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Prediction of lncRNA–Protein Interactions via the Multiple Information Integration

  • Chen, Yifan1, 2
  • Fu, Xiangzheng1
  • Li, Zejun2
  • Peng, Li3
  • Zhuo, Linlin4
  • 1 College of Information Science and Engineering, Hunan University, Changsha , (China)
  • 2 School of Computer and Information Science, Hunan Institute of Technology, Hengyang , (China)
  • 3 College of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan , (China)
  • 4 Department of Mathematics and Information Engineering, Wenzhou University Oujiang College, Wenzhou , (China)
Published Article
Frontiers in Bioengineering and Biotechnology
Frontiers Media SA
Publication Date
Feb 25, 2021
DOI: 10.3389/fbioe.2021.647113
  • Bioengineering and Biotechnology
  • Original Research


The long non-coding RNA (lncRNA)–protein interaction plays an important role in the post-transcriptional gene regulation, such as RNA splicing, translation, signaling, and the development of complex diseases. The related research on the prediction of lncRNA–protein interaction relationship is beneficial in the excavation and the discovery of the mechanism of lncRNA function and action occurrence, which are important. Traditional experimental methods for detecting lncRNA–protein interactions are expensive and time-consuming. Therefore, computational methods provide many effective strategies to deal with this problem. In recent years, most computational methods only use the information of the lncRNA–lncRNA or the protein–protein similarity and cannot fully capture all features to identify their interactions. In this paper, we propose a novel computational model for the lncRNA–protein prediction on the basis of machine learning methods. First, a feature method is proposed for representing the information of the network topological properties of lncRNA and protein interactions. The basic composition feature information and evolutionary information based on protein, the lncRNA sequence feature information, and the lncRNA expression profile information are extracted. Finally, the above feature information is fused, and the optimized feature vector is used with the recursive feature elimination algorithm. The optimized feature vectors are input to the support vector machine (SVM) model. Experimental results show that the proposed method has good effectiveness and accuracy in the lncRNA–protein interaction prediction.

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