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

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

Abstract 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 repre...

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Bibliographic Details
Main Authors: Guorui Zhang, Chao Song, Mingxue Yin, Liyuan Liu, Yuexin Zhang, Ye Li, Jianing Zhang, Maozu Guo, Chunquan Li
Format: Article
Language:English
Published: Nature Portfolio 2025-04-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-025-58921-0
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