Dynamically Heuristic Method for Identifying Mutated Driver Pathways in Cancer
Many genomics projects are bringing convenience to the research of identifying driver genes and driver pathways. However, it also brings us the biggest challenge that is how to screen functional mutation and superannuate the unfunctional mutation called as passenger mutation. In our study, we integrate dynamic ant colony optimization into genetic algorithm (DACGA) to identify driver pathways and the problem is equivalent to solve the so-called maximum weight submatrix problem in which driver pathways should satisfy two properties: high coverage and exclusivity. Integrating the two algorithms can make the most use of speed ability and global convergence of genetic algorithm (GA) and positive feedback of ant colony optimization (AC). AC is chose when it approaches stagnation in population of evolution, while GA is chose under other conditions, thus it can dynamically select AC or GA for achieving maximum weight. The proposed method is evaluated on simulated and biological datasets, respectively, and the experimental results indicate that our method is efficient and robust in identifying driver pathways.
KeywordsDynamically heuristic method Driver pathways Driver mutations Maximum weight submatrix problem
This research was supported by the National Natural Science Foundation of China (Grant Nos. 61472467, 60973153 and 61471169) and the Collaboration and Innovation Center for Digital Chinese Medicine of 2011 Project of Colleges and Universities in Hunan Province.
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