INFERENCE OF PERSONALIZED DRUG TARGETS VIA NETWORK PROPAGATION.

We present a computational strategy to simulate drug treatment in a personalized setting. The method is based on integrating patient mutation and differential expression data with a protein-protein interaction network. We test the impact of in-silico deletions of different proteins on the flow of information in the network and use the results to infer potential drug targets.