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The underlying relationship between genomic factors and the response of diverse cancer drugs still remains unclear. A number of studies showed that the heterogeneous responses to anticancer treatments of patients were partly associated with their specific changes in gene expression and somatic alterations. The emerging large-scale pharmacogenomic data provide us valuable opportunities to improve existing therapies or to guide early-phase clinical trials of compounds under development. However, how to identify the underlying combinatorial patterns among pharmacogenomics data are still a challenging issue.

Integrative analysis for identifying joint modular patterns of gene-expression and drug-response data