DeepDualEPI: Predicting Promoter-Enhancer Interactions Based on DNA Sequence and Genomic Signals

Enhancer-promoter interactions are one of the essential mechanisms in the regulation of gene expression, and Accurate identification of enhancer-promoter interactions (EPIs) is challenging. In recent years, many deep learning methods have been used for EPI prediction. In this study, we propose DeepDualEPI, a dual-channel deep learning model based on genomic signals and DNA sequences, for predicting enhancer-promoter interactions (EPI). We used network architectures such as Dilated CNN, BiLSTM, and Transformer to process genomic signals, and network architectures such as multiscale CNN to extract DNA sequence features, and finally obtained hybrid features and output EPI prediction probabilities. To obtain the best combination of parameters for the model, we conducted several ablation experiments to optimize the model parameters. And to validate the performance of DeepDualEPI, we conducted experiments on four independent test sets to verify the generalization ability of the model. Compared with other state-of-the-art EPI prediction models, the DeepDualEPI model shows significant improvement in both AUC and AUPR evaluation metrics and experimentally demonstrates that better results are achieved on every chromosome, which proves that our model can stably perform EPI prediction across cell lines. And this paper demonstrates through ablation experiments that the inclusion of DNA sequence information can improve the performance of the model. Therefore, the two-channel hybrid feature deep learning approach via genomic signals and DNA sequences proposed in this paper helps to improve the overall accuracy of EPI prediction.