  The pathogenesis of cancer in human is still poorly understood. With the
rapid development of high-throughput sequencing technologies, huge volumes of
cancer genomics data have been generated. Deciphering those data poses great
opportunities and challenges to computational biologists. One of such key
challenges is to distinguish driver mutations, genes as well as pathways from
passenger ones. Mutual exclusivity of gene mutations (each patient has no more
than one mutation in the gene set) has been observed in various cancer types
and thus has been used as an important property of a driver gene set or
pathway. In this article, we aim to review the recent development of
computational models and algorithms for discovering driver pathways or modules
in cancer with the focus on mutual exclusivity-based ones.
