A novel tree-based feature engineering framework that uses Classification and Regression Trees (CART) to explicitly encode high-order interaction decision paths as dummy variables that significantly improves classification accuracy and model interpretability compared to using the original feature space alone is proposed.
Interaction-Bridged Association Study is introduced, a general framework that incorporates pathway-level interaction patterns into genotype–phenotype association analysis without explicitly enumerating interactions, and provides a statistically robust and computationally tractable framework for incorporating interactio...
This review describes the main characteristics and limitations of standard statistical approaches for GWAS, the main uses of AI methods in computational genomics, and recent attempts to leverage AI strategies in GWAS and presents 30 methods designed to leverage AI in GWAS.
S. D’Antona, Mawada Elmagboul Abdalla Abakar, Daniele Ramazzotti et al.· BioData Mining· 0 citations
A novel statistical framework, iSVR, is presented that incorporates gene-environment interaction terms into a support vector regression model, enabling both modeling of interaction effects and their statistical testing, and provides a powerful approach to characterize the gene-environment interaction landscapes underly...
Efficient multi-eNvironmental Gene-environment Interaction iNference Estimator (ENGINE), a supervised variance-component framework that learns an embedding that combines multiple environmental exposures while jointly estimating additive, G×E, and heteroskedastic noise components, making polygenic G×E analysis tractable...
K-PoPS provides a post hoc framework for examining and interpreting GWAS effector-gene nominations by decomposing each prediction into contributions from training genes, and identifies multiple plausible effector genes within a dilated cardiomyopathy locus contrary to the parsimonious assumption.
Overall, XAI should be considered a framework for model interpretation, feature prioritization, and hypothesis generation rather than a replacement for experimental validation in plant genomics and breeding, and the common misconception that feature importance implies biological causality is emphasized.
Agata Głuchowska, Muhammad Hafeez Ullah Khan, M. Pawełkowicz· Applied Sciences· 1 citation
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