Evaluating the Generalization of Neural Network-Based Pan-Cancer Classification Models for Cohort-Specific Predictions - Fontanari, Thomas; Recamonde-Mendoza, Mariana (Anais do Simpósio Brasileiro de Bioinformática (BSB), 2024)
This study develops and evaluates pan-cancer (PC) models for cohort-specific (CS) predictions using neural networks (NNs). We adopt a dual approach, including a method inspired by few-shot learning, aiming at improving the models’ ability to distinguish between normal and tumorous tissues across diverse cohorts. The first approach trains a NN with comprehensive PC datasets containing 16 cancer types, comparing it against CS models on a target cohort, while the second analyzes whether PC models could generalize to smaller and unseen cohorts by training on 15 cohorts and evaluating on the excluded cohort. Our experiments show that PC models generally outperform CS models, even with limited sample sizes and class imbalances. Moreover, the few-shot approach successfully generalizes to other cancer types, highlighting its potential to advance personalized cancer diagnosis and treatment.