Abstract
Circular RNAs (circRNAs) play important roles in various biological processes, as essential non -coding RNAs that have effects on transcriptional and posttranscriptional gene expression regulation. Recently, many studies have shown that circRNAs can be regarded as micro RNA (miRNA) sponges, which are known to be associated with certain diseases. Therefore efficient computation methods are needed to explore miRNA-circRNA interactions, but only very few computational methods for predicting the associations between miRNAs and circRNAs exist. In this study, we adopt an improved random walk computational method, named KRWRMC, to express complicated associations between miRNAs and circRNAs. Our major contributions can be summed up in two points. First, in the conventional Random Walk Restart Heterogeneous (RWRH) algorithm, the computational method simply converts the circRNA/miRNA similarity network into the transition probability matrix; in contrast, we take the influence of the neighbor of the node in the network into account, which can suggest or stress some potential associations. Second, our proposed KRWRMC is the first computational model to calculate large numbers of miRNA-circRNA associations, which can be regarded as biomarkers to diagnose certain diseases and can thus help us to better understand complicated diseases. The reliability of KRWRMC has been verified by Leave One Out Cross Validation (LOOCV) and 10-fold cross validation, the results of which indicate that this method achieves excellent performance in predicting potential miRNA-circRNA associations.
Highlights
A decade ago, biologists discovered that circular RNAs are present in human cells and tissue[1]
We propose a novel computational method called KRWRMC to predict associations between micro RNA (miRNA) and circRNA
According to the changing threshold value, we can draw the Receiver Operating Characteristic (ROC) curve and calculate the Area Under Curve (AUC) value, which can be utilized to measure the accuracy of each prediction result
Summary
A decade ago, biologists discovered that circular RNAs (circRNAs) are present in human cells and tissue[1]. With improvements in detection methods[6], researchers can use advanced detection technologies to identify novel circRNAs. Recently, some researchers have used expression profiles or RNA sequence data to conduct tissue specificity experiments[7,8]. Predicting the potential associations between circRNAs and the disease-related miRNAs is important for promoting future work in disease prediction. Beyond that, these aforementioned works can help people to obtain a greater overall comprehension of RNAs and related diseases. The circRNA-related miRNAs can be regarded as labels for specific circRNAs. Based on the above idea, we can obtain new similarity scores between each miRNA pair, through which some of the miRNA similarity scores may increase and some may decrease. The improved RWR method is adopted on the reconstructed heterogeneous network
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