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Prediction of miRNA-circRNA Associations Based on k-NN Multi-Label with Random Walk Restart on a Heterogeneous Network
Xiujuan Lei(
)
)
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Fang Z, Lei X.
Prediction of miRNA-circRNA Associations Based on k-NN Multi-Label with Random Walk Restart on a Heterogeneous Network.
Big Data Mining and Analytics,
2019, 2(4): 261-272.
https://doi.org/10.26599/BDMA.2019.9020010
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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.
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Prediction of miRNA-circRNA Associations Based on k-NN Multi-Label with Random Walk Restart on a Heterogeneous Network
Xiujuan Lei(
)
)
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.
Keywords:
heterogeneous network, miRNA-circRNA associations, multi-label, random walk restart
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Received: 25 November 2018
Revised: 16 April 2019
Accepted: 17 April 2019
Published:
05 August 2019
Issue date: December 2019
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This paper was supported by the National Natural Science Foundation of China (No. 61672334) and the Fundamental Research Funds for the Central Universities (No. GK201901010).
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