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<identifier>oai:HAL:hal-00521936v1</identifier>
<datestamp>2018-01-11</datestamp>
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<metadata><dc>
<publisher>HAL CCSD</publisher>
<title lang=en>Clustering Gene Expression Using Graphs Separators</title>
<creator>Kaba, Bangaly</creator>
<creator>Pinet, Nicolas</creator>
<creator>Lelandais, Gaëlle</creator>
<creator>Sigayret, Alain</creator>
<creator>Berry, Anne</creator>
<contributor>Laboratoire d'Informatique, de Modélisation et d'optimisation des Systèmes (LIMOS) ; Centre National de la Recherche Scientifique (CNRS) - Sigma CLERMONT (Sigma CLERMONT) - Université d'Auvergne - Clermont-Ferrand I (UdA) - Université Blaise Pascal - Clermont-Ferrand 2 (UBP)</contributor>
<contributor>Bioinformatique génomique et moléculaire ; Université Paris Diderot - Paris 7 (UPD7) - Institut National de la Santé et de la Recherche Médicale (INSERM)</contributor>
<contributor>Protéines de la membrane érythrocytaire et homologues non-érythroides ; Université des Antilles et de la Guyane (UAG) - Institut National de la Transfusion Sanguine [Paris] (INTS) - Université Paris Diderot - Paris 7 (UPD7) - Université de la Réunion (UR) - Institut National de la Santé et de la Recherche Médicale (INSERM)</contributor>
<description>International audience</description>
<source>ISSN: 1386-6338</source>
<source>EISSN: 1434-3207</source>
<source>In Silico Biology</source>
<publisher>IOS Press</publisher>
<identifier>hal-00521936</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-00521936</identifier>
<source>https://hal.archives-ouvertes.fr/hal-00521936</source>
<source>In Silico Biology, IOS Press, 2007, 7-2007, pp.#0031</source>
<language>en</language>
<subject lang=en>clustering method</subject>
<subject lang=en>microarray</subject>
<subject lang=en>graph decomposition</subject>
<subject lang=en>threshold family of graphs</subject>
<subject lang=en>expression profile</subject>
<subject>[INFO.INFO-DS] Computer Science [cs]/Data Structures and Algorithms [cs.DS]</subject>
<subject>[INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM]</subject>
<subject>[SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]</subject>
<type>info:eu-repo/semantics/article</type>
<type>Journal articles</type>
<description lang=en>Recent work has used graphs to modelize expression data from microarray experiments, in view of partitioning the genes into clusters. In this paper, we introduce the use of a decomposition by clique separators. Our aim is to improve the classical clustering methods in two ways: first we want to allow an overlap between clusters, as this seems biologically sound, and second we want to be guided by the structure of the graph to define the number of clusters. We test this approach with a well-known yeast database (Saccharomyces cerevisiae). Our results are good, as the expression profiles of the clusters we find are very coherent. Moreover, we are able to organize into another graph the clusters we find, and order them in a fashion which turns out to respect the chronological order defined by the the sporulation process.</description>
<date>2007</date>
</dc>
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