iProm-Sigma54: A CNN Base Prediction Tool for σ⁵⁴ Promoters

The sigma ((Formula presented.)) factor of RNA holoenzymes is essential for identifying and binding to promoter regions during gene transcription in prokaryotes. (Formula presented.) promoters carried out various ancillary methods and environmentally responsive procedures; therefore, it is crucial to accurately identify (Formula presented.) promoter sequences to comprehend the underlying process of gene regulation. Herein, we come up with a convolutional neural network (CNN) based prediction tool named “iProm-Sigma54” for the prediction of (Formula presented.) promoters. The CNN consists of two one-dimensional convolutional layers, which are followed by max pooling layers and dropout layers. A one-hot encoding scheme was used to extract the input matrix. To determine the prediction performance of iProm-Sigma54, we employed four assessment metrics and five-fold cross-validation; performance was measured using a benchmark and test dataset. According to the findings of this comparison, iProm-Sigma54 outperformed existing methodologies for identifying (Formula presented.) promoters. Additionally, a publicly accessible web server was constructed.