ArticleApplied bioinformatics2006
Machine learning for detecting gene-gene interactions: a review.
Article in Applied bioinformatics, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 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.
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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
92 citing papers in PubMed.
- Integrated analysis of genetic and proteomic data identifies biomarkers associated with adverse events following smallpox vaccination.Genes and immunity · 2009Trial
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- Predicting the hub interactome of COVID-19 and oral squamous cell carcinoma: uncovering ALDH-mediated Wnt/β-catenin pathway activation via salivary inflammatory proteins.Scientific reports · 2025Article
- Genetic variations as predictors of dispositional and dyadic empathy-a couple study.Scientific reports · 2024Article
- Gene-environment interactions within a precision environmental health framework.Cell genomics · 2024Review
- Revealing third-order interactions through the integration of machine learning and entropy methods in genomic studies.BioData mining · 2024Article
- Enhancing genomic prediction with Stacking Ensemble Learning in Arabica Coffee.Frontiers in plant science · 2024Article
- Machine learning approaches and genetic determinants that influence the development of type 2 diabetes mellitus: a genetic association study in Brazilian patients.Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologica · 2024Article
- Exploring the potential of incremental feature selection to improve genomic prediction accuracy.Genetics, selection, evolution : GSE · 2023Article
- Automated quantitative trait locus analysis (AutoQTL).BioData mining · 2023Article
- Research on a UAV spray system combined with grid atomized droplets.Frontiers in plant science · 2023Article
- Advancing genome editing with artificial intelligence: opportunities, challenges, and future directions.Frontiers in bioengineering and biotechnology · 2023Review
- Fecal microbiome transplant from patients with lactation mastitis promotes mastitis in conventional lactating mice.Frontiers in microbiology · 2023Article
- Machine learning of pair-contact process with diffusion.Scientific reports · 2022Article
- learnMET: an R package to apply machine learning methods for genomic prediction using multi-environment trial data.G3 (Bethesda, Md.) · 2022Article
- A comparison of joint dichotomization and single dichotomization of interacting variables to discriminate a disease outcome.The international journal of biostatistics · 2022Article
- CRISPR-Cas9 gRNA efficiency prediction: an overview of predictive tools and the role of deep learning.Nucleic acids research · 2022Article
- In Silico Exploration ofBiomolecules · 2022Article
- Computational Genomics in the Era of Precision Medicine: Applications to Variant Analysis and Gene Therapy.Journal of personalized medicine · 2022Review
- Evaluating the detection ability of a range of epistasis detection methods on simulated data for pure and impure epistatic models.PloS one · 2022Article
32 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Complex interactions among genes and environmental factors are known to play a role in common human disease aetiology. There is a growing body of evidence to suggest that complex interactions are 'the norm' and, rather than amounting to a small perturbation to classical Mendelian genetics, interactions may be the predominant effect. Traditional statistical methods are not well suited for detecting such interactions, especially when the data are high dimensional (many attributes or independent variables) or when interactions occur between more than two polymorphisms. In this review, we discuss machine-learning models and algorithms for identifying and characterising susceptibility genes in common, complex, multifactorial human diseases. We focus on the following machine-learning methods that have been used to detect gene-gene interactions: neural networks, cellular automata, random forests, and multifactor dimensionality reduction. We conclude with some ideas about how these methods and others can be integrated into a comprehensive and flexible framework for data mining and knowledge discovery in human genetics.
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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.