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Integrative cancer genomics: models, algorithms and analysis |
Jinyu CHEN, Shihua ZHANG( ) |
National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China |
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Abstract 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.
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Keywords
cancer genomics
model
algorithm
data integration
bioinformatics
computational biology
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Corresponding Author(s):
Shihua ZHANG
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Just Accepted Date: 21 July 2016
Online First Date: 31 October 2016
Issue Date: 25 May 2017
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