Machine learning models for multi-omics data often trade off predictive accuracy against biological interpretability. An emerging class of deep learning architectures structurally encode biological knowledge to improve both prediction and explainability. Opportunities and challenges remain for broader adoption.
This is a preview of subscription content, access via your institution
Relevant articles
Open Access articles citing this article.
-
Unifying multi-sample network inference from prior knowledge and omics data with CORNETO
Nature Machine Intelligence Open Access 22 July 2025
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$32.99 /Â 30Â days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$259.00 per year
only $21.58 per issue
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
References
Wysocka, M., Wysocki, O., Zufferey, M., Landers, D. & Freitas, A. A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data. BMC Bioinformatics 24, 198 (2023).
van Hilten, A. et al. Phenotype prediction using biologically interpretable neural networks on multi-cohort multi-omics data. npj Syst. Biol. Appl. 10, 81 (2024).
Kuenzi, B. M. et al. Predicting drug response and synergy using a deep learning model of human cancer cells. Cancer Cell 38, 672–684 (2020).
Novakovsky, G., Dexter, N., Libbrecht, M. W., Wasserman, W. W. & Mostafavi, S. Obtaining genetics insights from deep learning via explainable artificial intelligence. Nat. Rev. Genet. 24, 125–137 (2023).
Elmarakeby, H. A. et al. Biologically informed deep neural network for prostate cancer discovery. Nature 598, 348–352 (2021).
Hao, J., Kim, Y., Mallavarapu, T., Oh, J. H. & Kang, M. Interpretable deep neural network for cancer survival analysis by integrating genomic and clinical data. BMC Med. Genomics 12 (Suppl. 10), 189 (2019).
Seninge, L., Anastropoulos, I., Ding, H. & Stuart, J. VEGA is an interpretable generative model for inferring biological network activity in single-cell transcriptomics. Nat. Commun. 12, 5684 (2021).
Hou, Z., Leng, J., Yu, J., Xia, Z. & Wu, L. Y. PathExpSurv: pathway expansion for explainable survival analysis and disease gene discovery. BMC Bioinformatics 24, 434 (2023).
Nguyen, T. et al. Optimal fusion of genotype and drug embeddings in predicting cancer drug response. Brief. Bioinform. 25, bbae227 (2024).
Molnar, C. et al. in xxAI – Beyond Explainable AI (eds Holzinger, A. et al.) 39–68 (Springer, 2022).
Acknowledgements
D.A.S. and S.J.V. acknowledge support from the German Federal Ministry of Education and Research within project curATime (03ZU1202JA). J.E. acknowledges support of the Sächsische Staatsministerium für Wissenschaft, Kultur und Tourismus under ERA PerMed (MIRACLE, 2021-055).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Rights and permissions
About this article
Cite this article
Selby, D.A., Sprang, M., Ewald, J. et al. Beyond the black box with biologically informed neural networks. Nat Rev Genet 26, 371–372 (2025). https://doi.org/10.1038/s41576-025-00826-1
Published:
Issue date:
DOI: https://doi.org/10.1038/s41576-025-00826-1
This article is cited by
-
Unifying multi-sample network inference from prior knowledge and omics data with CORNETO
Nature Machine Intelligence (2025)
