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

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.