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DNRLCNN: A CNN Framework for Identifying MiRNA–Disease Associations Using Latent Feature Matrix Extraction with Positive Samples

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Abstract

Emerging evidence indicates that miRNAs have strong relationships with many human diseases. Investigating the associations will contribute to elucidating the activities of miRNAs and pathogenesis mechanisms, and providing new opportunities for disease diagnosis and drug discovery. Therefore, it is of significance to identify potential associations between miRNAs and diseases. The existing databases about the miRNA–disease associations (MDAs) only provide the known MDAs, which can be regarded as positive samples. However, the unknown MDAs are not sufficient to regard as reliable negative samples. To deal with this uncertainty, we proposed a convolutional neural network (CNN) framework, named DNRLCNN, based on a latent feature matrix extracted by only positive samples to predict MDAs. First, by only considering the positive samples into the calculation process, we captured the latent feature matrix for complex interactions between miRNAs and diseases in low-dimensional space. Then, we constructed a feature vector for each miRNA and disease pair based on the feature representation. Finally, we adopted a modified CNN for the feature vector to predict MDAs. As a result, our model achieves better performance than other state-of-the-art methods which based CNN in fivefold cross-validation on both miRNA–disease association prediction task (average AUC of 0.9030) and miRNA–phenotype association prediction task (average AUC of 0. 9442). In addition, we carried out case studies on two human diseases, and all the top-50 predicted miRNAs for lung neoplasms are confirmed by HMDD v3.2 and dbDEMC 2.0 databases, 98% of the top-50 predicted miRNAs for heart failure are confirmed. The experiment results show that our model has the capability of inferring potential disease-related miRNAs.

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Acknowledgements

This work is supported in part by the Natural Science Foundation of Hunan Province of China (Nos.2018JJ2262, 2020JJ5373), the CSC (No. 201906725017), the Scientific Research Fund of Hunan Provincial Education Department (Nos.15CY007, 22A211, 20B348), Natural Science Foundation of Yunnan Province of China (2019FA024), and the National Natural Science Foundation of China (No. 62002116, 61972185)

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Authors and Affiliations

  1. College of Information Science and Engineering, Hunan Normal University, Changsha, 410083, China

    Jiancheng Zhong, Wubin Zhou, Jiedong Kang, Zhuo Fang, Minzhu Xie & Qiu Xiao

  2. Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 410083, China

    Jiancheng Zhong

  3. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, China

    Wei Peng

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  1. Jiancheng Zhong
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Correspondence to Qiu Xiao or Wei Peng.

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Zhong, J., Zhou, W., Kang, J. et al. DNRLCNN: A CNN Framework for Identifying MiRNA–Disease Associations Using Latent Feature Matrix Extraction with Positive Samples. Interdiscip Sci Comput Life Sci 14, 607–622 (2022). https://doi.org/10.1007/s12539-022-00509-z

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