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. 2021 Oct 26;11(1):21071.
doi: 10.1038/s41598-021-00677-w.

Predicting miRNA-disease associations using improved random walk with restart and integrating multiple similarities

Affiliations

Predicting miRNA-disease associations using improved random walk with restart and integrating multiple similarities

Van Tinh Nguyen et al. Sci Rep. .

Abstract

Predicting beneficial and valuable miRNA-disease associations (MDAs) by doing biological laboratory experiments is costly and time-consuming. Proposing a forceful and meaningful computational method for predicting MDAs is essential and captivated many computer scientists in recent years. In this paper, we proposed a new computational method to predict miRNA-disease associations using improved random walk with restart and integrating multiple similarities (RWRMMDA). We used a WKNKN algorithm as a pre-processing step to solve the problem of sparsity and incompletion of data to reduce the negative impact of a large number of missing associations. Two heterogeneous networks in disease and miRNA spaces were built by integrating multiple similarity networks, respectively, and different walk probabilities could be designated to each linked neighbor node of the disease or miRNA node in line with its degree in respective networks. Finally, an improve extended random walk with restart algorithm based on miRNA similarity-based and disease similarity-based heterogeneous networks was used to calculate miRNA-disease association prediction probabilities. The experiments showed that our proposed method achieved a momentous performance with Global LOOCV AUC (Area Under Roc Curve) and AUPR (Area Under Precision-Recall Curve) values of 0.9882 and 0.9066, respectively. And the best AUC and AUPR values under fivefold cross-validation of 0.9855 and 0.8642 which are proven by statistical tests, respectively. In comparison with other previous related methods, it outperformed than NTSHMDA, PMFMDA, IMCMDA and MCLPMDA methods in both AUC and AUPR values. In case studies of Breast Neoplasms, Carcinoma Hepatocellular and Stomach Neoplasms diseases, it inferred 1, 12 and 7 new associations out of top 40 predicted associated miRNAs for each disease, respectively. All of these new inferred associations have been confirmed in different databases or literatures.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
The workflow of the proposed method (RWRMMDA).
Figure 2
Figure 2
Illustration of calculating miRNA functional similarity.
Figure 3
Figure 3
The WKNKN algorithm.
Figure 4
Figure 4
Illustrations of the process of weight assignment in disease space and miRNA space.
Figure 5
Figure 5
ROC curves and AUC values (a) and PR curves and AUPR values (b) in 5 running times of fivefold cross-validation experiments.
Figure 6
Figure 6
ROC curve and AUC value (a) and PR curve and AUPR value (b) under global LOOCV experiment.
Figure 7
Figure 7
ROC curves and AUC values (a) and precision-recall curves and AUPR values (b) in comparison with other related approaches.
Figure 8
Figure 8
ROC curves and AUC values (a) and precision-recall curves and AUPR values (b) in different cases of RWRMMDAs.

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