Deciphering cancer biology using boolean methods

Abstract

Boolean implications (if-then rules) provide a conceptually simple, uniform and highly scalable way to find associations between pairs of random variables. In this paper, we describe how Boolean implications can be derived from large, heterogeneous cancer data sets. We demonstrate two applications of Boolean implications to discover new actionable insights in cancer biology.