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.
Highlights
Predicting beneficial and valuable miRNA–disease associations (MDAs) by doing biological laboratory experiments is costly and time-consuming
Chen and H uang[15] proposed a LRSSLMDA model to infer potential miRNA–disease associations by using sparse subspace learning with Laplacian regularization on known miRNA–disease association network and the informative feature profiles attained from integrated miRNA or disease similarity networks
To measure Area under roc curve (AUC) values, we computed the false positive rate (FPR) and true positive rate (TPR) values where FPR is used to indicate the proportion of the real negative samples in predicted positive samples to all negative samples
Summary
Predicting beneficial and valuable miRNA–disease associations (MDAs) by doing biological laboratory experiments is costly and time-consuming. A weighted K-nearest known neighbors (WKNKN) algorithm was usually used as a pre-processing step to eliminate unknown values in miRNA–disease association set as in the studies of Ezzat et al.[25], Gao et al.[26], Wu et al.[27], and Li et al.[28] It relied on the fact the number of known miRNA‐disease associations are very limited in comparison with the number of non-interacting miRNA–disease pairs which are unknown cases that could potentially be accurate associations in the training datasets. In these studies, a new miRNA or disease’s association profile was predicted using its similarities to other miRNAs or diseases, respectively, to reduce unfavorable impact of a large number of missing a ssociations[25,26]
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