ArticleBMC bioinformatics2014
Bayesian neural networks for detecting epistasis in genetic association studies.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed.
- The SURMetaGIT study: Design and rationale for a prospective pan-omics examination of the gastrointestinal response to Roux-en-Y gastric bypass surgery.The Journal of international medical research · 2016Trial
- A novel explainable deep-learning approach for network analysis of epistatic interactions.NAR genomics and bioinformatics · 2026Article
- Considerations in the search for epistasis.Genome biology · 2024Review
- A Novel Approach to Dual Feature Selection of Atrial Fibrillation Based on HC-MFS.Diagnostics (Basel, Switzerland) · 2024Article
- Artificial intelligence applications in the diagnosis and treatment of bacterial infections.Frontiers in microbiology · 2024Review
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- Article
- Genome-wide prediction for complex traits under the presence of dominance effects in simulated populations using GBLUP and machine learning methods.Journal of animal science · 2020Article
- Robust genetic interaction analysis.Briefings in bioinformatics · 2019Review
- Article
- Identification of genetic interaction networks via an evolutionary algorithm evolved Bayesian network.BioData mining · 2016Article
- Using Linkage Analysis to Detect Gene-Gene Interactions. 2. Improved Reliability and Extension to More-Complex Models.PloS one · 2016Article
- An investigation of gene-gene interactions in dose-response studies with Bayesian nonparametrics.BioData mining · 2015Article
- A survey about methods dedicated to epistasis detection.Frontiers in genetics · 2015Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
backgroundDiscovering causal genetic variants from large genetic association studies poses many difficult challenges. Assessing which genetic markers are involved in determining trait status is a computationally demanding task, especially in the presence of gene-gene interactions.
resultsA non-parametric Bayesian approach in the form of a Bayesian neural network is proposed for use in analyzing genetic association studies. Demonstrations on synthetic and real data reveal they are able to efficiently and accurately determine which variants are involved in determining case-control status. By using graphics processing units (GPUs) the time needed to build these models is decreased by several orders of magnitude. In comparison with commonly used approaches for detecting interactions, Bayesian neural networks perform very well across a broad spectrum of possible genetic relationships.
conclusionsThe proposed framework is shown to be a powerful method for detecting causal SNPs while being computationally efficient enough to handle large datasets.
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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.