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ICNNMDA: An Improved Convolutional Neural Network for Predicting MiRNA-Disease Associations

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Intelligent Computing Theories and Application (ICIC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12838))

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Abstract

An increasing number of works have validated that the expression of miRNA is associated with human diseases. miRNA expression profiles may become an indicator for clinical diagnosis, classification, grading and even prognosis of tumors and other diseases, and provide new targets for treatment. In this work, we presented an improved convolutional neural network model to predict miRNA-disease association (ICNNMDA). For capturing more feature of miRNAs and diseases, we designed feature cell to train ICNNMDA, which contains miRNA-disease associations information and three kinds of miRNAs and diseases similarity information. In addition, an improved convolutional neural network was presented which consists of three convolutional layers, three pooling layers and two fully-connected layers, where dropout mechanism was adopted in the first fully-connected layers. Finally, 5CV and a case study were conducted to validate the effectiveness of the proposed model. The results showed that ICNNMDA can effectively identify disease-related miRNAs.

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Acknowledgment

This study was supported by the Natural Science Foundation of Shandong Province (grant number ZR2020KC022).

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Authors and Affiliations

  1. School of Cyber Science and Engineering, Qufu Normal University, Qufu, China

    Rui-Kang Ni, Zhen Gao & Cun-Mei Ji

Authors
  1. Rui-Kang Ni
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  2. Zhen Gao
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  3. Cun-Mei Ji
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Corresponding author

Correspondence to Cun-Mei Ji .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Shenzhen University, Shenzhen, China

    Jianqiang Li

  4. Far Eastern Branch of the Russian Academy of Sciences, Vladivostok, Russia

    Valeriya Gribova

  5. University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

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Ni, RK., Gao, Z., Ji, CM. (2021). ICNNMDA: An Improved Convolutional Neural Network for Predicting MiRNA-Disease Associations. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12838. Springer, Cham. https://doi.org/10.1007/978-3-030-84532-2_40

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  • DOI: https://doi.org/10.1007/978-3-030-84532-2_40

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  • Online ISBN: 978-3-030-84532-2

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