Limsoon Wong

Limsoon Wong

National University of Singapore

Title: Some opinion and advice on machine learning in population-based genomic medicine

Abstract: Large amounts of genome-wide association studies (GWAS) and other omics studies have been accumulated and analyzed. However, most of the heritability accounting for many complex diseases remains unexplained by variants reported by GWAS. The mystery of this missing heritability maybe due to shortcomings of current approaches' inability to exploit correlation structures in these variations (e.g. functional relationships between genes) while avoiding their potential confounding effects. Deep learning and other forms of machine learning have become a general hammer for finding correlations and making predictions these days. Can these be used (and how) in the context of GWAS and other omics data analysis? In this talk, I will offer some opinions and advice on this question.

Resume: Please refer https://www.comp.nus.edu.sg/cs/bio/wongls/

Biography: Limsoon Wong is Kwan-Im-Thong-Hood-Cho-Temple chair professor of computer science in the School of Computing at the National University of Singapore (NUS). He was also a professor (now honorary) of pathology in the Yong Loo Lin School of Medicine at NUS. Before coming to NUS in 2005, he was the Deputy Executive Director for Research at A*STAR’s Institute for Infocomm Research, where he started his career as a young software engineer in the late 1980s. Limsoon also co-founded Molecular Connections in India the early 2000s; and as this start-up’s chairman, he oversaw its profitable 400x growth over a decade and a half. As a scientist, Limsoon has many well-known results in two distinct fields: database theory and computational biology; and he was inducted in 2013 as a Fellow of the ACM for his seminar works in both fields. These days he works mostly on knowledge discovery technologies and their application to biomedicine, with a current special interest on batch effects in gene expression and proteomic profile analysis.