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Sequence-Based Deep Learning Frameworks on Enhancer-Promoter Interactions Prediction


Xiaoping Min, Fengqing Lu and Chunyan Li*   Pages 1 - 9 ( 9 )


Enhancer-promoter interactions (EPIs) in the human genome are of great significance to transcriptional regulation which tightly controls gene expression. Identification of EPIs can help us better deciphering gene regulation and understanding disease mechanisms. However, experimental methods to identify EPIs are constrained by the fund, time and manpower while computational methods using DNA sequences and genomic features are viable alternatives. Deep learning methods have shown promising prospects in classification and efforts that have been utilized to identify EPIs. In this survey, we specifically focus on sequence-based deep learning methods and conduct a comprehensive review of the literatures of them. We first briefly introduce existing sequence-based frameworks on EPIs prediction and their technique details. After that, we elaborate on the dataset, pre-processing means and evaluation strategies. Finally, we discuss the challenges these methods are confronted with and suggest several future opportunities.


Enhancer-promoter interactions, Sequence features, Prediction, Deep learning, Attention mechanism, Word embedding, Convolutional Neural Network, Recurrent Neural Network, Interpretable model


School of Informatics, Xiamen University, Xiamen 361005, School of Informatics, Xiamen University, Xiamen 361005, Graduate School, Yunnan Minzu University, Kunming 650504

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