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Abstract: Motivation: Uncovering the genomic causes of cancer, known as cancer drivergenes, is a fundamental task in biomedical research. Cancer driver genes drivethe development and progression of cancer, thus identifying cancer driver genesand their regulatory mechanism is crucial to the design of cancer treatment andintervention. Many computational methods, which take the advantages of computerscience and data science, have been developed to utilise multiple types ofgenomic data to reveal cancer drivers and their regulatory mechanism behindcancer development and progression. Due to the complexity of the mechanisticinsight of cancer genes in driving cancer and the fast development of thefield, it is necessary to have a comprehensive review about the currentcomputational methods for discovering different types of cancer drivers.Results: We survey computational methods for identifying cancer drivers fromgenomic data. We categorise the methods into three groups, methods for singledriver identification, methods for driver module identification, and methodsfor identifying personalised cancer drivers. We also conduct a case study tocompare the performance of the current methods. We further analyse theadvantages and limitations of the current methods, and discuss the challengesand future directions of the topic. In addition, we investigate the resourcesfor discovering and validating cancer drivers in order to provide a one-stopreference of the tools to facilitate cancer driver discovery. The ultimate goalof the paper is to help those interested in the topic to establish a solidbackground to carry out further research in the field.
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From: Vu Viet Hoang Pham [view email]
[v1]Thu, 2 Jul 2020 05:18:08 UTC (1,537 KB)
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