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Machine learning algorithms for making inferences on networks and answering questions in biology and medicine

Venue: Online


An important idea that has emerged recently is that a cell can be viewed as a set of complex networks of interacting bio-molecules and genetic disease is the result of abnormal interactions within these networks. In this talk, I will present novel machine learning algorithms for solving problems in systems biology and medicine that can be phrased in terms of inference in such large-scale networks.

I will begin by describing a method to accurately quantify a distance between disease modules on the human interactome that uses only disease phenotype information. I will then show how this measure can be exploited by a semi-supervised learning algorithm for inferring disease genes for heritable disease. Importantly, our approach allows the prediction of disease genes for diseases for which no disease gene is already known. Finally, I will present a method for the prediction of drug side effects. This algorithm, which is based on matrix factorization, is the first that can predict the frequency of drug side effects in the population.

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