A New Computational Method Based on Heterogeneous Network for Predicting MicroRNA-Disease Associations

  • Thanh Van Thai
  • Duong Hung Bui
  • Xuan Tho Dang
  • Thanh-Phuong Nguyen
  • Dang Hung TranEmail author
  • The Dung Luong
Part of the Studies in Computational Intelligence book series (SCI, volume 899)


MicroRNAs (miRNAs) are a class of small non-coding RNAs that are involved in the development of various complex human diseases. A great effort has spent to uncover the relations between miRNAs and diseases for decades. Although most of known miRNA-disease associations are discovered by experimental methods, the experimental methods are in general expensive and time-consuming. Another approach using computational methods to predict potential miRNA-disease associations has been attracted many computer scientists in recent years. However, computational methods suffer from various limitations that affect the prediction accuracy and their applicability. In this paper, we proposed a new computational method that would be able to predict reliable miRNA-disease associations. We integrate different biological data sources such as known miRNA-disease associations, miRNA-miRNA functional similarity, and disease-disease semantic similarity into a miRNA-disease heterogeneous network. The structural characteristics of this network are represented as a feature vector dataset via meta-paths and a binary classification problem is formulated. However, because the number of known miRNA-disease associations is very small, we face with an imbalance data classification problem. To solve this issue, a clustering-based under-sampling algorithm has been proposed. Training classification models using SVMs, we obtained results of 2–5% higher in AUC measures when compared to previous methods. These results implied that our proposed model could be used to discover reliable miRNA-disease associations in the human genome.



This research was supported by the Vietnam Ministry of Education and Training, project B2018-SPH-52.


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Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021

Authors and Affiliations

  • Thanh Van Thai
    • 1
  • Duong Hung Bui
    • 2
  • Xuan Tho Dang
    • 3
  • Thanh-Phuong Nguyen
    • 4
  • Dang Hung Tran
    • 3
    Email author
  • The Dung Luong
    • 1
  1. 1.Academy of Cryptography TechniquesHanoiVietnam
  2. 2.Hanoi Trade Union UniversityHanoiVietnam
  3. 3.Hanoi National University of EducationHanoiVietnam
  4. 4.Life Science Research Unit - Systems Biology GroupUniversity of LuxembourgMegenoLuxembourg

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