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...

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Bibliographic Details
Main Authors: Cesim Erten, Aissa Houdjedj, Hilal Kazan
Format: Article
Language:English
Published: BMC 2021-02-01
Series:BMC Bioinformatics
Subjects:
Online Access:https://doi.org/10.1186/s12859-021-03989-w