Deep learning in biology faces a transferability crisis

Creating generalizable models is a conserved aim in deep learning-however, misleading claims of transferability threaten to obfuscate reliable performance evaluation. We outline the severity of this issue in the biosciences, and suggest potential solutions.

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Publication Details

Journal
PLoS Biology
Published
2026-03-03
DOI
https://doi.org/10.1371/journal.pbio.3003656
Primary Topic
Machine Learning in Bioinformatics
Type
article
Field-Weighted Citation Impact
0.00
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article

Deep learning in biology faces a transferability crisis

Thomas A. O’Shea-Wheller, Katie I. Murray
PLoS Biology
Machine Learning in Bioinformatics
article

Deep learning in biology faces a transferability crisis

Thomas A. O’Shea-Wheller, Katie I. Murray
article en

Abstract

Creating generalizable models is a conserved aim in deep learning-however, misleading claims of transferability threaten to obfuscate reliable performance evaluation. We outline the severity of this issue in the biosciences, and suggest potential solutions.

PLoS BiologyVol. 24(3)
University of Exeter (GB)
Openalex Percentile: Top 36%
Machine Learning in Bioinformatics
DOIOpen Access PDFOpenAlex
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Deep learning in biology faces a transferability crisis — Thomas A. O’Shea-Wheller, Katie I. Murray · PLoS Biology (2026) | TGRS Research Map | TGRS