PT  - JOURNAL ARTICLE
AU  - Subarna Sinha, Jr
AU  - David L. Dill
TI  - Mining Large Heterogeneous Cancer Data Sets Using Boolean Implications
AID  - 10.1101/045021
DP  - 2016 Jan 01
TA  - bioRxiv
PG  - 045021
4099  - http://biorxiv.org/content/early/2016/03/21/045021.short
4100  - http://biorxiv.org/content/early/2016/03/21/045021.full
AB  - 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 their usage in mining associations from large, heterogeneous cancer data sets. Next, we illustrate how Boolean implications were used to discover a new causal association between a mutation and aberrant DNA hypermethylation in acute myeloid leukemia as well as the therapeutic implications of this discovery. We conclude with a brief description of how Boolean implications can be extracted from a given data set.