Novel Pattern Mining Techniques for Genome-wide Association Studies

Hoang Son Pham (IRISA/INRIA lab, University Rennes 1)
Friday, December 22, 2017 - 14:00
Room Metiviers
Talk abstract: 
Discovering high-order SNP combinations associated with diseases is an important task of bioinformatics.
Once  new  genetic associations are identified, they can be used to develop better strategies to detect, treat and prevent the diseases.
Recently, this issue has been effectively tackled with discriminative pattern mining algorithms.
However, the number of SNPs is often very large, discovering of SNP combinations remains many challenges.
To address these challenges this thesis has been advanced the state-of-the-art discriminative pattern mining techniques to discover SNP combinations associated with interesting phenotype.
Different solutions have been proposed in this thesis to tackle GWAS analysis.
These solutions focus on efficient association strength evaluation, statistically significant discriminative SNP combinations discovery and interesting SNP combinations visualization.
The solutions proposed 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.