ArticleBioinformatics (Oxford, England)2010
The power of protein interaction networks for associating genes with diseases.
Article in Bioinformatics (Oxford, England), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 152 papers, 1 of them a synthesis that pooled it.
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Who cites it
152 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Interactome-transcriptome analysis discovers signatures complementary to GWAS Loci of Type 2 Diabetes.Scientific reports · 2016Pooled it
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- Network Pharmacology and Molecular Docking Approach to Identify and Investigate the Mechanisms of Active Components of Areca Catechu in Relation to Gut Disorders.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2025Article
- KG2ML: integrating knowledge graphs and positive unlabeled learning for identifying disease-associated genes.Frontiers in bioinformatics · 2025Article
- Influence of multi-species data on gene-disease associations in substance use disorder using random walk with restart models.PloS one · 2025Article
- Prioritizing cancer therapeutic genes using BioRank: A biologically-informed PageRank framework.Computational and structural biotechnology journal · 2025Article
- Predicting the pro-longevity or anti-longevity effect of model organism genes with enhanced Gaussian noise augmentation-based contrastive learning on protein-protein interaction networks.NAR genomics and bioinformatics · 2024Article
- Proteomics appending a complementary dimension to precision oncotherapy.Computational and structural biotechnology journal · 2024Review
- Disease gene prioritization with quantum walks.Bioinformatics (Oxford, England) · 2024Article
- The Y-ome Conundrum: Insights into Uncharacterized Genes and Approaches for Functional Annotation.Molecular and cellular biochemistry · 2024Review
- A probabilistic knowledge graph for target identification.PLoS computational biology · 2024Article
- Speos: an ensemble graph representation learning framework to predict core gene candidates for complex diseases.Nature communications · 2023Article
- Exploring pathway interactions to detect molecular mechanisms of disease: 22q11.2 deletion syndrome.Orphanet journal of rare diseases · 2023Article
- Graph Random Forest: A Graph Embedded Algorithm for Identifying Highly Connected Important Features.Biomolecules · 2023Article
- A gene pathway enrichment method based on improved TF-IDF algorithm.Biochemistry and biophysics reports · 2023Article
- Graph Domain Adaptation via Theory-Grounded Spectral Regularization.... International Conference on Learning Representations · 2023Article
- HetIG-PreDiG: A Heterogeneous Integrated Graph Model for Predicting Human Disease Genes based on gene expression.PloS one · 2023Article
92 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationUnderstanding the association between genetic diseases and their causal genes is an important problem concerning human health. With the recent influx of high-throughput data describing interactions between gene products, scientists have been provided a new avenue through which these associations can be inferred. Despite the recent interest in this problem, however, there is little understanding of the relative benefits and drawbacks underlying the proposed techniques.
resultsWe assessed the utility of physical protein interactions for determining gene-disease associations by examining the performance of seven recently developed computational methods (plus several of their variants). We found that random-walk approaches individually outperform clustering and neighborhood approaches, although most methods make predictions not made by any other method. We show how combining these methods into a consensus method yields Pareto optimal performance. We also quantified how a diffuse topological distribution of disease-related proteins negatively affects prediction quality and are thus able to identify diseases especially amenable to network-based predictions and others for which additional information sources are absolutely required. AVAILABILITY: The predictions made by each algorithm considered are available online at http://www.cbcb.umd.edu/DiseaseNet.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.