AI Meta-Analysis of Gene-Expression Signatures That Predict Treatment Response

Abstract

Predicting which patients will benefit from a therapy is one of the most practical promises of transcriptomics, but the literature is crowded with gene expression response signatures that do not generalize. A main reason is methodological: many signatures are trained on one cohort, one platform, and one response definition, so the reported genes partly reflect study specific noise, batch effects, and hidden confounding. This manuscript presents an AI assisted meta analysis workflow that aggregates evidence across independent cohorts to derive a consensus gene expression signature that is both biologically interpretable and prediction oriented. The approach combines standardized per gene effect sizes (Hedges g), random effects meta analysis, heterogeneity screening, and nested, cohort aware machine learning. We outline data acquisition from open repositories, harmonization, batch adjustment, and label mapping, then build a meta signature and train parsimonious classifiers using elastic net and gradient boosting. Model evaluation uses leave one study out validation to approximate real world deployment, with calibration and decision curve analysis to connect performance to clinical utility. The workflow is designed to be auditable: every inclusion decision is recorded, and all transformations are reversible and reproducible. By treating signatures as meta analytic objects rather than single study artifacts, the framework reduces overfitting, exposes context specific failure modes, and yields gene sets that map to known treatment response biology. We close with reporting guidance aligned to PRISMA, TRIPOD, and current best practice for transcriptomic predictors. A melanoma anti PD 1 case study is used to illustrate implementation and expected outputs.