title: Identification of mutated driver pathways in cancer using a multi-objective optimization model.
abstract: New-generation high-throughput technologies, including next-generation sequencing technology, have been extensively applied to solve biological problems. As a result, large cancer genomics projects such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium are producing large amount of rich and diverse data in multiple cancer types. The identification of mutated driver genes and driver pathways from these data is a significant challenge. Genome aberrations in cancer cells can be divided into two types: random 'passenger mutation' and functional 'driver mutation'. In this paper, we introduced a Multi-objective Optimization model based on a Genetic Algorithm (MOGA) to solve the maximum weight submatrix problem, which can be employed to identify driver genes and driver pathways promoting cancer proliferation. The maximum weight submatrix problem defined to find mutated driver pathways is based on two specific properties, i.e., high coverage and high exclusivity. The multi-objective optimization model can adjust the trade-off between high coverage and high exclusivity. We proposed an integrative model by combining gene expression data and mutation data to improve the performance of the MOGA algorithm in a biological context.
authors.affiliation: College of Electrical Engineering and Automation, Anhui University, Hefei, Anhui 230601, China.
authors.forename: Chun-Hou
authors.initials: CH
authors.lastname: Zheng
authors.affiliation: College of Electrical Engineering and Automation, Anhui University, Hefei, Anhui 230601, China.
authors.forename: Wu
authors.initials: W
authors.lastname: Yang
authors.affiliation: State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China.
authors.forename: Yan-Wen
authors.initials: YW
authors.lastname: Chong
authors.affiliation: Institute of Health Sciences, Anhui University, Hefei, Anhui 230601, China.
authors.forename: Jun-Feng
authors.initials: JF
authors.lastname: Xia
mesh_terms: D000465:Algorithms; D006801:Humans; D008962:Models, Theoretical; D009154:Mutation; D009369:Neoplasms / Q000235:genetics*
keywords: Driver mutation; Driver pathways; Genetic algorithm; Integrative model; Multi-objective optimization model