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

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成果类型：

期刊论文

作者：

Zhang, Guorui;Song, Chao;Yin, Mingxue;Liu, Liyuan;Zhang, Yuexin;...

通讯作者：

Li, CQ;Guo, MZ

作者机构：

[Zhang, Yuexin; Li, Ye; Yin, Mingxue; Li, Chunquan; Liu, Liyuan; Zhang, Guorui; Zhang, Jianing; Song, Chao] Univ South China, Affiliated Hosp 1, Hengyang 421001, Hunan, Peoples R China.

[Zhang, Yuexin; Li, Ye; Yin, Mingxue; Li, Chunquan; Liu, Liyuan; Zhang, Guorui; Zhang, Jianing; Song, Chao] Univ South China, Hengyang Med Sch, Natl Hlth Commiss Key Lab Birth Defect Res & Preve, Hengyang 421001, Hunan, Peoples R China.

[Zhang, Yuexin; Li, Ye; Yin, Mingxue; Li, Chunquan; Liu, Liyuan; Zhang, Guorui; Zhang, Jianing; Song, Chao] Univ South China, Hunan Prov Key Lab Multiomics&Artificial Intellige, Hengyang 421001, Hunan, Peoples R China.

[Li, Ye; Yin, Mingxue; Li, Chunquan; Liu, Liyuan; Zhang, Guorui] Univ South China, Sch Basic Med Sci, Hengyang Med Sch, Dept Biochem & Mol Biol, Hengyang 421001, Hunan, Peoples R China.

[Li, Chunquan; Song, Chao] Univ South China, Sch Comp, Hengyang 421001, Hunan, Peoples R China.

通讯机构：

[Li, CQ ] U

[Guo, MZ ] B

Univ South China, Affiliated Hosp 1, Hengyang 421001, Hunan, Peoples R China.

Univ South China, Hengyang Med Sch, Natl Hlth Commiss Key Lab Birth Defect Res & Preve, Hengyang 421001, Hunan, Peoples R China.

Univ South China, Hunan Prov Key Lab Multiomics&Artificial Intellige, Hengyang 421001, Hunan, Peoples R China.

语种：

英文

期刊：

Nature Communications

ISSN：

2041-1723

年：

2025

卷：

期：

页码：

3611

DOI：

10.1038/s41467-025-58921-0

基金类别：

Science and technology innovation Program of Hunan Province [2024RC1062, 2024RC3212]; National Natural Science Foundation of China [62171166, 62302206]; Research Foundation of the First Affiliated Hospital of University of South China for Advanced Talents [20210002-1005 USCAT-2021-01]; Provincial Key Laboratory of Multiomics and Artificial Intelligence of Cardiovascular Diseases [2023TP1047]; Natural Science Foundation of Hunan Province [2023JJ40594, 2023JJ30536]; Clinical Research 4310 Program of the University of South China [20224310NHYCG05]; [62031003]

机构署名：

本校为第一且通讯机构

院系归属：

化学化工学院

药学与生物科学学院

摘要：

It is challenging to identify regulatory transcriptional regulators (TRs), which control gene expression via regulatory elements and epigenomic signals, in context-specific studies on the onset and progression of diseases. The use of large-scale multi-omics epigenomic data enables the representation of the complex epigenomic patterns of control of the regulatory elements and the regulators. Herein, we propose Transcription Regulator Activity Prediction Tool (TRAPT), a multi-modality deep learning framework, which infers regulator activity by learning and integrating the regulatory potentials o...

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

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