iProm-EC: Identifying Promoters Using Embedding Layer and Convolutional Neural Network

A promoter is a short DNA sequence located near the start codon, responsible for initiating the transcription of specific genes in the genome. Accurate identification of promoters can help better understand the transcriptional regulation of genes. Identifying DNA promoters through traditional biochemical experimental methods is time-consuming and costly. In recent years, deep learning algorithms have shown excellent performance in the field of bioinformatics. Therefore, this paper proposes a new DNA promoter prediction model iProm-EC based on deep learning. This model automatically extracts sequence features of DNA promoters through embedding layers, and integrates architectures such as convolutional layers, pooling layers, and fully connected layers to classify promoters. The experimental results show that compared to other existing prediction models, iProm-EC has significantly improved in various indicators (Acc, Sn, Sp, Mcc, Auc). This indicates that the iProm-EC model proposed in this article has good predictive ability for DNA promoters.