Prof. Dr. Karsten Borgwardt talk
Date: 21.09.18 Time: 15.15 - 16.15 Room: ETH HG G 19.1 CANCELLED
One key challenge in Machine Learning in Medicine is Association Mapping: to link genetic properties of patients to disease risk, progression and therapy success, in order to then exploit this knowledge for improved diagnosis, prognosis and treatment. Disappointingly, for most complex diseases, current feature selection methods have failed to discover strong associations. One possible explanation is that the vast majority of current methods ignores disease-related interactions between genetic properties - combinations of genome variants that jointly affect a disease. The difficulty in exploring these interactions through Combinatorial Association Mapping stems from the combinatorial explosion of the candidate space, which grows exponentially with the number of interacting loci. This leads both to an enormous computational efficiency problem and a severe multiple testing problem. Ignoring this multiple testing problem may lead to millions of false positive associations; accounting for it may lead to a complete loss of statistical power. For this reason, statistically sound and efficient Combinatorial Association Mapping was long deemed an unsolvable problem. In this talk, we will describe our recent progress in solving this problem of Combinatorial Association Mapping, and we will give an outlook on how our new association mapping algorithms will be applied in the “Personalized Swiss Sepsis Study”, as part of the Swiss Personalized Health Network (SPHN).