Ranking cancer drivers via betweenness-based outlier detection and random walks.

ABSTRACT:

Background

Recent cancer genomic studies have generated detailed molecular data on a large number of cancer patients. A key remaining problem in cancer genomics is the identification of driver genes.

Results

We propose BetweenNet, a computational approach that integrates genomic data with a protein-protein interaction network to identify cancer driver genes. BetweenNet utilizes a measure based on betweenness centrality on patient specific networks to identify the so-called outlier genes that correspond to dysregulated genes for each patient. Setting up the relationship between the mutated genes and the outliers through a bipartite graph, it employs a random-walk process on the graph, which provides the final prioritization of the mutated genes. We compare BetweenNet against

SUBMITTER: Erten C 

PROVIDER: S-EPMC7877041 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature