A multi-perspective deep learning framework for enhancer characterization and identification

Abstract

Enhancers are vital elements in the genome that boost the transcriptional activity of neighboring genes and are essential in regulating cell-specific gene expression. Therefore, accurately identifying and characterizing enhancers is essential for comprehending gene regulatory networks and the development of related diseases. This study introduces MPDL-Enhancer, a novel multi-perspective deep learning framework aimed at enhancer characterization and identification. In this study, enhancer sequences are encoded using the dna2vec model along with features derived from the structural properties of DNA sequences. Subsequently, these representations are processed through a novel dual-scale deep neural network designed to discern subtle correlations and extended interactions embedded within the semantic content of DNA. The predictive phase of our methodology employs a Support Vector Machine classifier to render the final classification. To rigorously assess the efficacy of our approach, a comprehensive evaluation was executed utilizing an independent test dataset, thereby substantiating the robustness and accuracy of our model. Our methodology demonstrated superior performance over existing computational techniques, with an accuracy (ACC) of 81.00 %, a sensitivity (SN) of 79.00 %, and specificity (SP) of 83.00 %. The innovative dual-scale deep neural network and the unique feature representation strategy contributed to this performance improvement. MPDL-Enhancer has effectively characterized enhancer sequences and achieved excellent predictive performance. Building upon this foundation, we conducted an interpretability analysis of the model, which can assist researchers in identifying key features and patterns that affect the functionality of enhancers, thereby promoting a deeper understanding of gene regulatory networks.