Evaluating Hyperparameter Optimization in Machine Learning Algorithms for Cancer Driver Gene Classification - Schardosim, Ana Laura; Konarzewski, Kamille; Andrades, Renan Soares de; Recamonde-Mendoza, Mariana (Anais da Escola Regional de Aprendizado de Máquina e Inteligência Artificial da Região Sul (ERAMIA-RS), 2025)
Cancer driver genes (CDGs) play a central role in tumorigenesis and represent important targets for diagnosis and therapy. In this study, we evaluate the impact of hyperparameter optimization on the predictive performance of traditional machine learning algorithms using multi-omics data. We perform systematic searches across different configurations to identify the most effective settings for CDG classification. Our results show that optimized models consistently outperform their default counterparts, particularly in recall and precision-recall metrics, with ensemble methods showing the most pronounced gains. These findings indicate that traditional algorithms, when carefully tuned, can represent promising approaches for identifying CDGs from genomics data.