Adversarial training improves model interpretability in single-cell RNA-seq analysis.

ABSTRACT:

Motivation

Predictive computational models must be accurate, robust, and interpretable to be considered reliable in important areas such as biology and medicine. A sufficiently robust model should not have its output affected significantly by a slight change in the input. Also, these models should be able to explain how a decision is made to support user trust in the results. Efforts have been made to improve the robustness and interpretability of predictive computational models independently; however, the interaction of robustness and interpretability is poorly understood.

Results

As an example task, we explore the computational prediction of cell type based on single-cell RNA-seq data and show that it can be made more robust by adversarially training a deep learning model.

SUBMITTER: Sadria M 

PROVIDER: S-EPMC10719216 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature