Help Privacy Policy Disclaimer
  Advanced SearchBrowse




Journal Article

Deep learning in regulatory genomics: from identification to design


Fernie,  A. R.
Central Metabolism, Department Gutjahr, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available

Hu, X., Fernie, A. R., & Yan, J. (2023). Deep learning in regulatory genomics: from identification to design. Current Opinion in Biotechnology, 79: 102887. doi:10.1016/j.copbio.2022.102887.

Cite as: https://hdl.handle.net/21.11116/0000-000C-3E44-3
Genomics and deep learning are a natural match since both are data-driven fields. Regulatory genomics refers to functional noncoding DNA regulating gene expression. In recent years, deep learning applications on regulatory genomics have achieved remarkable advances so-much-so that it has revolutionized the rules of the game of the computational methods in this field. Here, we review two emerging trends: (i) the modeling of very long input sequence (up to 200 kb), which requires self-matched modularization of model architecture; (ii) on the balance of model predictability and model interpretability because the latter is more able to meet biological demands. Finally, we discuss how to employ these two routes to design synthetic regulatory DNA, as a promising strategy for optimizing crop agronomic properties.