Deep learning shows promise for predicting gene expression levels from DNA sequences. However, recent studies show that current state-of-the-art models struggle to accurately characterize expression variation from personal genomes, limiting their usefulness in personalized medicine.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
References
Leslie, R. et al. Bioinformatics 30, i185–i194 (2014).
Avsec, Ž. et al. Nat. Methods. 18, 1196–1203 (2021).
Toneyan, S. et al. Nat. Mach. Intell. 4, 1088–1100 (2022).
Karollus, A. et al. Genome Biol. 24, 56 (2023).
Sasse, A. et al. Nat. Genet. https://doi.org/10.1038/s41588-023-01524-6 (2023).
Huang, C. et al. Nat. Genet. https://doi.org/10.1038/s41588-023-01574-w (2023).
Bennett, D. A. et al. J. Alzheimers Dis. 64, S161–S189 (2018).
Kelley, D. R. et al. Genome Res. 28, 739–750 (2018).
Zhou, J. et al. Nat. Genet. 50, 1171–1179 (2018).
Agarwal, V. & Shendure, J. Cell Rep. 31, 107663 (2020).
Lappalainen, T. et al. Nature 501, 506–511 (2013).
Amazon, E. R. et al. Nat. Genet. 47, 1091–1098 (2015).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interest
The authors declare no competing interests.
Rights and permissions
About this article
Cite this article
Tang, Z., Toneyan, S. & Koo, P.K. Current approaches to genomic deep learning struggle to fully capture human genetic variation. Nat Genet 55, 2021–2022 (2023). https://doi.org/10.1038/s41588-023-01517-5
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41588-023-01517-5
