Abstract
Personalized medicine aims to tailor medical treatments to individual patients, and predicting drug responses from molecular profiles using machine learning is crucial for this goal. However, the high dimensionality of the molecular profiles compared to the limited number of samples presents significant challenges. Knowledge-based feature selection methods are particularly suitable for drug response prediction, as they leverage biological insights to reduce dimensionality and improve model interpretability. This study presents the first comparative evaluation of nine different knowledge-based and data-driven feature reduction methods on cell line and tumor data. Our analysis employs six distinct machine learning models, with a total of more than 6,000 runs to ensure a robust evaluation. Our findings indicate that transcription factor activities outperform other methods in predicting drug responses, effectively distinguishing between sensitive and resistant tumors for seven of the 20 drugs evaluated.