Title
Predicting CircRNA-Disease Associations Through Linear Neighborhood Label Propagation Method.
Abstract
Identification of circRNA-disease associations provides insight into the mechanism that circRNAs cause diseases. Wet experimental identification of circRNA-disease associations is time-consuming and labor-intensive, and thus developing computational methods for the circRNA-disease association prediction is an urgent task. In this paper, we propose a linear neighborhood label propagation method to predict circRNA-disease associations, named CD-LNLP. First, CD-LNLP uses association profiles based on known associations to calculate circRNA-circRNA similarities and disease-disease similarities. Next, CD-LNLP implements the label propagation based on the circRNA-circRNA similarity-based graph and the disease-disease similarity-based graph respectively to predict circRNA-disease associations. Finally, we combine the outputs from circRNA-circRNA similarity-based graph model and disease-disease similarity-based graph model to produce the results. In the experiments, CD-LNLP achieves impressive performance with the AUPR score of 0.4487 and the AUC score of 0.9007 and outperforms outstanding baseline methods (collaborative filter method, KATZ, nonnegative matrix factorization method, and resource allocation method) and the state-of-the-art method MRLDC. The case studies show that CD-LNLP identifies novel circRNA-disease associations, which are validated by up-to-date circRNA-disease databases and literature respectively. In conclusion, CD-LNLP is a promising method for predicting circRNA-disease associations.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2920942
IEEE ACCESS
Keywords
Field
DocType
CircRNA-disease associations,association profiles,linear neighborhood similarity,label propagation
Data mining,Label propagation,Computer science,Distributed computing
Journal
Volume
ISSN
Citations 
7
2169-3536
2
PageRank 
References 
Authors
0.37
0
4
Name
Order
Citations
PageRank
Wen Zhang115112.53
Chenglin Yu220.37
Xiaochan Wang320.37
feng liu418039.13