Multi-view multi-task learning for drug sensitivity prediction

author: Samuel Kaski, Department of Information and Computer Science, Aalto University
published: Oct. 6, 2014,   recorded: December 2013,   views: 1925

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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.

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