ArticleInternational journal of molecular sciences2022
Network-Based Approaches for Disease-Gene Association Prediction Using Protein-Protein Interaction Networks.
Article in International journal of molecular sciences, 2022. 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.
- A module-based approach for post-omics, post-GWAS network-based gene classification.Bioinformatics (Oxford, England) · 2026Article
- Data-intensive immune network modelling for One Health.Briefings in bioinformatics · 2026Review
- APOE and Cerebral Microbleeds: Insights Into Causal Proteins and Therapeutic Targets.Brain and behavior · 2026Article
- A Multi-Modal Graph Neural Network Framework for Parkinson's Disease Therapeutic Discovery.International journal of molecular sciences · 2025Article
- Integrated computational analysis of molecular mechanisms underlying perfluorooctane sulfonic acid induced thyroid toxicity.Scientific reports · 2025Article
- Application of Pan-Omics Technologies in Research on Important Economic Traits for Ruminants.International journal of molecular sciences · 2024Review
- Mitochondria-Related Gene MAOB is a Key Biomarker of Osteoarthritis and Inhibition of Its Expression Reduces LPS-induced Chondrocyte Damage.Biochemical genetics · 2024Article
- Inhibition of ADORA3 promotes microglial phagocytosis and alleviates chronic ischemic white matter injury.CNS neuroscience & therapeutics · 2024Article
- Drug Repositioning Based on Deep Sparse Autoencoder and Drug-Disease Similarity.Interdisciplinary sciences, computational life sciences · 2024Article
- Protein-protein interaction network-based integration of GWAS and functional data for blood pressure regulation analysis.Human genomics · 2024Article
- Bioinformatics Prediction for Network-Based Integrative Multi-Omics Expression Data Analysis in Hirschsprung Disease.Biomolecules · 2024Article
- Meta-2OM: A multi-classifier meta-model for the accurate prediction of RNA 2'-O-methylation sites in human RNA.PloS one · 2024Article
- Construction of HBV-HCC prognostic model and immune characteristics based on potential genes mining through protein interaction networks.Journal of cancer research and clinical oncology · 2023Article
- Predicting Drug-Gene-Disease Associations by Tensor Decomposition for Network-Based Computational Drug Repositioning.Biomedicines · 2023Article
- Recent Advances in Protein-Protein Interactions.International journal of molecular sciences · 2023Article
- Drug-Disease Association Prediction Using Heterogeneous Networks for Computational Drug Repositioning.Biomolecules · 2022Review
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3 authors.
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Abstract
Genome-wide association studies (GWAS) can be used to infer genome intervals that are involved in genetic diseases. However, investigating a large number of putative mutations for GWAS is resource- and time-intensive. Network-based computational approaches are being used for efficient disease-gene association prediction. Network-based methods are based on the underlying assumption that the genes causing the same diseases are located close to each other in a molecular network, such as a protein-protein interaction (PPI) network. In this survey, we provide an overview of network-based disease-gene association prediction methods based on three categories: graph-theoretic algorithms, machine learning algorithms, and an integration of these two. We experimented with six selected methods to compare their prediction performance using a heterogeneous network constructed by combining a genome-wide weighted PPI network, an ontology-based disease network, and disease-gene associations. The experiment was conducted in two different settings according to the presence and absence of known disease-associated genes. The results revealed that HerGePred, an integrative method, outperformed in the presence of known disease-associated genes, whereas PRINCE, which adopted a network propagation algorithm, was the most competitive in the absence of known disease-associated genes. Overall, the results demonstrated that the integrative methods performed better than the methods using graph-theory only, and the methods using a heterogeneous network performed better than those using a homogeneous PPI network only.
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