EnhPred: Deep Learning Model for Precise Prediction of Enhancer Positions

Jinseok Kim, Suyeon Wy, Jaebum Kim

http://doi.org/10.5626/JOK.2025.52.1.35

Enhancers are crucial regulatory elements that control gene expression in living organisms. Therefore, enhancer prediction is essential for a deeper understanding of gene regulation mechanisms. However, precise enhancer prediction is challenging due to their variable lengths and distant target genes. Existing artificial intelligence-based enhancer prediction methods often predict enhancers without identifying their boundaries accurately. In this study, we developed a new deep learning-based enhancer prediction method called EnhPred, which consisted of Convolutional Neural Networks (CNN) and bidirectional Gated Recurrent Units (GRU). To predict enhancer regions with a high resolution, we designed EnhPred to predict probabilities of enhancer presence within narrow segmented genomic regions. When evaluated with existing machine learning- and deep learning-based methods using data from three human cell lines, EnhPred demonstrated superior performances in terms of accuracy of enhancer prediction and resolution of enhancer boundaries.