ArticleComputational and structural biotechnology journal2022
An accurate prediction model of digenic interaction for estimating pathogenic gene pairs of human diseases.
Article in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 13 citations in OpenAlex.
- VUStruct: A compute pipeline for high throughput and personalized structural biology.PLoS computational biology · 2026Article
- Benchmarking knowledge graph embedding models for the prediction of oligogenic combinations.Briefings in bioinformatics · 2026Article
- Mathematical Model to Assess Mutational Burden in Retinal Dystrophy Patients Negative to Mendelian Genetic Tests and Carriers of Multiple Recessive Variants.Journal of ophthalmic & vision research · 2026Article
- iRUNNER: A Baseline Mutation Burden Regression for Identifying Gene Interaction Between Rare Variants for Diseases.Genomics, proteomics & bioinformatics · 2025Article
- Benchmark of computational methods to detect digenism in sequencing data.European journal of human genetics : EJHG · 2025Review
- Digenic variant interpretation with hypothesis-driven explainable AI.NAR genomics and bioinformatics · 2025Article
- Prediction model of in-hospital mortality risk in intensive care unit patients with cardiac arrest: a multicenter retrospective cohort study based on an ensemble model.Frontiers in cardiovascular medicine · 2025Article
- Probable digenic inheritance of Diamond-Blackfan anemia.American journal of medical genetics. Part A · 2024Article
- Missing Heritability in Albinism: Deep Characterization of a Hungarian Albinism Cohort Raises the Possibility of the Digenic Genetic Background of the Disease.International journal of molecular sciences · 2024Article
- Faster and more accurate pathogenic combination predictions with VarCoPP2.0.BMC bioinformatics · 2023Article
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Authors and funding
5 authors at 2 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
Abstract
Increasing evidence shows that genetic interaction across the entire genome may explain a non-trivial fraction of genetic diseases. Digenic interaction is the simplest manifestation of genetic interaction among genes. However, systematic exploration of digenic interactive effects on the whole genome is often discouraged by the high dimension burden. Thus, numerous digenic interactions are yet to be identified for many diseases. Here, we propose a Digenic Interaction Effect Predictor (DIEP), an accurate machine-learning approach to identify the genome-wide pathogenic coding gene pairs with digenic interaction effects. This approach achieved high accuracy and sensitivity in independent testing datasets, outperforming another gene-level digenic predictor (DiGePred). DIEP was also able to discriminate digenic interaction effect from bi-locus effects dual molecular diagnosis (pseudo-digenic). Using DIEP, we provided a valuable resource of genome-wide digenic interactions and demonstrated the enrichment of the digenic interaction effect in Mendelian and Oligogenic diseases. Therefore, DIEP will play a useful role in facilitating the genomic mapping of interactive causal genes for human diseases.
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