2018
DOI: 10.1111/jcmm.13799
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Abstract: AbstractmiRNAs are a class of small noncoding RNAs that are associated with a variety of complex biological processes. Increasing studies have shown that miRNAs have close relationships with many human diseases. The prediction of the associations between miRNAs and diseases has thus become a hot topic. Although traditional experimental methods are reliable, they could only identify a limited number of associations as they are time‐consuming and expensive. Consequently, great efforts have been made to effective… Show more

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Cited by 10 publications
(5 citation statements)
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“…We further compared our method with four state‐of‐the‐art methods, that is, SNMDA, HGIMDA, EGBMMDA and MKRMDA. It is worth mentioning that SNMDA was also proposed by our team and achieved superior results . Moreover, in order to clearly demonstrate the power of our method, we removed the similarity matrices constructed by matrix completion for both miRNAs and diseases and compared its prediction performance with MCLPMDA in all cross‐validation frameworks.…”
Section: Resultsmentioning
confidence: 96%
See 1 more Smart Citation
“…We further compared our method with four state‐of‐the‐art methods, that is, SNMDA, HGIMDA, EGBMMDA and MKRMDA. It is worth mentioning that SNMDA was also proposed by our team and achieved superior results . Moreover, in order to clearly demonstrate the power of our method, we removed the similarity matrices constructed by matrix completion for both miRNAs and diseases and compared its prediction performance with MCLPMDA in all cross‐validation frameworks.…”
Section: Resultsmentioning
confidence: 96%
“…It is worth mentioning that SNMDA was also proposed by our team and achieved superior results. 48 Moreover, in order to clearly demonstrate the power of our method, we removed the similarity matrices constructed by matrix completion for both miRNAs and diseases and compared its prediction perfor- Next, we adopted another evaluation metric called leave one disease out cross validation (LODOCV) to test the ability of our method to predict for diseases without any known associated miRNAs.…”
Section: Performance Evaluationmentioning
confidence: 99%
“…Predicting microRNA-disease associations based on sparse neighborhoods (SNMDA) : Qu et al ( 2018a ) presented a method named SNMDA that takes advantage of the sparsity of the miRNA-disease association network and integrates the sparse information into the current similarity matrices for both miRNAs and diseases.…”
Section: Methodsmentioning
confidence: 99%
“…The network or graph algorithms focused on constructing miRNAs and/or disease similarity networks and efficient transferring miRNA-disease association labels between similar miRNAs and/or similar diseases in the network. Therefore, label propagation algorithm, which has the advantages of simplicity and efficiency on the miRNA/disease similarity networks, often constitutes the core component of the algorithm framework for this type of methods, e.g., MCLPMDA [15], LPLNS [16], SNMDA [17], and HLPMDA [18]. Nevertheless, more sophisticated algorithm designs are often crucial for successful prediction of miRNA-disease associations.…”
Section: Introductionmentioning
confidence: 99%