MASE-GC: a multi-omics autoencoder and stacking ensemble framework for gastric cancer classification

MASE-GC advances computational oncology by offering an effective and generalizable framework for GC classification. By integrating multi-omics fusion, ensemble learning, and robust preprocessing, the proposed model improves both sensitivity and specificity, reduces false positives, and demonstrates strong potential for clinical translation in precision diagnostics and treatment planning.