智能系统学报2023，Vol.18Issue(6)：1165-1172,8.DOI:10.11992/tis.202212029
融合Doc2vec与GCN的多类型蛋白质相互作用预测方法
Prediction of multitype protein interactions combining Doc2vec and GCN
摘要
Abstract
The study of multitype protein-protein interactions(PPIs)is the basis for understanding biological processes and revealing disease mechanisms from a systematic perspective.Existing prediction methods for multiple types of PPIs,such as GNN-PPI and PIPR,show a considerable decline in test accuracy when the breadth-and depth-first searches are used to divide data sets.Therefore,this paper proposes a new multitype PPI prediction method(GDP)based on the Doc2vec method and graph convolutional neural network technology,which does not need to rely on the physical and biological properties of proteins.Moreover,the method only uses sequence information to encode proteins and com-bines the network structure information to conduct characteristic protein polymerization for developing PPI information to perform multitype prediction.Experimental results show that this method can effectively improve the prediction ac-curacy of multiple type PPIs in real data with different scales,especially in PPI between new proteins that have not been previously observed in the training set.关键词
PPI网络/图神经网络/蛋白质功能预测/深度学习/生物学意义/复杂网络/图卷积神经网络/非监督学习/蛋白质序列Key words
PPI network/graph neural network/protein function prediction/deep learning/biological significance/com-plex network/GCN/unsupervised learning/protein sequence分类
计算机与自动化引用本文复制引用
曹汉童,陈璟..融合Doc2vec与GCN的多类型蛋白质相互作用预测方法[J].智能系统学报,2023,18(6):1165-1172,8.基金项目
江苏省青年自然科学基金项目(BK20150159). （BK20150159）
