Interpretable deep learning architectures for improving drug response prediction performance: myth or reality?

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.

Results

We comprehensively and systematically assessed four state-of-the-art interpretable DL models using three pathway collections to assess their ability in making accurate predictions on unseen samples from the same dataset, as well as their generalizability to an independent dataset. Our results showed that models

SUBMITTER: Li Y 

PROVIDER: S-EPMC10301685 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature