Journal article
A novel dual-attention deep neural network with multi-scale fusion feature processing for predicting transcription factor binding sites
IEEE journal of biomedical and health informatics, v 36(11), pp 1-11
01 Sep 2025
PMID: 40889326
Abstract
Transcription functions as a pivotal biological process in cell biology, which is required to complete the binding of transcription factors (TFs) to transcription factor binding sites (TFBSs) on the DNA. Accurate prediction of TFBSs can provide great potential to regulate the expression of interested genes, which can facilitate exploration of new drugs and treatment for diseases. Although many deep learning-based models have been proposed for predicting TFBSs, existing models still have problems, including the use of convolutional processing of DNA sequences that loses information about the DNA double helix structure and fails to adequately account for the stereoscopic structure of DNA shape data in three dimensions. In this paper, we propose a novel model called DeepCTMS, in which both sequence features and shape features of DNA slices are effectively fused to derive high-quality representations for the task of TFBS prediction. A sequence feature processing module is first used to extract the DNA double helix structure features of DNA slices. The three-dimensional features of DNA shape data are extracted by employing a convolutional triple attention (CTA) module for the shape data of a DNA slice. Finally, a multi-scale fusion feature processing (MSFFP) module is used to fuse sequence features and shape features to obtain representations with significantly aligned semantics of both features. Ablation experiments, t-SNE visual analysis, and cross-cell line validation results demonstrate that DeepCTMS consistently outperforms benchmark models on prediction performance and generalization ability on 165 ChIP-seq datasets.
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Details
- Title
- A novel dual-attention deep neural network with multi-scale fusion feature processing for predicting transcription factor binding sites
- Creators
- Yuechuan Dai - Central China Normal UniversityXianjun Shen - Central China Normal UniversityWeizhong Zhao - Central China Normal UniversityXiaohua Hu - Drexel University
- Publication Details
- IEEE journal of biomedical and health informatics, v 36(11), pp 1-11
- Publisher
- IEEE
- Number of pages
- 11
- Grant note
- National Key Research and Development Program of China: 2023YFB3001504 National Natural Science Foundation of China: 62306328, 62421002, U24A20333, 62302514 Science and Technology Innovation Program of Hunan Province: 2023RC3021, 2024RC1047
This work was supported in part by the National Key Research and Development Program of China under Grant 2023YFB3001504, in part by the National Natural Science Foundation of China under Grant 62306328, Grant 62421002, Grant U24A20333, and Grant 62302514, in part by the Science and Technology Innovation Program of Hunan Province under Grant 2023RC3021 and Grant 2024RC1047.
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:001581988100009
- Scopus ID
- 2-s2.0-105015061999
- Other Identifier
- 991022087474004721
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- Web of Science research areas
- Computer Science, Theory & Methods
- Engineering, Electrical & Electronic