Diagnostics
Multi-Platform Transcriptomic Classification of LUAD Driver Mutation Subtypes via Heterogeneous Ensemble Learning
By I.sibel KERVANCI and Gözde Özsert Yiğit.
Abstract
Background
Accurate identification of EGFR-mutant, KRAS-mutant, and triple-negative (TN) LUAD driver mutation subtypes is essential for guiding targeted therapy, yet standard molecular testing is invasive and not always feasible. Gene expression-based machine learning offers an alternative that avoids additional invasive sampling; however, existing models are largely restricted to binary classification and lack cross-platform generalizability.
Methods
We developed a multi-cohort, ComBat-harmonized pipeline integrating SHAP-driven feature selection and a Bayesian-optimized heterogeneous ensemble of five learners, extended with NSGA-II Pareto-optimal weight optimization under leak-free cross-validation. External validation used an independent RNA-seq cohort with reference-batch harmonization and threshold calibration.