Optimal methods could effectively improve the accuracy of predicting and identifying
candidate driver genes. Various computational methods based on mutational frequency …

Cancer driver genes, ie, oncogenes and tumor suppressor genes, are involved in the
acquisition of important functions in tumors, providing a selective growth advantage …

Identifying the genes and mutations that drive the emergence of tumors is a critical step to
improving our understanding of cancer and identifying new directions for disease diagnosis …

The accumulating availability of next-generation sequencing data offers an opportunity to
pinpoint driver genes that are causally implicated in oncogenesis through computational …

Although rapid progress has been made in computational approaches for prioritizing cancer
driver genes, research is far from achieving the ultimate goal of discovering a complete …

It is complicated to identify cancer-causing mutations. The recurrence of a mutation in
patients remains one of the most reliable features of mutation driver status. However, some …

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 …

Existing cancer driver prediction methods are based on very different assumptions and each
of them can detect only a particular subset of driver genes. Here we perform a …

Background Cancer is a complex disease which is characterized by the accumulation of
genetic alterations during the patient's lifetime. With the development of the next-generation …

Identification of a few cancer driver mutation genes from a much larger number of passenger
mutation genes in cancer samples remains a highly challenging task. Here, a novel method …