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Molecules 2018, 23(5), 1114;

Identifying Cancer Specific Driver Modules Using a Network-Based Method

School of Computer Science and Technology, Xidian University, Xi’an 710071, China
Author to whom correspondence should be addressed.
Received: 12 March 2018 / Revised: 26 April 2018 / Accepted: 7 May 2018 / Published: 8 May 2018
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Detecting driver modules is a key challenge for understanding the mechanisms of carcinogenesis at the pathway level. Identifying cancer specific driver modules is helpful for interpreting the different principles of different cancer types. However, most methods are proposed to identify driver modules in one cancer, but few methods are introduced to detect cancer specific driver modules. We propose a network-based method to detect cancer specific driver modules (CSDM) in a certain cancer type to other cancer types. We construct the specific network of a cancer by combining specific coverage and mutual exclusivity in all cancer types, to catch the specificity of the cancer at the pathway level. To illustrate the performance of the method, we apply CSDM on 12 TCGA cancer types. When we compare CSDM with SpeMDP and HotNet2 with regard to specific coverage and the enrichment of GO terms and KEGG pathways, CSDM is more accurate. We find that the specific driver modules of two different cancers have little overlap, which indicates that the driver modules detected by CSDM are specific. Finally, we also analyze three specific driver modules of BRCA, BLCA, and LAML intersecting with well-known pathways. The source code of CSDM is freely accessible at View Full-Text
Keywords: cancer; network; driver module; specific driver module; driver gene cancer; network; driver module; specific driver module; driver gene

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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 (CC BY 4.0).

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Li, F.; Gao, L.; Wang, P.; Hu, Y. Identifying Cancer Specific Driver Modules Using a Network-Based Method. Molecules 2018, 23, 1114.

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