PGBTR: a powerful and general method for inferring bacterial transcriptional regulatory networks
Abstract Predicting bacterial transcriptional regulatory networks (TRNs) through computational methods is a core challenge in systems biology, and there is still a long way to go. Here we propose a powerful, general, and stable computational framework called PGBTR (Powerful and General Bacterial Tra...
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BMC
2025-08-01
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| Series: | BMC Genomics |
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| Online Access: | https://doi.org/10.1186/s12864-025-11863-9 |
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| author | Wei-Cheng Gu Bin-Guang Ma |
| author_facet | Wei-Cheng Gu Bin-Guang Ma |
| author_sort | Wei-Cheng Gu |
| collection | DOAJ |
| description | Abstract Predicting bacterial transcriptional regulatory networks (TRNs) through computational methods is a core challenge in systems biology, and there is still a long way to go. Here we propose a powerful, general, and stable computational framework called PGBTR (Powerful and General Bacterial Transcriptional Regulatory networks inference method), which employs Convolutional Neural Networks (CNN) to predict bacterial transcriptional regulatory relationships from gene expression data and genomic information. PGBTR consists of two main components: the input generation step PDGD (Probability Distribution and Graph Distance) and the deep learning model CNNBTR (Convolutional Neural Networks for Bacterial Transcriptional Regulation inference). On the real Escherichia coli and Bacillus subtilis datasets, PGBTR outperforms other advanced supervised and unsupervised learning methods in terms of AUROC (Area Under the Receiver Operating Characteristic Curve), AUPR (Area Under Precision-Recall Curve), and F1-score. Moreover, PGBTR exhibits greater stability in identifying real transcriptional regulatory interactions compared to existing methods. PGBTR provides a new software tool for bacterial TRNs inference, and its core ideas can be further extended to other molecular network inference tasks and other biological problems using gene expression data. |
| format | Article |
| id | doaj-art-a1e0a9768a694ad0a8c46e633d7c4cb1 |
| institution | Kabale University |
| issn | 1471-2164 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj-art-a1e0a9768a694ad0a8c46e633d7c4cb12025-08-20T04:01:47ZengBMCBMC Genomics1471-21642025-08-0126111010.1186/s12864-025-11863-9PGBTR: a powerful and general method for inferring bacterial transcriptional regulatory networksWei-Cheng Gu0Bin-Guang Ma1Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural UniversityHubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural UniversityAbstract Predicting bacterial transcriptional regulatory networks (TRNs) through computational methods is a core challenge in systems biology, and there is still a long way to go. Here we propose a powerful, general, and stable computational framework called PGBTR (Powerful and General Bacterial Transcriptional Regulatory networks inference method), which employs Convolutional Neural Networks (CNN) to predict bacterial transcriptional regulatory relationships from gene expression data and genomic information. PGBTR consists of two main components: the input generation step PDGD (Probability Distribution and Graph Distance) and the deep learning model CNNBTR (Convolutional Neural Networks for Bacterial Transcriptional Regulation inference). On the real Escherichia coli and Bacillus subtilis datasets, PGBTR outperforms other advanced supervised and unsupervised learning methods in terms of AUROC (Area Under the Receiver Operating Characteristic Curve), AUPR (Area Under Precision-Recall Curve), and F1-score. Moreover, PGBTR exhibits greater stability in identifying real transcriptional regulatory interactions compared to existing methods. PGBTR provides a new software tool for bacterial TRNs inference, and its core ideas can be further extended to other molecular network inference tasks and other biological problems using gene expression data.https://doi.org/10.1186/s12864-025-11863-9Transcriptional regulatory networkEscherichia coliBacillus subtilisDeep learningNetwork inference |
| spellingShingle | Wei-Cheng Gu Bin-Guang Ma PGBTR: a powerful and general method for inferring bacterial transcriptional regulatory networks BMC Genomics Transcriptional regulatory network Escherichia coli Bacillus subtilis Deep learning Network inference |
| title | PGBTR: a powerful and general method for inferring bacterial transcriptional regulatory networks |
| title_full | PGBTR: a powerful and general method for inferring bacterial transcriptional regulatory networks |
| title_fullStr | PGBTR: a powerful and general method for inferring bacterial transcriptional regulatory networks |
| title_full_unstemmed | PGBTR: a powerful and general method for inferring bacterial transcriptional regulatory networks |
| title_short | PGBTR: a powerful and general method for inferring bacterial transcriptional regulatory networks |
| title_sort | pgbtr a powerful and general method for inferring bacterial transcriptional regulatory networks |
| topic | Transcriptional regulatory network Escherichia coli Bacillus subtilis Deep learning Network inference |
| url | https://doi.org/10.1186/s12864-025-11863-9 |
| work_keys_str_mv | AT weichenggu pgbtrapowerfulandgeneralmethodforinferringbacterialtranscriptionalregulatorynetworks AT binguangma pgbtrapowerfulandgeneralmethodforinferringbacterialtranscriptionalregulatorynetworks |