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<p>Promoters are key DNA elements that regulate bacterial gene expression, yet most existing computational methods demonstrate limited effectiveness in predicting promoters across diverse bacterial species. Here, we propose PBP_ICBA, a deep learning model featuring a dual-path architecture that integrates two-dimensional convolution and improved Convolutional Block Attention Module for accurate species-specific bacterial promoter identification. The model employs a comprehensive encoding scheme combining one-hot encoding, Nucleotide Chemical Property C2, and ESM-2 representations. Evaluation on 13 species-specific bacterial promoter datasets shows that PBP_ICBA achieves superior performance in 11 species. This study provides a robust framework for species-specific bacterial promoter prediction and enhances our understanding of transcriptional regulatory mechanisms. Research data is available in this public repository: <a href="https://github.com/liuchang-chun">https://github.com/liuchang-chun</a> /PBP_ICBAA.</p>

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PBP_ICBA: a prediction of bacterial promoters in specific organisms using an improved convolutional block attention module

  • 期刊论文

  • 发表日期：

    2026年2月12日

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  • Xin Wang,
  • Chang Liu,
  • Witold Pedrycz,
  • Wenhui Shang

摘要

Promoters are key DNA elements that regulate bacterial gene expression, yet most existing computational methods demonstrate limited effectiveness in predicting promoters across diverse bacterial species. Here, we propose PBP_ICBA, a deep learning model featuring a dual-path architecture that integrates two-dimensional convolution and improved Convolutional Block Attention Module for accurate species-specific bacterial promoter identification. The model employs a comprehensive encoding scheme combining one-hot encoding, Nucleotide Chemical Property C2, and ESM-2 representations. Evaluation on 13 species-specific bacterial promoter datasets shows that PBP_ICBA achieves superior performance in 11 species. This study provides a robust framework for species-specific bacterial promoter prediction and enhances our understanding of transcriptional regulatory mechanisms. Research data is available in this public repository: https://github.com/liuchang-chun /PBP_ICBAA.