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Hierarchical structural component modeling of microRNA-mRNA integration analysis
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Yongkang | - |
dc.contributor.author | Lee, Sungyoung | - |
dc.contributor.author | Choi, Sungkyoung | - |
dc.contributor.author | Jang, Jin-Young | - |
dc.contributor.author | Park, Taesung | - |
dc.date.accessioned | 2018-05-31T01:18:07Z | - |
dc.date.available | 2018-05-31T10:27:16Z | - |
dc.date.issued | 2018-05-08 | - |
dc.identifier.citation | BMC Bioinformatics, 19(Suppl 4):75 | ko_KR |
dc.identifier.issn | 1471-2105 | - |
dc.identifier.uri | https://hdl.handle.net/10371/142657 | - |
dc.description.abstract | Background
Identification of multi-markers is one of the most challenging issues in personalized medicine era. Nowadays, many different types of omics data are generated from the same subject. Although many methods endeavor to identify candidate markers, for each type of omics data, few or none can facilitate such identification. Results It is well known that microRNAs affect phenotypes only indirectly, through regulating mRNA expression and/or protein translation. Toward addressing this issue, we suggest a hierarchical structured component analysis of microRNA-mRNA integration (HisCoM-mimi) model that accounts for this biological relationship, to efficiently study and identify such integrated markers. In simulation studies, HisCoM-mimi showed the better performance than the other three methods. Also, in real data analysis, HisCoM-mimi successfully identified more gives more informative miRNA-mRNA integration sets relationships for pancreatic ductal adenocarcinoma (PDAC) diagnosis, compared to the other methods. Conclusion As exemplified by an application to pancreatic cancer data, our proposed model effectively identified integrated miRNA/target mRNA pairs as markers for early diagnosis, providing a much broader biological interpretation. | ko_KR |
dc.description.sponsorship | This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI16C2037010016) and Bio-Synergy Research Project of the Ministry of Science, ICT and Future Planning through the National Research Foundation (grant number: 2013M3A9C4078158). Publication of this article was sponsored by the Bio-Synergy Research Project (grant number: 2013M3A9C4078158). | ko_KR |
dc.language.iso | en | ko_KR |
dc.publisher | BioMed Central | ko_KR |
dc.subject | miRNA | ko_KR |
dc.subject | mRNA | ko_KR |
dc.subject | Integration analysis | ko_KR |
dc.subject | Generalized Structured Component Analysis | ko_KR |
dc.subject | GSCA | ko_KR |
dc.subject | Hierarchical | ko_KR |
dc.subject | structured component analysis of miRNA-mRNA integration | ko_KR |
dc.subject | HisCoM-mimi | ko_KR |
dc.title | Hierarchical structural component modeling of microRNA-mRNA integration analysis | ko_KR |
dc.type | Article | ko_KR |
dc.contributor.AlternativeAuthor | 김용강 | - |
dc.contributor.AlternativeAuthor | 이성영 | - |
dc.contributor.AlternativeAuthor | 최성경 | - |
dc.contributor.AlternativeAuthor | 장진영 | - |
dc.contributor.AlternativeAuthor | 박태성 | - |
dc.identifier.doi | 10.1186/s12859-018-2070-0 | - |
dc.language.rfc3066 | en | - |
dc.rights.holder | The Author(s). | - |
dc.date.updated | 2018-05-13T03:35:33Z | - |
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