Universidad Hispana

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.

En el presente manuscrito se muestra una secuencia del origen y desarrollo conceptual histórico del conductismo a la luz crítica de las ideas propuestas por el Filósofo Pragmaticista Charles Sanders Peirce que en esta ocasión es utilizado para evaluar la propuesta de John Broadus Watson con respecto de los criterios de falibilismo, continuidad y evolución que son características propias del desarrollo cientifico, con la finalidad deverificar si cumplen con los principios.