混合线性非线性网络的多源miRNA-疾病关联预测方法  

A Hybrid Linear and Nonlinear Network-Based Method on Multi-Source miRNA-Disease Association Prediction

作　　者：赵静 李浩琳 王会青 王彬 ZHAO Jing;LI Haolin;WANG Huiqing;WANG Bin(School of Information and Computer,Taiyuan University of Technology,Taiyuan 030600,China)

机构地区：[1]太原理工大学信息与计算机学院,太原030600

出　　处：《华东理工大学学报（自然科学版）》2023年第3期439-449,共11页Journal of East China University of Science and Technology

基　　金：山西省自然科学基金青年基金项目(20210302124272);国家自然科学基金面上项目(20210302123099)。

摘　　要：现有miRNA-疾病关联研究大多采用miRNA功能和疾病语义相似性作为输入,未考虑miRNA序列和疾病功能等相似性信息,并且在特征提取过程中忽略了线性与非线性特征间的信息互补,影响特征质量。本文提出一种miRNA-疾病关联预测模型GCNMSF,它基于miRNA和疾病多源相似性信息,融合嵌入卷积注意块的图卷积网络学习的非线性特征和非负矩阵分解方法学习的线性特征,实现信息互补,以预测miRNA-疾病关联。实验结果表明,GCNMSF模型优于现有的miRNA-疾病关联预测方法,可以有效预测miRNA-疾病关联。The miRNA is a single-stranded and small non-coding RNA,which is closely related to human diseases.The prediction on miRNA-disease associations can help understand the pathogenesis of diseases at the molecular level and provide a basis of studying the prognosis,diagnosis,evaluation and treatment of diseases.In the miRNA-disease association prediction,most methods take miRNA functional similarity and disease semantic similarity as input and don’t consider the similarity information including miRNA sequence,disease functional and hamming.Moreover,in the feature extraction process,they ignore the information complementarity between the linear features and nonlinear features such that the quality of feature extraction of miRNA and disease may be affected.Aiming at the above shortcomings,this paper proposes a novel miRNA-disease association prediction model,GCNMSF.First,we introduce the miRNA sequence similarity,disease semantic similarity and hamming similarity,and use similarity kernel fusion method to integrate multi-source similarities of miRNA and disease,respectively.Then,we use the graph convolutional network to learn nonlinear features and embed the convolutional attention block into GCN to optimize feature distribution.At the same time,the non-negative matrix factorization method is introduced to learn linear features of miRNA and disease for enriching the feature space and improving the ability of predicting miRNA-disease associations.Besides,we integrate the linear and nonlinear features of miRNA and disease to predict miRNA-disease associations.Finally,it is shown via experimental results on five-fold-cross validation that the proposed model in this work has better performance than some existing methods.It is also verified from the results of ablation experiment and case studies that the fusion of multi-source similarity information and the combination of linear and nonlinear features are helpful for miRNA-disease association prediction.In addition,the case studies of lung and breast cancers further conf

关 键 词：多源相似性 图卷积网络 非负矩阵分解 注意力机制 拓扑结构信息 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]