Novel Pattern Mining Techniques for Genome-wide Association Studies

Hoang Son Pham 1, 2
1 GenScale - Scalable, Optimized and Parallel Algorithms for Genomics
Inria Rennes – Bretagne Atlantique , IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE
2 LACODAM - Large Scale Collaborative Data Mining
Inria Rennes – Bretagne Atlantique , IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE
Abstract : Genome-wide association studies (GWAS) is designed to discover single nucleotide polymorphism (SNP) combinations associated with diseases. Once new genetic associations are identified, they can be used to develop better strategies to detect, treat and prevent the diseases. Recently, GWAS has been tackled with discriminative pattern mining algorithms. However, discovering of SNP combinations in large genetic variant datasets remains challenging. To address these challenges this thesis advances the state-of-the-art of discriminative pattern mining techniques to discover SNP combinations associated with interesting phenotype. Different solutions have been proposed in this thesis. They focus on major problems of GWAS such as association strength evaluation, SNP combinations discovery and interesting SNP combinations visualization. The proposed solutions in this thesis are also promising for other tasks of bioinformatics such as differential gene expression discovery, phosphorylation motifs detection and regulatory motif combination mining.
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Submitted on : Monday, December 25, 2017 - 11:30:44 AM
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Hoang Son Pham. Novel Pattern Mining Techniques for Genome-wide Association Studies. Bioinformatics [q-bio.QM]. IRISA, equipe GENSCALE, 2017. English. ⟨tel-01672442⟩

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