In this paper, we presented a network-based method, named DriverFinder, by filtering frequently mutated genes just because of their large size, and comparing tumor expression with normal expression data to obtain gene expression outliers which are more likely to be cancer genes. Then greedy algorithm was applied to prioritize candidate driver genes. The proposed method can not only indentify frequently mutated genes, but also novel and infrequently mutated driver genes.
['Pi-Jing Wei', 'Di Zhang', 'Chun-Hou Zheng', 'Junfeng Xia']
Cancer genes discovery based on integtating transcriptomic data and the impact of gene length