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

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 of target gene cis-regulatory elements and genome-wide binding sites. The results of experiments on 570 TR-related…

• NN
  National Natural Science Foundation of China

  Awards: 62171166, 62302206, 62031003, 2021-01
• UO
  University of South China

  Award: 20224310NHYCG05