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BioMed Research International
Volume 2014, Article ID 375980, 7 pages
Research Article

Simulated Annealing Based Algorithm for Identifying Mutated Driver Pathways in Cancer

1College of Information and Communication Technology, Qufu Normal University, Rizhao 276826, China
2College of Jia Sixie Agriculture, Weifang University of Science and Technology, Shouguang 262700, China
3College of Electrical Engineering and Automation, Anhui University, Hefei 230000, China
4Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230000, China

Received 20 March 2014; Accepted 13 May 2014; Published 26 May 2014

Academic Editor: Bairong Shen

Copyright © 2014 Hai-Tao Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


With the development of next-generation DNA sequencing technologies, large-scale cancer genomics projects can be implemented to help researchers to identify driver genes, driver mutations, and driver pathways, which promote cancer proliferation in large numbers of cancer patients. Hence, one of the remaining challenges is to distinguish functional mutations vital for cancer development, and filter out the unfunctional and random “passenger mutations.” In this study, we introduce a modified method to solve the so-called maximum weight submatrix problem which is used to identify mutated driver pathways in cancer. The problem is based on two combinatorial properties, that is, coverage and exclusivity. Particularly, we enhance an integrative model which combines gene mutation and expression data. The experimental results on simulated data show that, compared with the other methods, our method is more efficient. Finally, we apply the proposed method on two real biological datasets. The results show that our proposed method is also applicable in real practice.