Journal Article

PGBTR: a powerful and general method for inferring bacterial transcriptional regulatory networks

Ma, Bin-Guang, Gu, Wei-Cheng

2025

Overview

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.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Animal Genetics and Genomics / Artificial neural networks / Bacillus subtilis / Bacillus subtilis - genetics / Bacteria / Bacterial genetics / Biomedical and Life Sciences / Computational Biology - methods / Computer applications / Datasets / Deep Learning / Distribution (Probability theory) / E coli / Escherichia coli / Escherichia coli - genetics / Gene expression / Gene Expression Regulation, Bacterial / Gene regulation / Gene Regulatory Networks / Genetic aspects / Genetic research / Genomes / Graph representations / Inference / Life Sciences / Machine learning / Mathematical models / Microarrays / Microbial Genetics and Genomics / Network inference / Neural networks / Neural Networks, Computer / Physiological aspects / Plant Genetics and Genomics / Probability distribution / Proteomics / Software / Support vector machines / Transcription / Transcription factors / Transcriptional regulatory network / Unsupervised learning