Evidence map›Paper›PMID 41677998›Full record

ArticleJournal of computer-aided molecular design2026

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

Xin Wang, Chang Liu, Witold Pedrycz, Wenhui Shang

Abstract read
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In one paragraph

Article in Journal of computer-aided molecular design, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Xin WangSchool of Science, Dalian Maritime University, Dalian, 116026, China. xenawang@dlmu.edu.cn.ORCID 0000-0001-9001-2192
Chang LiuSchool of Science, Dalian Maritime University, Dalian, 116026, China.ORCID 0009-0003-4918-762X
Witold PedryczDepartment of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, T6G 2R3, Canada.ORCID 0000-0002-9335-9930
Wenhui ShangSchool of Science, Dalian Maritime University, Dalian, 116026, China.ORCID 0009-0009-3252-1883

Funding

the Basic Scientific Research Project of the Educational Department of Liaoning Province LJ212510151022
6 · The paper itself

Abstract

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.

Indexed as

BacteriaComputational BiologyDeep LearningPromoter Regions, GeneticConvolutional Neural NetworksGene Expression Regulation, BacterialBacterial promotersConvolutional block attention module (CBAM)Deep learning

Identifiers

PMID41677998

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.