ArticlePloS one2013
Prediction and validation of gene-disease associations using methods inspired by social network analyses.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 63 papers.
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Who cites it
63 citing papers in PubMed.
- Computational strategies for cross-species knowledge transfer.Nature methods · 2026Review
- Denoising self-supervised learning for disease-gene association prediction.BMC bioinformatics · 2025Article
- Heterogeneous graph neural networks for link prediction in biomedical networks.Bioinformatics advances · 2025Review
- Computational strategies for cross-species knowledge transfer and translational biomedicine.ArXiv · 2024Article
- TCMNPAS: a comprehensive analysis platform integrating network formulaology and network pharmacology for exploring traditional Chinese medicine.Chinese medicine · 2024Article
- Potential Schizophrenia Disease-Related Genes Prediction Using Metagraph Representations Based on a Protein-Protein Interaction Keyword Network: Framework Development and Validation.JMIR formative research · 2023Article
- A publication-wide association study (PWAS), historical language models to prioritise novel therapeutic drug targets.Scientific reports · 2023Article
- Predicting disease genes based on multi-head attention fusion.BMC bioinformatics · 2023Article
- Drug-target interaction prediction based on spatial consistency constraint and graph convolutional autoencoder.BMC bioinformatics · 2023Article
- HetIG-PreDiG: A Heterogeneous Integrated Graph Model for Predicting Human Disease Genes based on gene expression.PloS one · 2023Article
- Dynamic rewiring of biological activity across genotype and lineage revealed by context-dependent functional interactions.Genome biology · 2022Article
- Exploring diet associations with Covid-19 and other diseases: a Network Analysis-based approach.Medical & biological engineering & computing · 2022Article
- M2PP: a novel computational model for predicting drug-targeted pathogenic proteins.BMC bioinformatics · 2022Article
- WINNER: A network biology tool for biomolecular characterization and prioritization.Frontiers in big data · 2022Article
- Exploring complex and heterogeneous correlations on hypergraph for the prediction of drug-target interactions.Patterns (New York, N.Y.) · 2021Article
- Network propagation-based prioritization of long tail genes in 17 cancer types.Genome biology · 2021Article
- Identifying potential association on gene-disease network via dual hypergraph regularized least squares.BMC genomics · 2021Article
- NOGEA: A Network-oriented Gene Entropy Approach for Dissecting Disease Comorbidity and Drug Repositioning.Genomics, proteomics & bioinformatics · 2021Article
- Disease gene prediction with privileged information and heteroscedastic dropout.Bioinformatics (Oxford, England) · 2021Article
- Prediction of lncRNA-disease associations via an embedding learning HOPE in heterogeneous information networks.Molecular therapy. Nucleic acids · 2021Article
3 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Correctly identifying associations of genes with diseases has long been a goal in biology. With the emergence of large-scale gene-phenotype association datasets in biology, we can leverage statistical and machine learning methods to help us achieve this goal. In this paper, we present two methods for predicting gene-disease associations based on functional gene associations and gene-phenotype associations in model organisms. The first method, the Katz measure, is motivated from its success in social network link prediction, and is very closely related to some of the recent methods proposed for gene-disease association inference. The second method, called Catapult (Combining dATa Across species using Positive-Unlabeled Learning Techniques), is a supervised machine learning method that uses a biased support vector machine where the features are derived from walks in a heterogeneous gene-trait network. We study the performance of the proposed methods and related state-of-the-art methods using two different evaluation strategies, on two distinct data sets, namely OMIM phenotypes and drug-target interactions. Finally, by measuring the performance of the methods using two different evaluation strategies, we show that even though both methods perform very well, the Katz measure is better at identifying associations between traits and poorly studied genes, whereas Catapult is better suited to correctly identifying gene-trait associations overall [corrected].
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