Inferring potential microRNA-microRNA associations based on targeting propensity and connectivity in the context of protein interaction network.
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2013-01-01
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| Series: | PLoS ONE |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23874989/?tool=EBI |
| _version_ | 1828902524543827968 |
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| author | Jie Sun Meng Zhou Haixiu Yang Jiaen Deng Letian Wang Qianghu Wang |
| author_facet | Jie Sun Meng Zhou Haixiu Yang Jiaen Deng Letian Wang Qianghu Wang |
| author_sort | Jie Sun |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T16:18:13Z |
| format | Article |
| id | doaj.art-d5ce49bbc2e44c58aaf2a5dc8248eaf4 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-13T16:18:13Z |
| publishDate | 2013-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-d5ce49bbc2e44c58aaf2a5dc8248eaf42022-12-21T23:38:48ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-0187e6971910.1371/journal.pone.0069719Inferring potential microRNA-microRNA associations based on targeting propensity and connectivity in the context of protein interaction network.Jie SunMeng ZhouHaixiu YangJiaen DengLetian WangQianghu WangMicroRNAs (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.https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23874989/?tool=EBI |
| spellingShingle | Jie Sun Meng Zhou Haixiu Yang Jiaen Deng Letian Wang Qianghu Wang Inferring potential microRNA-microRNA associations based on targeting propensity and connectivity in the context of protein interaction network. PLoS ONE |
| title | Inferring potential microRNA-microRNA associations based on targeting propensity and connectivity in the context of protein interaction network. |
| title_full | Inferring potential microRNA-microRNA associations based on targeting propensity and connectivity in the context of protein interaction network. |
| title_fullStr | Inferring potential microRNA-microRNA associations based on targeting propensity and connectivity in the context of protein interaction network. |
| title_full_unstemmed | Inferring potential microRNA-microRNA associations based on targeting propensity and connectivity in the context of protein interaction network. |
| title_short | Inferring potential microRNA-microRNA associations based on targeting propensity and connectivity in the context of protein interaction network. |
| title_sort | inferring potential microrna microrna associations based on targeting propensity and connectivity in the context of protein interaction network |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23874989/?tool=EBI |
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