"Belief Propagation Algorithms: From Matching Problems to Network Discovery in Cancer Genomics"
Abstract: We review a certain class of algorithms, belief propagation algorithms, inspired by the study of phase transitions in computationally difficult problems. We show how these algorithms can be used both in the mathematical analysis of relatively simple problems like matching, and in the heuristic analysis of more complex problems. In particular, we show how particular forms of these algorithms can be used to discover pathways in cancer genomics, and to suggest possible drug targets for cancer therapy. These methods give us the ability to share information across multiple patients to help reconstruct highly patient-specific networks and potential treatments.