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FCGCNMDA: predicting miRNA-disease associations by applying fully connected graph convolutional networks

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Abstract

Growing evidence indicates that the development and progression of multiple complex diseases are influenced by microRNA (miRNA). Identifying more miRNAs as biomarkers for clinical diagnosis, treatment and prognosis is vital to promote the development of bioinformatics and medicine. Considering that the traditional biological experimental methods are generally time-consuming and expensive, high-efficient computational methods are encouraged to uncover potential disease-related miRNAs. In this paper, FCGCNMDA is presented to predict latent miRNA-disease associations by utilizing fully connected graph convolutional networks. Specially, our method first constructs a fully connected graph in which edge weights represent correlation coefficient between any two pairs of miRNA-disease pair, and then feeds this fully connected graph along with miRNA-disease pairs feature matrix into a two-layer graph convolutional networks (GCN) for training. At last, we utilize the trained network to predict the scores for unknown miRNA-disease pairs. As a result, FCGCNMDA achieves AUC value of \(92.85 \pm 0.71{\text{\% }}\) and AUPRC value of \(92.55{\text{\% }}\) in HMDD v2.0 based on five-fold cross validation. Moreover, case studies on Lymphoma, Breast Neoplasms and Prostate Neoplasms shown that 98%, 98%, 98% of the top 50 selected miRNAs were validated by recent experimental evidence. From above results, we can deduce that FCGCNMDA can be regarded as reliable method for potential miRNA-disease associations prediction.

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Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

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Funding

This work is supported in part by the National Natural Science Foundation of China, under Grants 61722212, 61873270, 61902337, 61732012 and 61972399. The publication costs are funded by the grant 61722212. The funders have no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript. The authors would like to thank all anonymous reviewers for their constructive advices.

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  1. Jiashu Li and Zhengwei Li contributed equally to this work.

Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China

    Jiashu Li, Zhengwei Li & Ru Nie

  2. Mine Digitization Engineering Research Center of Ministry of Education, China University of Mining and Technology, Xuzhou, 221116, China

    Jiashu Li, Zhengwei Li & Ru Nie

  3. KUNPAND Communications (Kunshan) Co., Ltd, Suzhou, 215300, China

    Zhengwei Li

  4. Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi, 830011, China

    Zhuhong You

  5. School of Information and Electrical Engineering, Xuzhou University of Technology, Xuzhou, 221018, China

    Wenzhang Bao

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  1. Jiashu Li
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Contributions

Z-WL and RN conceived the algorithm, carried out the analyses, prepared the data sets, carried out experiments, and wrote the manuscript. J-SL, Z-HY and W-ZB designed, performed and analyzed experiments and wrote the manuscript. All authors read and approved the final manuscript.

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Correspondence to Zhengwei Li or Ru Nie.

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Jiashu Li declares that he has no conflict of interest. Zhengwei Li declares that he has no conflict of interest. Ru Nie declares that she has no conflict of interest. Zhuhong You declares that he has no conflict of interest. Wenzhang Bao declares that he has no conflict of interest.

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Communicated by: Stefan Hohmann.

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Li, J., Li, Z., Nie, R. et al. FCGCNMDA: predicting miRNA-disease associations by applying fully connected graph convolutional networks. Mol Genet Genomics 295, 1197–1209 (2020). https://doi.org/10.1007/s00438-020-01693-7

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