In the core of personalized medicine is the computational task of predicting drug sensitivities based on genomic information. This is a supervised learning task which can be addressed by a combination of multi-view and multi-task learning. Alternatively, it can be viewed as a structured prediction task or a recommender system. I will discuss an approach which has recently turned out to be successful, Bayesian kernelized multi-view multi-task methods for predicting sensitivities across drug profiles, and its generalizations to matrix factorization with side information.