Uncovering High-Order Epistatic Interactions in GWAS via a Machine Learning-Based Feature Engineering Framework
Background: 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 nonlinear relationships, extracting interpretable insights from these models remains a challenge. We propose a novel tree-based feature engineering framework that uses…
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