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

Author(s): Hristov, Borislav H; Chazelle, Bernard; Singh, Mona

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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.
Publication Date: 24-Jun-2020
Citation: Hristov, Borislav H., Bernard Chazelle, and Mona Singh. "uKIN Combines New and Prior Information with Guided Network Propagation to Accurately Identify Disease Genes." Cell Systems 10, no. 6 (2020): 470-479.e3. doi:10.1016/j.cels.2020.05.008
DOI: 10.1016/j.cels.2020.05.008
ISSN: 2405-4712
Pages: 470 - 479.e3
Language: eng
Type of Material: Journal Article
Journal/Proceeding Title: Cell Systems
Version: Final published version. This is an open access article.



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