RT Journal Article
SR Electronic
T1 Assessment and Optimization of the Interpretability of Machine Learning Models Applied to Transcriptomic Data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.02.18.481077
DO 10.1101/2022.02.18.481077
A1 Yongbing Zhao
A1 Jinfeng Shao
A1 Yan W Asmann
YR 2022
UL http://biorxiv.org/content/early/2022/03/30/2022.02.18.481077.abstract
AB Explainable artificial intelligence aims to interpret how the machine learning models make decisions, and many model explainers have been developed in the computer vision field. However, the understandings of the applicability of these model explainers to biological data are still lacking. In this study, we comprehensively evaluated multiple explainers by interpreting pretrained models of predicting tissue types from transcriptomic data, and by identifying 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 Network (CNN). We observed three groups of explainer and model architecture combinations with high reproducibility. Group II, which contains three model explainers on aggregated MLP models, identified top contributing genes in different tissues that exhibited tissue-specific manifestation and were potential cancer biomarkers. In summary, our work provides novel insights and guidance for exploring biological mechanisms using explainable machine learning models.Competing Interest StatementThe authors have declared no competing interest.