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The cost of drug development has dramatically increased in the last decades, with the number new drugs approved per billion US dollars spent on R&amp;D halving every year or less. The selection and prioritization of targets is one the most influential decisions in drug discovery. Here we present a Gaussian Process model for the prioritization of drug targets cast as a problem of learning with only positive and unlabeled examples.

Adaptive one-class Gaussian processes allow accurate prioritization of oncology drug targets