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Applying interpretable machine learning in computational biology—pitfalls, recommendations and opportunities for new developments

Published Online: 2024-08-09

Published Print: 2024-08

Authors

Chen, Valerie https://orcid.org/0009-0007-2783-0265
Yang, Muyu https://orcid.org/0000-0002-0142-0328
Cui, Wenbo https://orcid.org/0009-0006-9866-4439
Kim, Joon Sik https://orcid.org/0009-0006-0057-4735
Talwalkar, Ameet https://orcid.org/0000-0001-6650-1893
Ma, Jian https://orcid.org/0000-0002-4202-5834
Funding

Funding for this research was provided by:

U.S. Department of Health & Human Services | National Institutes of Health (R01HG012303)

U.S. Department of Health & Human Services | National Institutes of Health (R01HG007352)

U.S. Department of Health & Human Services | National Institutes of Health (UM1HG011593)

National Science Foundation (IIS1705121)

National Science Foundation (IIS1838017)

National Science Foundation (IIS2046613)

National Science Foundation (IIS2112471)
License Information

Text and Data Mining valid from 2024-08-01

Version of Record valid from 2024-08-01
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Article History

Received: 27 October 2022

Accepted: 24 June 2024

First Online: 9 August 2024

Competing interests

: A.T. received gift research grants from Meta, Morgan Stanley, and Amazon. J.M. received gift research grant from Google Research. A.T. works part-time for Amplify Partners. The other authors declare no competing interests.

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