IEEE/ACM Trans Comput Biol Bioinform. 2018 May-Jun;15(3):988-998. doi: 10.1109/TCBB.2016.2640963. Epub 2016 Dec 15.
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 that 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.
人类癌症的发病机制仍不清楚。随着高通量测序技术的快速发展，产生了大量的癌症基因组学数据。对这些数据进行解读给计算生物学家带来了巨大的机遇和挑战。其中一个关键挑战是将驱动突变、基因和途径与乘客突变、基因和途径区分开来。在各种癌症类型中观察到基因突变的互斥性（每个患者在基因集中的突变不超过一个），因此已被用作驱动基因集或途径的重要特性。本文旨在回顾近年来基于互斥性的用于发现癌症中驱动途径或模块的计算模型和算法的最新进展。
