MECoRank: cancer driver genes discovery simultaneously evaluating the impact of SNVs and differential expression on transcriptional networks.

ABSTRACT: BACKGROUND:Although there are huge volumes of genomic data, how to decipher them and identify driver events is still a challenge. The current methods based on network typically use the relationship between genomic events and consequent changes in gene expression to nominate putative driver genes. But there may exist some relationships within the transcriptional network. METHODS:We developed MECoRank, a novel method that improves the recognition accuracy of driver genes. MECoRank is based on bipartite graph to propagates the scores via an iterative process. After iteration, we will obtain a ranked gene list for each patient sample. Then, we applied the Condorcet voting method to determine the most impactful drivers in a population. RESULTS:We applied MECoRank to three cancer datasets to rev

SUBMITTER: Hui Y 

PROVIDER: S-EPMC6936061 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature