Nature Communications

An end-to-end generalizable deep learning framework to comprehensively analyze transcriptional regulation

Z Zhaoxi Zhang X Xiaoya Fan J Jiaxin Zhong L Lijuan Jia Y Yuanyuan Han C Chenyi Yang Z Zengyou He X Xiaoyan Li S Shing-Tung Yau (Yau Mathematical Sciences Center, Jingzhai, Tsinghua University) R Rongling Wu (Beijing Key Laboratory of Topological Statistics and Applications for Complex Systems, Beijing Institute of Mathematical Sciences and Applications) C Charles G. Danko (Department of Biomedical Sciences and Cornell Reproductive Sciences Center, College of Veterinary Medicine, Cornell University) Z Zhong Wang (Alan G. MacDiarmid NanoTech Institute, University of Texas at Dallas)
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Article Details

Volume / Issue Vol. 17, Issue 1
Published April 01, 2026
ISSN 2041-1723
Publisher Nature Portfolio

Journal Info

Nature Communications

Nature Portfolio
ISSN: 2041-1723 Open Access Life Sciences

Authors (12)

Z

Zhaoxi Zhang

X

Xiaoya Fan

J

Jiaxin Zhong

L

Lijuan Jia

Y

Yuanyuan Han

C

Chenyi Yang

Z

Zengyou He

X

Xiaoyan Li

S

Shing-Tung Yau

Yau Mathematical Sciences Center, Jingzhai, Tsinghua University

R

Rongling Wu

Beijing Key Laboratory of Topological Statistics and Applications for Complex Systems, Beijing Institute of Mathematical Sciences and Applications

C

Charles G. Danko

Department of Biomedical Sciences and Cornell Reproductive Sciences Center, College of Veterinary Medicine, Cornell University

Z

Zhong Wang

Alan G. MacDiarmid NanoTech Institute, University of Texas at Dallas

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