Although a lot of methods have been proposed to identify driver genes, how to separate the driver mutations from the passenger mutations is still a challenging problem in cancer genomics. The detection of driver genes with rare mutation and low accuracy is unsolved better. In this study, we present an integrated network-based approach to locate potential driver genes in a cohort of patients. The approach is composed of two steps including a network diffusion step and an aggregated ranking step, which fuses the correlation between the gene mutations and gene expression, the relationship between the mutated genes and the heterogeneous characteristic of the patient mutation. We analyze three cancer datasets including Glioblastoma multiforme, Ovarian cancer and Breast cancer. Our method has not only identified the known driver genes with high-frequency mutations, but also discovered the potential driver genes with a rare mutation. At the same time, validation by literature search and functional enrichment analysis reveal that the predicted genes are obviously related to these three kinds of cancers.

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