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Inferring potential microRNA-microRNA associations based on targeting propensity and connectivity in the context of protein interaction network.

Sun J, Zhou M, Yang H, Deng J, Wang L, Wang Q - PLoS ONE (2013)

Bottom Line: To evaluate the performance of our method, we applied the miRFunSim method to compute functional similarity scores of miRNA pairs between 100 miRNAs whose target genes have been experimentally supported and found that the functional similarity scores of miRNAs in the same family or in the same cluster are significantly higher compared with other miRNAs which are consistent with prior knowledge.Further validation analysis on experimentally verified miRNA-disease associations suggested that miRFunSim can effectively recover the known miRNA pairs associated with the same disease and achieve a higher AUC of 83.1%.In comparison with similar methods, our miRFunSim method can achieve more effective and more reliable performance for measuring the associations of miRNAs.

View Article: PubMed Central - PubMed

Affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.

ABSTRACT
MicroRNAs (miRNAs) are a group of small non-coding RNAs that play important regulatory roles at the post-transcriptional level. Although several computational methods have been developed to compare miRNAs, it is still a challenging and a badly needed task with the availability of various biological data resources. In this study, we proposed a novel graph theoretic property based computational framework and method, called miRFunSim, for quantifying the associations between miRNAs based on miRNAs targeting propensity and proteins connectivity in the integrated protein-protein interaction network. To evaluate the performance of our method, we applied the miRFunSim method to compute functional similarity scores of miRNA pairs between 100 miRNAs whose target genes have been experimentally supported and found that the functional similarity scores of miRNAs in the same family or in the same cluster are significantly higher compared with other miRNAs which are consistent with prior knowledge. Further validation analysis on experimentally verified miRNA-disease associations suggested that miRFunSim can effectively recover the known miRNA pairs associated with the same disease and achieve a higher AUC of 83.1%. In comparison with similar methods, our miRFunSim method can achieve more effective and more reliable performance for measuring the associations of miRNAs. We also conducted the case study examining liver cancer based on our method, and succeeded in uncovering the candidate liver cancer related miRNAs such as miR-34 which also has been proven in the latest study.

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Related in: MedlinePlus

Performance evaluation of miRFunSim using miRNA family and miRNA cluster.(A) A comparison of functional similarity scores of intrafamily miRNA pairs, interfamily miRNA pairs and random miRNA pairs. (B) A comparison of functional similarity scores of intracluster miRNA pairs, intercluster miRNA pairs and random miRNA pairs.
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pone-0069719-g002: Performance evaluation of miRFunSim using miRNA family and miRNA cluster.(A) A comparison of functional similarity scores of intrafamily miRNA pairs, interfamily miRNA pairs and random miRNA pairs. (B) A comparison of functional similarity scores of intracluster miRNA pairs, intercluster miRNA pairs and random miRNA pairs.

Mentions: The accumulating evidence revealed that miRNAs in the same family are likely to have similar functions [34], [35], [36]. Therefore, to evaluate the reliability of functional similarity scores computed by our miRFunSim method, we first downloaded miRNA family data from miRBase Database [30] and obtained 100 miRNAs whose target genes have been experimentally supported from TarBase [31]. Then we used our miRFunSim method to compute functional similarity scores of miRNA pairs between 100 miRNAs. These miRNA pairs were grouped into two classes: intrafamily miRNA pairs and interfamily miRNA pairs. We further compared the functional similarity scores of intrafamily miRNA pairs,interfamily miRNA pairs and random miRNAs pairs. As a result, the significant differences in functional similarity scores among intrafamily miRNA pairs, interfamily miRNA pairs and random miRNA pairs are observed (Figure 2A, Kruskal-Wallis test, df = 2, p-value = 0). The functional similarity scores for intrafamily miRNA pairs are significantly higher compared with interfamily miRNA pairs (p-value = 4.30e-5, Wilcoxon rank sum test) and random miRNA pairs (p-value = 1.80e-14, Wilcoxon rank sum test). Interfamily miRNA pairs also showed higher functional similarity scores than random miRNA pairs (p-value = 1.46e-20, Wilcoxon rank sum test).


Inferring potential microRNA-microRNA associations based on targeting propensity and connectivity in the context of protein interaction network.

Sun J, Zhou M, Yang H, Deng J, Wang L, Wang Q - PLoS ONE (2013)

Performance evaluation of miRFunSim using miRNA family and miRNA cluster.(A) A comparison of functional similarity scores of intrafamily miRNA pairs, interfamily miRNA pairs and random miRNA pairs. (B) A comparison of functional similarity scores of intracluster miRNA pairs, intercluster miRNA pairs and random miRNA pairs.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3713046&req=5

pone-0069719-g002: Performance evaluation of miRFunSim using miRNA family and miRNA cluster.(A) A comparison of functional similarity scores of intrafamily miRNA pairs, interfamily miRNA pairs and random miRNA pairs. (B) A comparison of functional similarity scores of intracluster miRNA pairs, intercluster miRNA pairs and random miRNA pairs.
Mentions: The accumulating evidence revealed that miRNAs in the same family are likely to have similar functions [34], [35], [36]. Therefore, to evaluate the reliability of functional similarity scores computed by our miRFunSim method, we first downloaded miRNA family data from miRBase Database [30] and obtained 100 miRNAs whose target genes have been experimentally supported from TarBase [31]. Then we used our miRFunSim method to compute functional similarity scores of miRNA pairs between 100 miRNAs. These miRNA pairs were grouped into two classes: intrafamily miRNA pairs and interfamily miRNA pairs. We further compared the functional similarity scores of intrafamily miRNA pairs,interfamily miRNA pairs and random miRNAs pairs. As a result, the significant differences in functional similarity scores among intrafamily miRNA pairs, interfamily miRNA pairs and random miRNA pairs are observed (Figure 2A, Kruskal-Wallis test, df = 2, p-value = 0). The functional similarity scores for intrafamily miRNA pairs are significantly higher compared with interfamily miRNA pairs (p-value = 4.30e-5, Wilcoxon rank sum test) and random miRNA pairs (p-value = 1.80e-14, Wilcoxon rank sum test). Interfamily miRNA pairs also showed higher functional similarity scores than random miRNA pairs (p-value = 1.46e-20, Wilcoxon rank sum test).

Bottom Line: To evaluate the performance of our method, we applied the miRFunSim method to compute functional similarity scores of miRNA pairs between 100 miRNAs whose target genes have been experimentally supported and found that the functional similarity scores of miRNAs in the same family or in the same cluster are significantly higher compared with other miRNAs which are consistent with prior knowledge.Further validation analysis on experimentally verified miRNA-disease associations suggested that miRFunSim can effectively recover the known miRNA pairs associated with the same disease and achieve a higher AUC of 83.1%.In comparison with similar methods, our miRFunSim method can achieve more effective and more reliable performance for measuring the associations of miRNAs.

View Article: PubMed Central - PubMed

Affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.

ABSTRACT
MicroRNAs (miRNAs) are a group of small non-coding RNAs that play important regulatory roles at the post-transcriptional level. Although several computational methods have been developed to compare miRNAs, it is still a challenging and a badly needed task with the availability of various biological data resources. In this study, we proposed a novel graph theoretic property based computational framework and method, called miRFunSim, for quantifying the associations between miRNAs based on miRNAs targeting propensity and proteins connectivity in the integrated protein-protein interaction network. To evaluate the performance of our method, we applied the miRFunSim method to compute functional similarity scores of miRNA pairs between 100 miRNAs whose target genes have been experimentally supported and found that the functional similarity scores of miRNAs in the same family or in the same cluster are significantly higher compared with other miRNAs which are consistent with prior knowledge. Further validation analysis on experimentally verified miRNA-disease associations suggested that miRFunSim can effectively recover the known miRNA pairs associated with the same disease and achieve a higher AUC of 83.1%. In comparison with similar methods, our miRFunSim method can achieve more effective and more reliable performance for measuring the associations of miRNAs. We also conducted the case study examining liver cancer based on our method, and succeeded in uncovering the candidate liver cancer related miRNAs such as miR-34 which also has been proven in the latest study.

Show MeSH
Related in: MedlinePlus