Abstract
Genome aberrations in cancer cells can be divided into two types as random ‘passenger mutation’ and functional ‘driver mutation’. Identifying mutated driver genes and driver pathways from cancer genome sequencing data is one of the greatest challenges. In this paper, we introduced a Multi-Objective optimization model based on Genetic Algorithm (MOGA) to solve the so-called maximum weight submatrix problem, which can be used to identify driver genes and driver pathways in cancer. The maximum weight submatrix problem is built on two specific properties, i.e., high coverage and high exclusivity. Those two properties are considered as two inconsistent objectives in our MOGA algorithm. The results show that our MOGA method is effective in real biological data.
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