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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Acknowledgments
This work was supported by National Natural Science Foundation of China (31301101 and 61272339), the Anhui Provincial Natural Science Foundation (1408085QF106), the Specialized Research Fund for the Doctoral Program of Higher Education (20133401120011), and the Technology Foundation for Selected Overseas Chinese Scholars from Department of Human Resources and Social Security of Anhui Province (No. [2014]-243).
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Yang, W., Xia, J., Zhang, Y., Zheng, CH. (2015). Multi-objective Optimization Method for Identifying Mutated Driver Pathways in Cancer. In: Huang, DS., Jo, KH., Hussain, A. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9226. Springer, Cham. https://doi.org/10.1007/978-3-319-22186-1_57
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DOI: https://doi.org/10.1007/978-3-319-22186-1_57
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