A Bayesian Nonparametric Approach to Integrative Genomics for Cancer Subgroup Discovery
نویسندگان
چکیده
Systematic integration of multiple omics data is a promising approach to identify cancer subgroups. In this project, miRNA and gene expression profiles were jointly analyzed for better defined molecular tumor subtypes by a Bayesian nonparametric method. Kaplan-Meier survival analysis showed that the subgroups identified by the proposed method on two hepatocellular carcinoma cohorts have different survival characteristics. Our proposal is easily extensible to other omics data types such as DNA methylation data and copy number alterations.
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