Article

Uncovering High-Order Epistatic Interactions in GWAS via a Machine Learning-Based Feature Engineering Framework

2026-08-09

Abstract excerpt

<h4>Background</h4> Genome-wide association studies (GWAS) often fail to identify higher-order epistatic interactions that contribute to complex inheritance patterns of traits and diseases. While machine learning (ML) can capture non-linear relationships, extracting interpretable insights from these models remains a challenge. We propose a novel tree-based feature engineering framework that uses Classification an...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 871b1fb1-3963-5478-aacc-15acb7e75272
DOI: 10.64898/2026.08.03.742638

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Uncovering High-Order Epistatic Interactions in GWAS via a Machine Learning-Based Feature Engineering FrameworkDOI 10.64898/2026.08.03.742638Read

Deep learning captures the effect of epistasis in multifactorial diseasesSemantic proximity Read

Genome Wide Epistasis Study of On-Statin Cardiovascular Events with Iterative Feature Reduction and SelectionSemantic proximity Read

Deep Learning captures the effect of epistasis in multifactorial diseasesSemantic proximity Read

Learning epistatic polygenic phenotypes with Boolean interactionsSemantic proximity Read

Learning epistatic polygenic phenotypes with Boolean interactions.Semantic proximity Read

Collective feature selection to identify crucial epistatic variantsSemantic proximity Read

An expanded analysis framework for multivariate GWAS connects inflammatory biomarkers to functional variants and diseaseSemantic proximity Read

Post-GWAS machine learning prioritizes key genes regulating blood pressureSemantic proximity Read

Epi-MEIF: detecting higher order epistatic interactions for complex traits using mixed effect conditional inference forests.Semantic proximity Read

A random forest approach to the detection of epistatic interactions in case-control studies.Semantic proximity Read

Improving Genetic Association Studies with a Novel Methodology that Unveils the Hidden Complexity of All-Cause Heart FailureSemantic proximity Read

Causal Genomics in the Deep Learning EraSemantic proximity Read