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Predicting LncRNA–Disease Association by a Random Walk With Restart on Multiplex and Heterogeneous Networks

  • Yao, Yuhua1, 2, 3
  • Ji, Binbin4
  • Lv, Yaping1
  • Li, Ling5
  • Xiang, Ju6, 7, 8
  • Liao, Bo1
  • Gao, Wei9
  • 1 School of Mathematics and Statistics, Hainan Normal University, Haikou , (China)
  • 2 Key Laboratory of Data Science and Intelligence Education, Ministry of Education, Hainan Normal University, Haikou , (China)
  • 3 Key Laboratory of Computational Science and Application of Hainan Province, Hainan Normal University, Haikou , (China)
  • 4 Geneis Beijing Co., Ltd., Beijing , (China)
  • 5 Basic Courses Department, Zhejiang Shuren University, Hangzhou , (China)
  • 6 School of Computer Science and Engineering, Central South University, Changsha , (China)
  • 7 Department of Basic Medical Sciences, Changsha Medical University, Changsha , (China)
  • 8 Department of Computer Science, Changsha Medical University, Changsha , (China)
  • 9 Departments of Internal Medicine-Oncology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou , (China)
Published Article
Frontiers in Genetics
Frontiers Media SA
Publication Date
Aug 19, 2021
DOI: 10.3389/fgene.2021.712170
  • Genetics
  • Original Research


Studies have found that long non-coding RNAs (lncRNAs) play important roles in many human biological processes, and it is critical to explore potential lncRNA–disease associations, especially cancer-associated lncRNAs. However, traditional biological experiments are costly and time-consuming, so it is of great significance to develop effective computational models. We developed a random walk algorithm with restart on multiplex and heterogeneous networks of lncRNAs and diseases to predict lncRNA–disease associations (MHRWRLDA). First, multiple disease similarity networks are constructed by using different approaches to calculate similarity scores between diseases, and multiple lncRNA similarity networks are also constructed by using different approaches to calculate similarity scores between lncRNAs. Then, a multiplex and heterogeneous network was constructed by integrating multiple disease similarity networks and multiple lncRNA similarity networks with the lncRNA–disease associations, and a random walk with restart on the multiplex and heterogeneous network was performed to predict lncRNA–disease associations. The results of Leave-One-Out cross-validation (LOOCV) showed that the value of Area under the curve (AUC) was 0.68736, which was improved compared with the classical algorithm in recent years. Finally, we confirmed a few novel predicted lncRNAs associated with specific diseases like colon cancer by literature mining. In summary, MHRWRLDA contributes to predict lncRNA–disease associations.

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