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LRMDA: Using Logistic Regression and Random Walk with Restart for MiRNA-Disease Association Prediction

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Intelligent Computing Theories and Application (ICIC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11644))

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Abstract

MicroRNAs (MiRNAs) have received much attention in recent years because growing evidences indicate that they play critical roles in tumor initiation and progression. Predicting underlying disease-related miRNAs from existing huge amount of biological data is a hot topic in biomedical research. Herein, we presented a novel computational model of logistic regression and random walk with restart algorithm for miRNA-disease association prediction (LRMDA) through integrating multi-source data. The model employs random walk with restart to fuse the association distribution between miRNAs and diseases and obtains highly discriminative feature from those heterogeneous data. To evaluate the performance of LRMDA, we performed 5-fold cross validation to compare it with several state-of-the-art models. As a result, our model achieves mean AUC of 0.9230 ± 0.0059. Besides, we carried out case study for predicting potential miRNAs related to Esophageal Neoplasms (EN). The achieved results indicate that 90% out of the top 50 prioritized miRNAs for EN are confirmed by biological experiments and further demonstrates the feasibility of our method. Therefore, LRMDA could potentially aid future research efforts for miRNA-disease association identification.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Grant No. 61873270, 61732012), the Jiangsu Postdoctoral Innovation Plan (Grant No. 1701031C), and the Jiangsu Shuangchuang Talents Program.

Author information

Authors and Affiliations

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

    Zhengwei Li, Ru Nie & Xin Ge

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

    Zhengwei Li & Yang Wang

  3. Institute of Machine Learning and Systems Biology, College of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China

    Zhengwei Li

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

    Zhengwei Li

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

    Zhuhong You

  6. School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, 21116, China

    Yan Zhao

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  1. Zhengwei Li
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  2. Ru Nie
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  3. Zhuhong You
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  4. Yan Zhao
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  5. Xin Ge
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  6. Yang Wang
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Correspondence to Zhengwei Li or Ru Nie .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

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

    Kang-Hyun Jo

  3. Nanchang Institute of Technology, Nanchang, China

    Zhi-Kai Huang

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Li, Z., Nie, R., You, Z., Zhao, Y., Ge, X., Wang, Y. (2019). LRMDA: Using Logistic Regression and Random Walk with Restart for MiRNA-Disease Association Prediction. In: Huang, DS., Jo, KH., Huang, ZK. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11644. Springer, Cham. https://doi.org/10.1007/978-3-030-26969-2_27

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  • DOI: https://doi.org/10.1007/978-3-030-26969-2_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26968-5

  • Online ISBN: 978-3-030-26969-2

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