Network-based integration of multi-omics data for prioritizing cancer genes.

ABSTRACT: Motivation:Several molecular events are known to be cancer-related, including genomic aberrations, hypermethylation of gene promoter regions and differential expression of microRNAs. These aberration events are very heterogeneous across tumors and it is poorly understood how they affect the molecular makeup of the cell, including the transcriptome and proteome. Protein interaction networks can help decode the functional relationship between aberration events and changes in gene and protein expression. Results:We developed NetICS (Network-based Integration of Multi-omics Data), a new graph diffusion-based method for prioritizing cancer genes by integrating diverse molecular data types on a directed functional interaction network. NetICS prioritizes genes by their mediator effect, defined as

SUBMITTER: Dimitrakopoulos C 

PROVIDER: S-EPMC6041755 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature