An Effective Hybrid Deep Learning Model for Eukaryotic Promoter Identification

Abstract

The promoter is a region located near the transcription start site (TSS) and responsible for the initiation and regulation of DNA transcription. Hence, accurate identification of promoters is essential for further building and understanding the mechanism of genetic regulatory networks. Numerous approaches for eukaryotic promoter identification were proposed. Nevertheless, the performances of these approaches are still unsatisfactory due to the variety nature of promoters. To extract more discriminative features and accurately identify eukaryotic promoters, here, we develop an effective hybrid deep learning model HDLMepi, which is able to characterize the original promoter sequences and the structural profiles of promoters simultaneously. We integrate the method we name PromoterClCce which characterizes the original promoter sequences and extracts sequence features, with an approach DSPN, which we design to model the structural profile of promoters and extract structure features, in HDLMepi for precisely eukaryotic promoter identification. We apply HDLMepi on both human and plants datasets and the experimental results demonstrate it is effective in promoter features extraction and can improve the performance of promoter identification significantly. HDLMepi is also open to add new features or new models and can be applied to other biology functional sequences.