Hierarchical structural component modeling of microRNA-mRNA integration analysis
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2018-05-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12859-018-2070-0 |
| _version_ | 1818545011704201216 |
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| author | Yongkang Kim Sungyoung Lee Sungkyoung Choi Jin-Young Jang Taesung Park |
| author_facet | Yongkang Kim Sungyoung Lee Sungkyoung Choi Jin-Young Jang Taesung Park |
| author_sort | Yongkang Kim |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-11T22:55:57Z |
| format | Article |
| id | doaj.art-0ce7fadf0ae14bb6b7aa1ab8bc6ea88d |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-11T22:55:57Z |
| publishDate | 2018-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-0ce7fadf0ae14bb6b7aa1ab8bc6ea88d2022-12-22T00:47:15ZengBMCBMC Bioinformatics1471-21052018-05-0119S4253410.1186/s12859-018-2070-0Hierarchical structural component modeling of microRNA-mRNA integration analysisYongkang Kim0Sungyoung Lee1Sungkyoung Choi2Jin-Young Jang3Taesung Park4Department of Statistics, Seoul National UniversityInterdisciplinary program in Bioinformatics, Seoul National UniversityInterdisciplinary program in Bioinformatics, Seoul National UniversityDepartment of Surgery and Cancer Research Institute, Seoul National University College of MedicineDepartment of Statistics, Seoul National UniversityAbstract 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.http://link.springer.com/article/10.1186/s12859-018-2070-0miRNAmRNAIntegration analysisGeneralized Structured Component Analysis (GSCA)Hierarchical structured component analysis of miRNA-mRNA integration (HisCoM-mimi) |
| spellingShingle | Yongkang Kim Sungyoung Lee Sungkyoung Choi Jin-Young Jang Taesung Park Hierarchical structural component modeling of microRNA-mRNA integration analysis BMC Bioinformatics miRNA mRNA Integration analysis Generalized Structured Component Analysis (GSCA) Hierarchical structured component analysis of miRNA-mRNA integration (HisCoM-mimi) |
| title | Hierarchical structural component modeling of microRNA-mRNA integration analysis |
| title_full | Hierarchical structural component modeling of microRNA-mRNA integration analysis |
| title_fullStr | Hierarchical structural component modeling of microRNA-mRNA integration analysis |
| title_full_unstemmed | Hierarchical structural component modeling of microRNA-mRNA integration analysis |
| title_short | Hierarchical structural component modeling of microRNA-mRNA integration analysis |
| title_sort | hierarchical structural component modeling of microrna mrna integration analysis |
| topic | miRNA mRNA Integration analysis Generalized Structured Component Analysis (GSCA) Hierarchical structured component analysis of miRNA-mRNA integration (HisCoM-mimi) |
| url | http://link.springer.com/article/10.1186/s12859-018-2070-0 |
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