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 decipher gene
regulation and understand disease mechanisms. However, experimental methods to identify EPIs are constrained
by funds, 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 literature. First, we 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 concluded with the challenges these methods are
confronted with and suggest several future opportunities. We hope this review will provide a useful reference
for further studies on enhancer-promoter interactions.
Keywords: Enhancer-promoter interactions, sequence features, prediction, deep learning, attention mechanism, word embedding, convolutional
neural network, recurrent neural network, interpretable model.
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