【主 题】A novel and efficient algorithm for de novo discovery of mutated driver pathways in cancer
【时 间】 2017年03月31日（星期五）15:00－16:00
【地 点】 上海财经大学统计与管理学院大楼1208室
【摘 要】Next-generation sequencing studies on cancer somatic mutations have discovered that driver mutations tend to appear in most tumor samples, but they barely overlap in any single tumor sample, presumably because a single driver mutation can perturb the whole pathway. Based on the corresponding new concepts of coverage and mutual exclusivity, new methods can be designed for de novo discovery of mutated driver path-ways in cancer. Since the computational problem is a combinatorial optimization with an objective function involving a discontinuous indicator function in high dimension, many existing optimization algorithms, such as a brute force enumeration, gradient descent and Newton's methods, are not practically feasible or directly applicable. We develop a new algorithm based on a novel formulation of the problem as non-convex programming and non-convex regularization. The method is computationally more efficient, effective and scalable than existing Monte Carlo searching and several other algorithms, which have been applied to The Cancer Genome Atlas (TCGA) project. We demonstrate its promising performance with application to some cancer datasets to discover de novo mutated driver pathways.