Predicting novel CircRNA-disease associations based on random walk and logistic regression model. (August 2020)

• Record Type: Journal Article
• Authors: Ding, Yulian; Chen, Bolin; Lei, Xiujuan; Liao, Bo; Wu, Fang-Xiang
• Is Part Of: Computational biology and chemistry. Volume 87(2020)
• Publication Date: 2020-08
• Subjects: CircRNA -- Disease -- CircRNA-disease association -- Logistic regression -- Random walk; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2020.107287
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: Random walk with restart can get the global relationship information from circRNA-circRNA similarity network. Gaussian Interaction Profile Kernel similarity is not suitable for circRNA-circRNA similarity calculation. Integrating global information in Logistic Regression can improve the prediction performance. Abstract: Circular RNAs (circRNAs), a large group of small endogenous noncoding RNA molecules, have been proved to modulate protein-coding genes in the human genome. In recent years, many experimental studies have demonstrated that circRNAs are dysregulated in a number of diseases, and they can serve as biomarkers for disease diagnosis and prognosis. However, it is expensive and time-consuming to identify circRNA-disease associations by biological experiments and few computational models have been proposed for novel circRNA-disease association prediction. In this study, we develop a computational model based on the random walk and the logistic regression (RWLR) to predict circRNA-disease associations. Firstly, a circRNA-circRNA similarity network is constructed by calculating their functional similarity of circRNA based on circRNA-related gene ontology. Then, a random walk with restart is implemented on the circRNA similarity network, and the features of each pair of circRNA-disease are extracted based on the results of the random walk and the circRNA-disease association matrix. Finally, a logistic regression model is used to predict novel circRNA-disease associations. Leave one out validation (LOOCV), five-fold cross validation (5CV) and ten-fold cross validation (10CV) are adopted to evaluate the prediction performance of RWLR, by comparing with the latest two methods PWCDA and DWNN-RLS. The experiment results show that our RWLR has higher AUC values of LOOCV, 5CV and 10CV than the other two latest methods, which demonstrates that RWLR has a better performance than other computational methods. What's more, case studies also illustrate the reliability and effectiveness of RWLR for circRNA-disease association prediction.