INGI Seminar

Novel Pattern Mining Techniques for Genome-wide Association Studies

by Hoang Son Pham, Post-Doc UCLouvain

Genome-wide association studies (GWAS) are 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 ofdiscriminative pattern mining techniques to discover SNP combinations associated with interesting phenotypes. 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.

Currently, I am working on the INTiMals project with professor Kim Mens and professor Siegfried Nijssen at INGI. I did my Phd in IRISA/INRIA Lab, University of Rennes 1, France, and was part of the Genscale Bioinformatics team and Lacodam (data mining team). Before doing PhD, I worked as an IT engineer at Can Tho University for 3 years, as a lecturer of IT department and vice dean of science and international relations epartment at Can Tho economic college for 6 years.