| Enhancers are special regulatory regions on the genome that can affect the transcription level of target genes by binding to specific transcription factor proteins.Enhancers are generally considered to be one of the most important participating members in the whole process of gene expression regulation,so their identification has always been a very hot topic in bioinformatics.In recent years,more and more algorithms targeting at enhancer identification have been proposed,and great progress has been made in the prediction performance.However,these methods ignore the importance of the spatial structure features of DNA sequences for enhancer prediction.Related studies have shown that the three-dimensional structure of DNA plays a very important role in influencing the binding propensity of transcription factors to regulatory elements such as enhancers.In this study,we hypothesize that using the three-dimensional structural information of DNA can further improve the recognition effect of enhancers,and thus an enhancer identification algorithm based on deep learning and multi-feature fusion is proposed.The algorithm includes the following three key tasks:1.The DNA shape feature was introduced into enhancer identification for the first time,and the prediction performance of the algorithm was improved by using the threedimensional structural information of the enhancer region.2.In this paper,a novel multi-scale perception dual-attention module is designed.On the one hand,it can effectively extract the multi-scale information in the features.On the other hand,it uses the dual attention mechanism to learn the interdependence between spatial location features and channel features at the same time.3.Through multi-feature fusion of different feature representations of DNA sequences,information complementarity between different features is strengthened and redundant information is removed.Compared with the current SOTA method,our algorithm has obvious improvement in many indicators.Ablation experiment analysis shows that the introduction of DNA shape features and multi-scale double attention mechanism module is indeed helpful to improve the prediction performance of the algorithm,and the multi feature fusion method based on deep learning can effectively improve the recognition effect of enhancer. |
