Ridge regression baseline model outperforms deep learning method for cancer genetic dependency prediction
Chang, D.; Zhang, X.
Show 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.
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