基于多特征融合的增强子-启动子相互作用预测综述

Enhancer-Promoter Interaction Prediction Based on Multi-feature Fusion

研究增强子-启动子相互作用机理有助于人们理解基因调控关系,进而揭示与疾病相关的基因,为疾病诊疗提供新思路和新方法。传统的生物检测方法的实验成本高、耗时长,且受分辨率的限制,难以精确鉴定单个增强子-启动子的相互作用。通过计算方法来解决生物问题已成为近年来的研究热点,此类方法可以通过复杂的网络结构主动学习序列特征和空间结构,进而准确预测增强子-启动子的作用。首先介绍了传统生物实验检测方法的研究现状;然后从序列特征的角度出发,围绕多特征融合的基本思想,对统计学和深度学习方法在增强子-启动子相互作用预测上的应用进行归纳整理;最后对该领域的研究热点和挑战进行总结分析。

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.