ArticleBMC systems biology2014
Identifying disease associated genes by network propagation.
Article in BMC systems biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Network Model to Predict Age-Related Transcriptional Reprogramming.Aging cell · 2026Article
- Elastic network modeling of cellular networks unveils sensor and effector genes that control information flow.PLoS computational biology · 2022Article
- Biological mechanisms of aging predict age-related disease co-occurrence in patients.Aging cell · 2022Article
- NetCore: a network propagation approach using node coreness.Nucleic acids research · 2020Article
- NPF:network propagation for protein function prediction.BMC bioinformatics · 2020Article
- Predicting miRNA-based disease-disease relationships through network diffusion on multi-omics biological data.Scientific reports · 2020Article
- Article
- Network Diffusion Approach to Predict LncRNA Disease Associations Using Multi-Type Biological Networks: LION.Frontiers in physiology · 2019Article
- Systematic Evaluation of Molecular Networks for Discovery of Disease Genes.Cell systems · 2018Article
- Probability-based collaborative filtering model for predicting gene-disease associations.BMC medical genomics · 2017Article
- Network propagation in the cytoscape cyberinfrastructure.PLoS computational biology · 2017Article
- NetNorM: Capturing cancer-relevant information in somatic exome mutation data with gene networks for cancer stratification and prognosis.PLoS computational biology · 2017Article
- Network diffusion-based analysis of high-throughput data for the detection of differentially enriched modules.Scientific reports · 2016Article
- A knowledge-based approach for predicting gene-disease associations.Bioinformatics (Oxford, England) · 2016Article
- Systems medicine of inflammaging.Briefings in bioinformatics · 2016Article
- Article
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundGenome-wide association studies have identified many individual genes associated with complex traits. However, pathway and network information have not been fully exploited in searches for genetic determinants, and including this information may increase our understanding of the underlying biology of common diseases.
resultsIn this study, we propose a framework to address this problem in a principled way, with the underlying hypothesis that complex disease operates through multiple connected genes. Associations inferred from GWAS are translated into prior scores for vertices in a protein-protein interaction network, and these scores are propagated through the network. Permutation is used to select genes that are guilty-by-association and thus consistently obtain high scores after network propagation. We apply the approach to data of Crohn's disease and call candidate genes that have been reported by other independent GWAS, but not in the analysed data set. A prediction model based on these candidate genes show good predictive power as measured by Area Under the Receiver Operating Curve (AUC) in 10 fold cross-validations.
conclusionsOur network propagation method applied to a genome-wide association study increases association findings over other approaches.
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