With the rapid development of deep sequencing technologies, many programs are generating multi-platform genomic profiles (e.g., somatic mutation, DNA methylation, and gene expression) for a large number of tumors. This activity has provided unique opportunities and challenges to stratify tumors and decipher tumor heterogeneity. In this chapter, we summarize several computational methods to address the challenge of tumor stratification with different types of genomic data. We further introduce their applications in emerging large-scale genomic data to show their effectiveness in deciphering tumor heterogeneity and clinical relevance.
Bioinformatics Cancer genomics Cancer subtype Cancer stratification Machine learning Model and algorithm
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This work has been supported by the National Natural Science Foundation of China [No. 11661141019, 61621003, 61422309 and 61379092], the Strategic Priority Research Program of the Chinese Academy of Sciences (CAS) [XDB13040600], and CAS Frontier Science Research Key Project for Top Young Scientist [No. QYZDB-SSW-SYS008].
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