  Motivation: Uncovering the genomic causes of cancer, known as cancer driver
genes, is a fundamental task in biomedical research. Cancer driver genes drive
the development and progression of cancer, thus identifying cancer driver genes
and their regulatory mechanism is crucial to the design of cancer treatment and
intervention. Many computational methods, which take the advantages of computer
science and data science, have been developed to utilise multiple types of
genomic data to reveal cancer drivers and their regulatory mechanism behind
cancer development and progression. Due to the complexity of the mechanistic
insight of cancer genes in driving cancer and the fast development of the
field, it is necessary to have a comprehensive review about the current
computational methods for discovering different types of cancer drivers.
Results: We survey computational methods for identifying cancer drivers from
genomic data. We categorise the methods into three groups, methods for single
driver identification, methods for driver module identification, and methods
for identifying personalised cancer drivers. We also conduct a case study to
compare the performance of the current methods. We further analyse the
advantages and limitations of the current methods, and discuss the challenges
and future directions of the topic. In addition, we investigate the resources
for discovering and validating cancer drivers in order to provide a one-stop
reference of the tools to facilitate cancer driver discovery. The ultimate goal
of the paper is to help those interested in the topic to establish a solid
background to carry out further research in the field.
