Affiliation:
1. Department of Computer Science, Xiamen University, Xiamen, China
2. School of Information Science and Engineering, Hunan University, Changsha, China
Abstract
Abstract
Enhancer-promoter interactions (EPIs) play an important role in transcriptional regulation. Recently, machine learning-based methods have been widely used in the genome-scale identification of EPIs due to their promising predictive performance. In this paper, we propose a novel method, termed EPI-DLMH, for predicting EPIs with the use of DNA sequences only. EPI-DLMH consists of three major steps. First, a two-layer convolutional neural network is used to learn local features, and an bidirectional gated recurrent unit network is used to capture long-range dependencies on the sequences of promoters and enhancers. Second, an attention mechanism is used for focusing on relatively important features. Finally, a matching heuristic mechanism is introduced for the exploration of the interaction between enhancers and promoters. We use benchmark datasets in evaluating and comparing the proposed method with existing methods. Comparative results show that our model is superior to currently existing models in multiple cell lines. Specifically, we found that the matching heuristic mechanism introduced into the proposed model mainly contributes to the improvement of performance in terms of overall accuracy. Additionally, compared with existing models, our model is more efficient with regard to computational speed.
Funder
Xiamen University
Natural Science Foundation of Fujian Province
Natural Science Research of Jiangsu Higher Education Institutions of China
National Natural Science Foundation of China
Publisher
Oxford University Press (OUP)
Subject
Molecular Biology,Information Systems
Cited by
40 articles.
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