Ridge regression baseline model outperforms deep learning method for cancer genetic dependency prediction

Daniel Chang, Xiang Zhang

Abstract: Accurately predicting genetic or other cellular vulnerabilities of unscreened, or difficult to screen, cancer samples will allow vast advancements in precision oncology. We re-analyzed a recently published deep learning method for predicting cancer genetic dependencies from their omics profiles. After implementing a ridge regression baseline model with an alternative, simplified problem setup, we achieved a model that outperforms the original deep learning method. Our study demonstrates the importance of problem formulation in machine learning applications and underscores the need for rigorous comparisons with baseline approaches.