Interpreting cis-regulatory mechanisms from genomic deep neural networks using surrogate models-Reference-Cited by-同舟云学术

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Interpreting cis-regulatory mechanisms from genomic deep neural networks using surrogate models

Published:2024-06-21 Issue:6 Volume:6 Page:701-713
ISSN:2522-5839
Container-title:Nature Machine Intelligence
language:en
Short-container-title:Nat Mach Intell

Author:

Seitz Evan E.^ORCID,McCandlish David M.^ORCID,Kinney Justin B.^ORCID,Koo Peter K.^ORCID

Funder

U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute

U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences

Alfred P. Sloan Foundation

Publisher

Springer Science and Business Media LLC

Link

https://www.nature.com/articles/s42256-024-00851-5.pdf

Reference71 articles.

1. Linder, J., Srivastava, D., Yuan, H., Agarwal, V. & Kelley, D. R. Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation. Preprint at bioRxiv https://doi.org/10.1101/2023.08.30.555582 (2023).

2. Avsec, Ž. et al. Effective gene expression prediction from sequence by integrating long-range interactions. Nat. Methods 18, 1196–1203 (2021).

3. Dudnyk, K., Cai, D., Shi, C., Xu, J. & Zhou, J. Sequence basis of transcription initiation in the human genome. Science 384, 694 (2024).

4. Jaganathan, K. et al. Predicting splicing from primary sequence with deep learning. Cell 176, 535–548.e24 (2019).

5. Avsec, Ž. et al. Base-resolution models of transcription-factor binding reveal soft motif syntax. Nat. Genet. 53, 354–366 (2021).

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