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Prise en compte des connaissances du domaine dans l'analyse transcriptomique : Similarité sémantique, classification fonctionnelle et profils flous : application au cancer colorectal

Abstract : Bioinformatic analyses of transcriptomic data aims to identify genes with variations in their expression level in different tissue samples, for example tissues from healthy versus seek patients, and to characterize these genes on the basis of their functional annotation. In this thesis, I present four contributions for taking into account domain knowledge in these methods. Firstly, I define a new semantic and functional similarity measure which optimally exploits functional annotations from Gene Ontology (GO). Then, I show, thanks to a rigorous evaluation method, that this measure is efficient for the functional classification of genes. In the third contribution, I propose a differential approach with fuzzy assignment for building differential expression profiles (DEPs). I define an algorithm for analyzing overlaps between functional clusters and reference sets such as DEPs here, in order to point out genes that have both similar functional annotation and similar variations in expression. This method is applied to experimental data produced from samples of healthy tissue, colorectal tumor and cancerous cultured cell line. Finally the similarity measure IntelliGO is generalized to another structured vocabulary organized as GO as a rooted directed acyclic graph, with an application concerning the semantic reduction of attributes before mining.
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https://hal.univ-lorraine.fr/tel-01746248
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Sid-Ahmed Benabderrahmane. Prise en compte des connaissances du domaine dans l'analyse transcriptomique : Similarité sémantique, classification fonctionnelle et profils flous : application au cancer colorectal. Informatique [cs]. Université Henri Poincaré - Nancy 1, 2011. Français. ⟨NNT : 2011NAN10097⟩. ⟨tel-01746248⟩

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