ArticleCommunications biology2022
Gene-gene interaction detection with deep learning.
Article in Communications biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Using deep learning models as a genetic architecture for the simulation of breeding schemes.G3 (Bethesda, Md.) · 2026Article
- The choice-wide behavioral association study: data-driven identification of interpretable behavioral components.Nature communications · 2026Article
- Explainable graph learning for multimodal single-cell data integration.BMC bioinformatics · 2026Article
- Deep-Neural-Network-Aided Genetic Association Testing in Samples with Related Individuals.Current issues in molecular biology · 2026Article
- Modeling and application of alzheimer's disease complex trait prediction based on multi-task learning.Scientific reports · 2026Article
- Benchmarking interpretability of deep learning for predictive genomics: Recall, precision, and variability of feature attribution.PLoS computational biology · 2025Article
- Applying gradient tree boosting to QTL mapping with Shapley additive explanations.Breeding science · 2025Article
- HyperdCas12a-based multiplexed genetic regulation in Candida albicans.Nucleic acids research · 2025Article
- An Introduction to Network Analysis in Plant Biology.Plant direct · 2025Article
- GE-IA-NAM: gene-environment interaction analysis via imaging-assisted neural additive model.Bioinformatics (Oxford, England) · 2025Article
- Leveraging hierarchical structures for genetic block interaction studies using the hierarchical transformer.medRxiv : the preprint server for health sciences · 2025Article
- CoTF-reg reveals cooperative transcription factors in oligodendrocyte gene regulation using single-cell multi-omics.Communications biology · 2025Article
- Comparing statistical learning methods for complex trait prediction from gene expression.PloS one · 2025Article
- Error-controlled non-additive interaction discovery in machine learning models.Nature machine intelligence · 2025Article
- Considerations in the search for epistasis.Genome biology · 2024Review
- Comparing statistical learning methods for complex trait prediction from gene expression.bioRxiv : the preprint server for biology · 2024Article
- BridGE: a pathway-based analysis tool for detecting genetic interactions from GWAS.Nature protocols · 2024Review
- Role of gene interactions in the pathophysiology of skeletal dysplasias: A case report in Colombia.Journal, genetic engineering & biotechnology · 2024Article
- BRASS: Permutation methods for binary traits in genetic association studies with structured samples.PLoS genetics · 2023Article
- A Comprehensive Comparative Analysis of Deep Learning Based Feature Representations for Molecular Taste Prediction.Foods (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The extent to which genetic interactions affect observed phenotypes is generally unknown because current interaction detection approaches only consider simple interactions between top SNPs of genes. We introduce an open-source framework for increasing the power of interaction detection by considering all SNPs within a selected set of genes and complex interactions between them, beyond only the currently considered multiplicative relationships. In brief, the relation between SNPs and a phenotype is captured by a neural network, and the interactions are quantified by Shapley scores between hidden nodes, which are gene representations that optimally combine information from the corresponding SNPs. Additionally, we design a permutation procedure tailored for neural networks to assess the significance of interactions, which outperformed existing alternatives on simulated datasets with complex interactions, and in a cholesterol study on the UK Biobank it detected nine interactions which replicated on an independent FINRISK dataset.
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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.