Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 최성경 | - |
dc.date.accessioned | 2019-12-23T04:47:29Z | - |
dc.date.available | 2019-12-23T04:47:29Z | - |
dc.date.issued | 2018-05 | - |
dc.identifier.citation | BMC BIOINFORMATICS, v. 19, Article no. 75 | en_US |
dc.identifier.issn | 1471-2105 | - |
dc.identifier.uri | https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2070-0 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/121384 | - |
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. | en_US |
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). | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | BIOMED CENTRAL LTD | en_US |
dc.subject | miRNA | en_US |
dc.subject | mRNA | en_US |
dc.subject | Integration analysis | en_US |
dc.subject | Generalized Structured Component Analysis (GSCA) | en_US |
dc.subject | Hierarchical structured component analysis of miRNA-mRNA integration (HisCoM-mimi) | en_US |
dc.title | Hierarchical Structural Component Analysis of microRNA-mRNA Integration Analysis | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1186/s12859-018-2070-0 | - |
dc.relation.journal | BMC BIOINFORMATICS | - |
dc.contributor.googleauthor | Kim, Yongkang | - |
dc.contributor.googleauthor | Lee, Sungyoung | - |
dc.contributor.googleauthor | Choi, Sungkyoung | - |
dc.contributor.googleauthor | Jang, Jin-Young | - |
dc.contributor.googleauthor | Park, Taesung | - |
dc.relation.code | 2018004942 | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF SCIENCE AND CONVERGENCE TECHNOLOGY[E] | - |
dc.sector.department | DEPARTMENT OF APPLIED MATHEMATICS | - |
dc.identifier.pid | day0413 | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.