ReHoGCNES-MDA: prediction of miRNA-disease associations using homogenous graph convolutional networks based on regular graph with random edge sampler

Author:

Zhang Yufang123ORCID,Chu Yanyi4,Lin Shenggeng56ORCID,Xiong Yi567ORCID,Wei Dong-Qing2356ORCID

Affiliation:

1. School of Mathematical Sciences and SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University , Shanghai 200240 , China

2. Peng Cheng Laboratory , Shenzhen, Guangdong 518055 , China

3. Zhongjing Research and Industrialization Institute of Chinese Medicine , Zhongguancun Scientific Park, Meixi, Nanyang, Henan, 473006 , China

4. Department of Pathology, Stanford University School of Medicine , Stanford, CA, 94305 , USA

5. State Key Laboratory of Microbial Metabolism , School of Life Sciences and Biotechnology, and Joint Laboratory of International Cooperation in Metabolic and Developmental Sciences, Ministry of Education, , Shanghai 200240 , China

6. Shanghai Jiao Tong University , School of Life Sciences and Biotechnology, and Joint Laboratory of International Cooperation in Metabolic and Developmental Sciences, Ministry of Education, , Shanghai 200240 , China

7. Shanghai Artificial Intelligence Laboratory , Shanghai, 200232 , China

Abstract

Abstract Numerous investigations increasingly indicate the significance of microRNA (miRNA) in human diseases. Hence, unearthing associations between miRNA and diseases can contribute to precise diagnosis and efficacious remediation of medical conditions. The detection of miRNA-disease linkages via computational techniques utilizing biological information has emerged as a cost-effective and highly efficient approach. Here, we introduced a computational framework named ReHoGCNES, designed for prospective miRNA-disease association prediction (ReHoGCNES-MDA). This method constructs homogenous graph convolutional network with regular graph structure (ReHoGCN) encompassing disease similarity network, miRNA similarity network and known MDA network and then was tested on four experimental tasks. A random edge sampler strategy was utilized to expedite processes and diminish training complexity. Experimental results demonstrate that the proposed ReHoGCNES-MDA method outperforms both homogenous graph convolutional network and heterogeneous graph convolutional network with non-regular graph structure in all four tasks, which implicitly reveals steadily degree distribution of a graph does play an important role in enhancement of model performance. Besides, ReHoGCNES-MDA is superior to several machine learning algorithms and state-of-the-art methods on the MDA prediction. Furthermore, three case studies were conducted to further demonstrate the predictive ability of ReHoGCNES. Consequently, 93.3% (breast neoplasms), 90% (prostate neoplasms) and 93.3% (prostate neoplasms) of the top 30 forecasted miRNAs were validated by public databases. Hence, ReHoGCNES-MDA might serve as a dependable and beneficial model for predicting possible MDAs.

Funder

National Science Foundation of China

Intergovernmental International Scientific and Technological Innovation and Cooperation Program of The National Key R&D Program

Joint Research Funds for Medical and Engineering and Scientific Research at Shanghai Jiao Tong University

Publisher

Oxford University Press (OUP)

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