DMFVAE: miRNA-disease associations prediction based on deep matrix factorization method with variational autoencoder

作     者：WEI Pijing WANG Qianqian GAO Zhen CAO Ruifen ZHENG Chunhou 

作者机构：Information Materials and Intelligent Sensing Laboratory of Anhui Province Institutes of Physical Science and Information Technology Anhui University Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Anhui University 

出 版 物：《Frontiers of Computer Science》 (计算机科学前沿(英文))

年 卷 期：2024年第18卷第6期

页      面：186912-186912页

核心收录：

基　　金：the National Natural Science Foundation of China (Grant Nos. 62202004, and 62322301) the Natural Science Foundation of Anhui Province (No. 2108085QF267) the University Synergy Innovation Program of Anhui Province (No. GXXT-2021-039) the Anhui University Outstanding Youth Research Project (No. 2022AH020010) 

主　　题：miRNA-disease association deep matrix factorization self-adjusted nearest neighbor variational encoder network structure 

摘      要：MicroRNAs (miRNAs) are closely related to numerous complex human diseases, therefore, exploring miRNA-disease associations (MDAs) can help people gain a better understanding of complex disease mechanism. An increasing number of computational methods have been developed to predict MDAs. However, the sparsity of the MDAs may hinder the performance of many methods. In addition, many methods fail to capture the nonlinear relationships of miRNA-disease network and inadequately leverage the features of network and neighbor nodes. In this study, we propose a deep matrix factorization model with variational autoencoder (DMFVAE) to predict potential MDAs. DMFVAE first decomposes the original association matrix and the enhanced association matrix, in which the enhanced association matrix is enhanced by self-adjusting the nearest neighbor method, to obtain sparse vectors and dense vectors, respectively. Then, the variational encoder is employed to obtain the nonlinear latent vectors of miRNA and disease for the sparse vectors, and meanwhile, node2vec is used to obtain the network structure embedding vectors of miRNA and disease for the dense vectors. Finally, sample features are acquired by combining the latent vectors and network structure embedding vectors, and the final prediction is implemented by convolutional neural network with channel attention. To evaluate the performance of DMFVAE, we conduct five-fold cross validation on the HMDD v2.0 and HMDD v3.2 datasets and the results show that DMFVAE performs well. Furthermore, case studies on lung neoplasms, colon neoplasms, and esophageal neoplasms confirm the ability of DMFVAE in identifying potential miRNAs for human diseases.