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Interpreting how machine learning models make predictions in biological studies

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

Interpreting how the machine learning models make decisions is a new method to explore meaningful rules. However, it still lacks an understanding of the applicability of different model explainers in biological study. To address this question, we made a comprehensive evaluation on various explainers, and analyzed their performance and biological preference by quantifying the contribution of individual gene in the models trained to predict tissue type from transcriptome. Additionally, we also proposed a series of optimization strategies to improve the performance of different explainers. Interestingly, all explainers can be classified into three groups based on their outputs on different neural network architectures. With explainers from the group II, we found that the top contributing genes in different tissues exhibit tissue-specific manifestation and are potential biomarkers for cancer research. In summary, this work provides a novel insight and general guidance for exploring biological mechanisms by interpreting machine learning models.

Competing Interest Statement

The authors have declared no competing interest.

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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 February 22, 2022.
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Interpreting how machine learning models make predictions in biological studies
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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Interpreting how machine learning models make predictions in biological studies
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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