基于多特征融合的增强子-启动子相互作用预测综述

doi:10.11896/jsjkx.191100027

Abstract

Abstract: The study of the mechanism of Enhancer-Promoter Interaction is helpful to understand gene regulations,thus revealing specific genes that are relevant to diseases as well as providing new clinical methods and ideas for disease diagnosis and treatment.Compared to traditional biological analysis methods which are always more expensive,time-consuming and more difficult to precisely identify specific interactions due to limited resolution,computational methods to solve biological problems have become a hot research topic in recent years.This method can actively learn sequence features and spatial structures through complex network structures,so as to precisely and accurately predict the interactions of enhancers and promoters.This paper firstly introduces the research status of traditional biological detection methods.Then,from the perspective of sequence features,the application of statistics and deep learning method in the prediction of enhancer - promoter interaction is summarized and sorted out based on the basic idea of multi-feature fusion.Finally,the research hotspots and challenges in this field are summarized and analyzed.

Key words: Application overview, Disease diagnosis and treatment, Enhancer-promoter interaction, Multi-feature fusion, Sequence feature

CLC Number:

TP391

HU Yu-jia, GAN Wei, ZHU Min. Enhancer-Promoter Interaction Prediction Based on Multi-feature Fusion[J].Computer Science, 2020, 47(5): 64-71.

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