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Complexity
Volume 2017, Article ID 4826206, 10 pages
https://doi.org/10.1155/2017/4826206
Research Article

DriverFinder: A Gene Length-Based Network Method to Identify Cancer Driver Genes

1College of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, China
2State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu 210018, China
3Institute of Health Sciences, Anhui University, Hefei, Anhui 230601, China

Correspondence should be addressed to Chun-Hou Zheng; moc.621@99hcgnehz

Received 7 March 2017; Revised 8 June 2017; Accepted 3 July 2017; Published 10 August 2017

Academic Editor: Haiying Wang

Copyright © 2017 Pi-Jing Wei 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.

Abstract

Integration of multi-omics data of cancer can help people to explore cancers comprehensively. However, with a large volume of different omics and functional data being generated, there is a major challenge to distinguish functional driver genes from a sea of inconsequential passenger genes that accrue stochastically but do not contribute to cancer development. In this paper, we present a gene length-based network method, named DriverFinder, to identify driver genes by integrating somatic mutations, copy number variations, gene-gene interaction network, tumor expression, and normal expression data. To illustrate the performance of DriverFinder, it is applied to four cancer types from The Cancer Genome Atlas including breast cancer, head and neck squamous cell carcinoma, thyroid carcinoma, and kidney renal clear cell carcinoma. Compared with some conventional methods, the results demonstrate that the proposed method is effective. Moreover, it can decrease the influence of gene length in identifying driver genes and identify some rare mutated driver genes.