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Assessment and Optimization of the Interpretability of Machine Learning Models Applied to Transcriptomic Data

Yongbing Zhao, Jinfeng Shao, Yan W Asmann
doi: https://doi.org/10.1101/2022.02.18.481077
Yongbing Zhao
1Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL 32224, USA
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  • For correspondence: im@ybzhao.com asmann.yan@mayo.edu
Jinfeng Shao
2The Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD, 20852, USA
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Yan W Asmann
1Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL 32224, USA
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  • For correspondence: im@ybzhao.com asmann.yan@mayo.edu
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Abstract

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 Statement

The authors have declared no competing interest.

Footnotes

  • In the revised version, we have updates many sections to make the manuscript easy to read.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted March 30, 2022.
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Assessment and Optimization of the Interpretability of Machine Learning Models Applied to Transcriptomic Data
Yongbing Zhao, Jinfeng Shao, Yan W Asmann
bioRxiv 2022.02.18.481077; doi: https://doi.org/10.1101/2022.02.18.481077
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Assessment and Optimization of the Interpretability of Machine Learning Models Applied to Transcriptomic Data
Yongbing Zhao, Jinfeng Shao, Yan W Asmann
bioRxiv 2022.02.18.481077; doi: https://doi.org/10.1101/2022.02.18.481077

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