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

摘要: Motivation Several molecular events are known to be cancer-related, including genomic aberrations, hypermethylation of gene promoter regions and differential expression microRNAs. These aberration very heterogeneous across tumors it is poorly understood how they affect the makeup cell, transcriptome proteome. Protein interaction networks can help decode functional relationship between changes in protein expression. Results We developed NetICS (Network-based Integration Multi-omics Data), a new graph diffusion-based method for prioritizing cancer genes by integrating diverse data types on directed network. prioritizes their mediator effect, defined as proximity upstream downstream differentially expressed proteins an Genes prioritized individual samples separately integrated using robust rank aggregation technique. provides comprehensive computational framework that aid explaining heterogeneity convergence common proteins. demonstrate NetICS' competitive performance predicting generating lists TCGA from five types. Availability implementation available at https://github.com/cbg-ethz/netics. Supplementary information Bioinformatics online.