The Integrative Method Based on the Module-Network for Identifying Driver Genes in Cancer Subtypes.

ABSTRACT: With advances in next-generation sequencing(NGS) technologies, a large number of multiple types of high-throughput genomics data are available. A great challenge in exploring cancer progression is to identify the driver genes from the variant genes by analyzing and integrating multi-types genomics data. Breast cancer is known as a heterogeneous disease. The identification of subtype-specific driver genes is critical to guide the diagnosis, assessment of prognosis and treatment of breast cancer. We developed an integrated frame based on gene expression profiles and copy number variation (CNV) data to identify breast cancer subtype-specific driver genes. In this frame, we employed statistical machine-learning method to select gene subsets and utilized an module-network analysis method to ide

SUBMITTER: Lu X 

PROVIDER: S-EPMC6099653 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature