In the past decade, the remarkable development of high-throughput sequencing technology accelerates the generation of large amount of multiple dimensional data such as genomic, epigenomic, transcriptomic and proteomic data. The comprehensive data make it possible to understand the underlying mechanisms of biology and disease such as cancer systematically. It also provides great challenges for computational cancer genomics due to the complexity, scale and noise of data. In this article, we aim to review the recent developments and progresses of computational models, algorithms and analysis of complex data in cancer genomics. These topics of this paper include the identification of driver mutations, the genetic heterogeneity analysis, genomic markers discovery of drug response, pan-cancer scale analysis and so on.
['Jinyu Chen', 'Shihua Zhang']
Integrative cancer genomics: models, algorithms and analysis