EPI-RMDL: Prediction of Enhancer-Promoter Interactions Based on RoFormer Mechanism and Deep Learning

Enhancer-Promoter Interactions (EPIs) play a crucial role in gene expression regulation. However, traditional experimental methods for detecting EPIs are often time-consuming and costly, prompting a growing demand for computational approaches. In this work, we propose a novel deep-learning model, termed EPI-RMDL, for the prediction of enhancer-promoter interactions based only on the DNA sequences. EPI-RMDL at first encodes the DNA sequences of a set of enhancers and promoters into an information matrix via the dna2vec method. Subsequently, local and global features are extracted from the matrix using a three-layer convolution neural network. Then, the features are processed through three RoFormers, which are enhanced transformers with Rotary Position Embedding (RoPE), in order to obtain relative positional information and interaction details between promoters and enhancers. Finally, a special matching mechanism is incorporated to analyze the interplay among the output vectors generated by the front-end RoFormers. We trained a general model by integrating data from six distinct cell lines and fine-tuned it with specific cell-line data to obtain an optimal model EPI-RMDL best for each cell line. Benchmarking against six state-of-the-art methods using datasets from six cell lines, our model demonstrates superior performance. Specifically, the EPI-RMDL_best model achieves a mean AUROC of 95.8% and an average AUPR of 80.8%.