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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.
An exploratory study on the introduction and removal of different types of technical debt in deep learning frameworks
