SHAP-Driven Deep Learning Identifies Pre-Symptomatic Influenza A Biomarkers: An Explainable AI Framework for Clinical Genomics

Background: DeepFlu predicts symptomatic influenza onset from pre-exposure gene expression, but the contribution of individual genes remains unclear.Methods: We apply SHapley Additive exPlanations (SHAP), an explainable AI (XAI) method, to evaluate gene-level contributions in DeepFlu models trained on H1N1 and H3N2 influenza gene expression data (GSE52428 and GSE73072 datasets).Results: Five key genes—HLA-DQA1, HLA-DQB1, XIST, RPS4Y1, and KDM5D—were shared across influenza subtypes. These genes are associated with immune response and sex-linked pathways, with elevated XIST and reduced RPS4Y1/KDM5D expression suggesting higher susceptibility in females. Conclusion: Integrating SHAP with DeepFlu enhances the interpretability of influenza risk predictions, offering actionable biological insights for early intervention.