Assessment and Optimization of Explainable Machine Learning Models Applied to Transcriptomic Data.

ABSTRACT: Explainable artificial intelligence aims to interpret how machine learning models make decisions, and many model explainers have been developed in the computer vision field. However, understanding of the applicability of these model explainers to biological data is still lacking. In this study, we comprehensively evaluated multiple explainers by interpreting pre-trained models for predicting tissue types from transcriptomic data and by identifying the top contributing genes from each sample with the greatest impacts on model prediction. To improve the reproducibility and interpretability of results generated by model explainers, we proposed a series of optimization strategies for each explainer on two different model architectures of multilayer perceptron (MLP) and convolutional neural net

SUBMITTER: Zhao Y 

PROVIDER: S-EPMC10025763 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature