithm to Discover Patterns in Genetic Markers that Indicate Predisposition to Multifactorial Disease</title><author><name sortKey="Rausch, Tobias" sort="Rausch, Tobias" uniqKey="Rausch T" first="Tobias" last="Rausch">Tobias Rausch</name><affiliation><nlm:aff id="A1">Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT 84112, USA</nlm:aff></affiliation><affiliation><nlm:aff id="A2">Hasso-Plattner-Institute, University of Potsdam, 14482 Potsdam, Germany UT 84112, USA</nlm:aff></affiliation></author><author><name sortKey="Thomas, Alun" sort="Thomas, Alun" uniqKey="Thomas A" first="Alun" last="Thomas">Alun Thomas</name><affiliation><nlm:aff id="A1">Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT 84112, USA</nlm:aff></affiliation></author><author><name sortKey="Camp, Nicola J" sort="Camp, Nicola J" uniqKey="Camp N" first="Nicola J." last="Camp">Nicola J. Camp</name><affiliation><nlm:aff id="A1">Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT 84112, USA</nlm:aff></affiliation></author><author><name sortKey="Cannon Albright, Lisa A" sort="Cannon Albright, Lisa A" uniqKey="Cannon Albright L" first="Lisa A." last="Cannon-Albright">Lisa A. Cannon-Albright</name><affiliation><nlm:aff id="A1">Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT 84112, USA</nlm:aff></affiliation></author><author><name sortKey="Facelli, Julio C" sort="Facelli, Julio C" uniqKey="Facelli J" first="Julio C." last="Facelli">Julio C. Facelli</name><affiliation><nlm:aff id="A1">Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT 84112, USA</nlm:aff></affiliation><affiliation><nlm:aff id="A3">Center for High Performance Computing, University of Utah, Salt Lake City, UT 84112, USA</nlm:aff></affiliation></author></analytic><series><title level="j">Computers in biology and medicine</title><idno type="ISSN">0010-4825</idno><imprint><date when="2008">2008</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p id="P1">This paper describes a novel algorithm to analyze genetic linkage data using pattern recognition techniques and genetic algorithms (GA). The method allows a search for regions of the chromosome that may contain genetic variations that jointly predispose individuals for a particular disease. The method uses correlation analysis, filtering theory and genetic algorithms (GA) to achieve this goal. Because current genome scans use from hundreds to hundreds of thousands of markers, two versions of the method have been implemented. The first is an exhaustive analysis version that can be used to visualize, explore, and analyze small genetic data sets for two marker correlations; the second is a GA version, which uses a parallel implementation allowing searches of higher-order correlations in large data sets. Results on simulated data sets indicate that the method can be informative in the identification of major disease loci and gene-gene interactions in genome-wide linkage data and that further exploration of these techniques is justified. The results presented for both variants of the method show that it can help genetic epidemiologists to identify promising combinations of genetic factors that might predispose to complex disorders. In particular, the correlation analysis of IBD expression patterns might hint to possible gene-gene interactions and the filtering might be a fruitful approach to distinguish true correlation signals from noise.</p></div></front></TEI><pmc article-type="research-article" xml:lang="EN"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
<pmc-dir>properties manuscript</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-journal-id">1250250</journal-id><journal-id journal-id-type="pubmed-jr-id">3132</journal-id><journal-id journal-id-type="nlm-ta">Comput Biol Med</journal-id><journal-title>Computers in biology and medicine</journal-title><issn pub-type="ppub">0010-4825</issn></journal-meta><article-meta><article-id pub-id-type="pmid">18547558</article-id><article-id pub-id-type="pmc">2532987</article-id><article-id pub-id-type="manuscript">NIHMS59060</article-id><article-id pub-id-type="doi">10.1016/j.compbiomed.2008.04.011</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>A Parallel Genetic Algorithm to Discover Patterns in Genetic Markers that Indicate Predisposition to Multifactorial Disease</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Rausch</surname><given-names>Tobias</given-names></name><xref ref-type="aff" rid="A1">a</xref><xref ref-type="aff" rid="A2">b</xref></contrib><contrib contrib-type="author"><name><surname>Thomas</surname><given-names>Alun</given-names></name><xref ref-type="aff" rid="A1">a</xref></contrib><contrib contrib-type="author"><name><surname>Camp</surname><given-names>Nicola J.</given-names></name><xref ref-type="aff" rid="A1">a</xref></contrib><contrib contrib-type="author"><name><surname>Cannon-Albright</surname><given-names>Lisa A.</given-names></name><xref ref-type="aff" rid="A1">a</xref></contrib><contrib contrib-type="author"><name><surname>Facelli</surname><given-names>Julio C.</given-names></name><xref ref-type="aff" rid="A1">a</xref><xref ref-type="aff" rid="A3">c</xref><xref ref-type="corresp" rid="cor1">*</xref></contrib></contrib-group><aff id="A1"><label>a</label>Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT 84112, USA</aff><aff id="A2"><label>b</label>Hasso-Plattner-Institute, University of Potsdam, 14482 Potsdam, Germany UT 84112, USA</aff><aff id="A3"><label>c</label>Center for High Performance Computing, University of Utah, Salt Lake City, UT 84112, USA</aff><author-notes><corresp id="cor1"><label>*</label>Corresponding author: Julio C. Facelli, Department of Biomedical Informatics, University of Utah School of Medicine, 26 South 2000 East - Room 5775 HSEB, Salt Lake City, UT 84112-5750, Phone: (801) 581-4080, FAX: (801) 581-4297, Email: <email>Julio.Facelli@utah.edu</email></corresp></author-notes><pub-date pub-type="nihms-submitted"><day>16</day><month>7</month><year>2008</year></pub-date><pub-date pub-type="epub"><day>10</day><month>6</month><year>2008</year></pub-date><pub-date pub-type="ppub"><month>7</month><year>2008</year></pub-date><pub-date pub-type="pmc-release"><day>1</day><month>7</month><year>2009</year></pub-date><volume>38</volume><issue>7</issue><fpage>826</fpage><lpage>836</lpage><abstract><p id="P1">This paper describes a novel algorithm to analyze genetic linkage data using pattern recognition techniques and genetic algorithms (GA). The method allows a search for regions of the chromosome that may contain genetic variations that jointly predispose individuals for a particular disease. The method uses correlation analysis, filtering theory and genetic algorithms (GA) to achieve this goal. Because current genome scans use from hundreds to hundreds of thousands of markers, two versions of the method have been implemented. The first is an exhaustive analysis version that can be used to visualize, explore, and analyze small genetic data sets for two marker correlations; the second is a GA version, which uses a parallel implementation allowing searches of higher-order correlations in large data sets. Results on simulated data sets indicate that the method can be informative in the identification of major disease loci and gene-gene interactions in genome-wide linkage data and that further exploration of these techniques is justified. The results presented for both variants of the method show that it can help genetic epidemiologists to identify promising combinations of genetic factors that might predispose to complex disorders. In particular, the correlation analysis of IBD expression patterns might hint to possible gene-gene interactions and the filtering might be a fruitful approach to distinguish true correlation signals from noise.</p></abstract><kwd-group><kwd>Gene-Gene Interactions</kwd><kwd>Multifactorial Diseases</kwd><kwd>Pattern Recognition</kwd><kwd>Data Mining</kwd><kwd>Correlation Analysis</kwd><kwd>Parallel Genetic Algorithm</kwd></kwd-group><contract-num rid="RR1">S10 RR017214-01</contract-num><contract-num rid="GM1">R21 GM070710-02</contract-num><contract-num rid="CA1">R01 CA090752-04</contract-num><contract-num rid="CA1">K07 CA098364-05</contract-num><contract-sponsor id="RR1">National Center for Research Resources : NCRR</contract-sponsor><contract-sponsor id="GM1">National Institute of General Medical Sciences : NIGMS</contract-sponsor><contract-sponsor id="CA1">National Cancer Institute : NCI</contract-sponsor></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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