uKIN Combines New and Prior Information with Guided Network Propagation to Accurately Identify Disease Genes.

TitleuKIN Combines New and Prior Information with Guided Network Propagation to Accurately Identify Disease Genes.
Publication TypeJournal Article
Year of Publication2020
AuthorsHristov, BH, Chazelle, B, Singh, M
JournalCell Syst
Volume10
Issue6
Pagination470-479.e3
Date Published2020 Jun 24
ISSN2405-4720
Abstract

Protein interaction networks provide a powerful framework for identifying genes causal for complex genetic diseases. Here, we introduce a general framework, uKIN, that uses prior knowledge of disease-associated genes to guide, within known protein-protein interaction networks, random walks that are initiated from newly identified candidate genes. In large-scale testing across 24 cancer types, we demonstrate that our network propagation approach for integrating both prior and new information not only better identifies cancer driver genes than using either source of information alone but also readily outperforms other state-of-the-art network-based approaches. We also apply our approach to genome-wide association data to identify genes functionally relevant for several complex diseases. Overall, our work suggests that guided network propagation approaches that utilize both prior and new data are a powerful means to identify disease genes. uKIN is freely available for download at: https://github.com/Singh-Lab/uKIN.

DOI10.1016/j.cels.2020.05.008
Alternate JournalCell Syst
PubMed ID32684276