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

Abstract

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.

Graphical abstract

Keywords

miRNA-disease association / deep matrix factorization / self-adjusted nearest neighbor / variational encoder / network structure

Pijing Wei received the PhD degree in computer science and technology from Anhui University, China in 2020. She is currently a lecturer in the Institute of Physical Science and Information Technology, Anhui University, China. Her main research interests include bioinformatics, synthetic biology, cancer data mining, and machine learning

Qianqian Wang received the BS degree in science from Anhui University of Science and Technology, China in 2021. She is currently pursuing the MS degree in the School of Computer Science and Technology, Anhui University, China. Her research interests include research of bioinformatics and deep learning

Zhen Gao received the MS degree in computer science from Qufu Normal University, China in 2021. She is currently working toward the PhD degree in the School of Computer Science and Technology, Anhui University, China. Her research interests include research of bioinformatics, deep learning and gene regulatory networks

Ruifen Cao received the PhD degree from Hefei Institute of Physical Sciences, Chinese Academy of Sciences, China in 2009. She is currently an associate professor at the School of Computer Science and Technology, Anhui University, China. Her research interests include artificial intelligence, medical image processing, and multimodal data fusion

Chunhou Zheng received the the PhD degree in pattern recognition and intelligent system in 2006, from University of Science and Technology of China. From February 2007 to June 2009, he worked as a Postdoctoral Fellow in the Hefei Institutes of Physical Sceience, Chinese Academy of Sciences, China. From July 2009 to July 2010, he worked as a Postdoctoral Fellow in the Department of Computing, the Hong Kong Polytechnic University, China. He is currently a Professor in the School of Artificial Intelligence, Anhui University, China. His research interests include pattern recognition, synthetic biology and bioinformatics

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Acknowledgments

This work was supported by 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), and the Anhui University Outstanding Youth Research Project (No. 2022AH020010).

Supplementary document

Our supplementary document consists of three parts. The first part is the Supplementary Figures related to the experiment, the second part is the Supplementary Tables related to the experiment, and the third part is the Supplementary Formulas related to the experimental results and some methods. The supporting information is available online at journal.hep.com.cn and link.springer.com.

Competing interests

The authors declare that they have no competing interests or financial conflicts to disclose.

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