ArticleCell systems2020
uKIN Combines New and Prior Information with Guided Network Propagation to Accurately Identify Disease Genes.
Article in Cell systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- Network-based machine learning to identify biomarkers for systemic lupus erythematosus.BMC biology · 2026Article
- A Co-essentiality Network of Cancer Driver Genes Better Prioritizes Anticancer Drugs.Genomics, proteomics & bioinformatics · 2025Article
- DiCE: differential centrality-ensemble analysis based on gene expression profiles and protein-protein interaction network.Nucleic acids research · 2025Article
- DiCE: differential centrality-ensemble analysis based on gene expression profiles and protein-protein interaction network.bioRxiv : the preprint server for biology · 2025Article
- CDHu40: a novel marker gene set of neuroendocrine prostate cancer.Briefings in bioinformatics · 2024Article
- Unveiling hidden connections in omics data via pyPARAGON: an integrative hybrid approach for disease network construction.Briefings in bioinformatics · 2024Article
- A gray box framework that optimizes a white box logical model using a black box optimizer for simulating cellular responses to perturbations.Cell reports methods · 2024Article
- PWN: enhanced random walk on a warped network for disease target prioritization.BMC bioinformatics · 2023Article
- Suggesting disease associations for overlooked metabolites using literature from metabolic neighbors.GigaScience · 2022Article
- Interpretable network propagation with application to expanding the repertoire of human proteins that interact with SARS-CoV-2.GigaScience · 2021Article
- Prediction of cancer driver genes through network-based moment propagation of mutation scores.Bioinformatics (Oxford, England) · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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.
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Registered trials
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