ArticleBMC bioinformatics2009
A random forest approach to the detection of epistatic interactions in case-control studies.
Article in BMC bioinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 118 papers, 1 of them a synthesis that pooled it.
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118 citing papers in PubMed, 1 synthesis or guideline pooled it, 315 citations in OpenAlex.
- A meta-analytic framework for detection of genetic interactions.Genetic epidemiology · 2016Pooled it
- Machine learning for evolutionary genetics and molecular evolution.Trends in genetics : TIG · 2026Review
- A novel explainable deep-learning approach for network analysis of epistatic interactions.NAR genomics and bioinformatics · 2026Article
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- Decoding the genomic symphony: unravelling brain disorders through data integration and machine learning.Molecular psychiatry · 2025Review
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- Identification of Immune-Related Biomarkers of Schizophrenia in the Central Nervous System Using Bioinformatic Methods and Machine Learning Algorithms.Molecular neurobiology · 2025Article
- A machine learning-based predictive model for 48-week hepatitis B surface antigen seroclearance in chronic hepatitis B patients treated with pegylated interferon α-2b: prediction at week 24.Frontiers in cell and developmental biology · 2025Article
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- ACOCMPMI: An Ant Colony Optimization Algorithm Based on Composite Multiscale Part Mutual Information for Detecting Epistatic Interactions.Human mutation · 2025Article
- An integrative analysis based on multiple cell death patterns identifies an immunosuppressive subtype and establishes a prognostic signature in lower-grade glioma.Annals of medicine · 2024Article
- Considerations in the search for epistasis.Genome biology · 2024Review
- Exploring Antiviral Drugs on Monolayer Black Phosphorene: Atomistic Theory and Explainable Machine Learning-Assisted Platform.International journal of molecular sciences · 2024Article
- Evaluation of penalized and machine learning methods for asthma disease prediction in the Korean Genome and Epidemiology Study (KoGES).BMC bioinformatics · 2024Article
- Development and experimental validation of a machine learning-based disulfidptosis-related ferroptosis score for hepatocellular carcinoma.Apoptosis : an international journal on programmed cell death · 2024Article
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- Epistasis regulates genetic control of cardiac hypertrophy.Research square · 2023Article
- Exhaustive Variant Interaction Analysis using Multifactor Dimensionality Reduction.Research square · 2023Article
- Sustainable Operation and Maintenance Modeling and Application of Building Infrastructures Combined with Digital Twin Framework.Sensors (Basel, Switzerland) · 2023Article
- Unified model-free interaction screening via CV-entropy filter.Computational statistics & data analysis · 2023Article
58 more citing papers are in PubMed but not listed here.
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4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe key roles of epistatic interactions between multiple genetic variants in the pathogenesis of complex diseases notwithstanding, the detection of such interactions remains a great challenge in genome-wide association studies. Although some existing multi-locus approaches have shown their successes in small-scale case-control data, the "combination explosion" course prohibits their applications to genome-wide analysis. It is therefore indispensable to develop new methods that are able to reduce the search space for epistatic interactions from an astronomic number of all possible combinations of genetic variants to a manageable set of candidates.
resultsWe studied case-control data from the viewpoint of binary classification. More precisely, we treated single nucleotide polymorphism (SNP) markers as categorical features and adopted the random forest to discriminate cases against controls. On the basis of the gini importance given by the random forest, we designed a sliding window sequential forward feature selection (SWSFS) algorithm to select a small set of candidate SNPs that could minimize the classification error and then statistically tested up to three-way interactions of the candidates. We compared this approach with three existing methods on three simulated disease models and showed that our approach is comparable to, sometimes more powerful than, the other methods. We applied our approach to a genome-wide case-control dataset for Age-related Macular Degeneration (AMD) and successfully identified two SNPs that were reported to be associated with this disease.
conclusionBesides existing pure statistical approaches, we demonstrated the feasibility of incorporating machine learning methods into genome-wide case-control studies. The gini importance offers yet another measure for the associations between SNPs and complex diseases, thereby complementing existing statistical measures to facilitate the identification of epistatic interactions and the understanding of epistasis in the pathogenesis of complex diseases.
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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.