Computational Biology PhD Student
New York University
March 4, 2014
Novel Causal Score for Learning Conjunctive Bayesian Networks
Cancer is a complex disease that involves the interplay of many genes and regulatory factors. Despite this complexity, each specific type of cancer is thought to progress through a sequence of stages, defined by genomic events. Various mathematical models to capture this progression have been proposed. In particular, graphical approaches have been used to model the progression as a path, as a tree, and various types of directed acyclic graphs (DAG). The most recent work considered conjunctive Bayesian networks (CBNs). These model progression through a DAG, in which an event only occurs when all of its causes have occurred. The CBNs are learned with the standard statistical approach of maximum likelihood, under the constraints of monotonicity and conjuntivity. We learn the same model but consider an alternate score based in the philosophical foundations of causality and in the monotonicity of the progression events. Just like log likelihood, our score decomposes into a sum of scores for each node. However, our score distinguishes between edge directions and is thus better suited to this particular type of Bayesian network. We learn the resulting network using GOBNILP, a Bayesian network learning software that optimizes an arbitrary decomposable score by formulating the problem as an integer program and solving it to optimality through linear relaxation and plane cutting.