Interpretable deep learning architectures for improving drug response prediction performance: myth or reality?
Journal:
Bioinformatics (Oxford, England)
Published Date:
Jun 1, 2023
Abstract
MOTIVATION: Interpretable deep learning (DL) models that can provide biological insights, in addition to accurate predictions, are of great interest to the biomedical community. Recently, interpretable DL models that incorporate signaling pathways have been proposed for drug response prediction (DRP). While these models improve interpretability, it is unclear whether this comes at the cost of less accurate DRPs, or a prediction improvement can also be obtained.