Nature Communications

TRAPT: a multi-stage fused deep learning framework for predicting transcriptional regulators based on large-scale epigenomic data

G Guorui Zhang (Department of Chemistry, Advanced Institute of Future Energy, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, State Key Laboratory of Porous Materials for Separation and Conversion) C Chao Song M Mingxue Yin L Liyuan Liu Y Yuexin Zhang (State Key Laboratory of Functional Crystals and Devices, Fujian Institute of Research on the Structure of Matter) Y Ye Li J Jianing Zhang (Department of Land Resources and Urban Development Management, School of Public Policy and Administration, Chongqing University) M Maozu Guo C Chunquan Li
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Article Details

Volume / Issue Vol. 16, Issue 1
Published April 16, 2025
ISSN 2041-1723
Publisher Nature Portfolio

Journal Info

Nature Communications

Nature Portfolio
ISSN: 2041-1723 Open Access Life Sciences

Authors (9)

G

Guorui Zhang

Department of Chemistry, Advanced Institute of Future Energy, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, State Key Laboratory of Porous Materials for Separation and Conversion

C

Chao Song

M

Mingxue Yin

L

Liyuan Liu

Y

Yuexin Zhang

State Key Laboratory of Functional Crystals and Devices, Fujian Institute of Research on the Structure of Matter

Y

Ye Li

J

Jianing Zhang

Department of Land Resources and Urban Development Management, School of Public Policy and Administration, Chongqing University

M

Maozu Guo

C

Chunquan Li

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