Machine learning for precision medicine

The development of DNA sequencing technologies allows us to collect large amounts of molecular data about the genome of each individual, and opens the possibility to predict drug response or evaluate the risk of various diseases from one's molecular identity. It also raises statistical and computational challenges, as the quantity of data collected per sample is usually far larger than the number of samples available to estimate predictive models. In this talk I will discuss some of these challenges and describe a few methods to attack them through regularization or change of representation, illustrated on applications in cancer prognosis and drug response prediction from gene expression and somatic mutations