Publications
Detailed Information
Kernel-based hierarchical structural component models for pathway analysis
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hwangbo, Suhyun | - |
dc.contributor.author | Lee, Sungyoung | - |
dc.contributor.author | Lee, Seungyeoun | - |
dc.contributor.author | Hwang, Heungsun | - |
dc.contributor.author | Kim, Inyoung | - |
dc.contributor.author | Park, Taesung | - |
dc.date.accessioned | 2022-10-11T00:49:06Z | - |
dc.date.available | 2022-10-11T00:49:06Z | - |
dc.date.created | 2022-06-16 | - |
dc.date.issued | 2022-06 | - |
dc.identifier.citation | Bioinformatics, Vol.38 No.11, pp.3078-3086 | - |
dc.identifier.issn | 1367-4803 | - |
dc.identifier.uri | https://hdl.handle.net/10371/185690 | - |
dc.description.abstract | Motivation: Pathway analyses have led to more insight into the underlying biological functions related to the phenotype of interest in various types of omics data. Pathway-based statistical approaches have been actively developed, but most of them do not consider correlations among pathways. Because it is well known that there are quite a few biomarkers that overlap between pathways, these approaches may provide misleading results. In addition, most pathway-based approaches tend to assume that biomarkers within a pathway have linear associations with the phenotype of interest, even though the relationships are more complex. Results: To model complex effects including non-linear effects, we propose a new approach, Hierarchical structural CoMponent analysis using Kernel (HisCoM-Kernel). The proposed method models non-linear associations between biomarkers and phenotype by extending the kernel machine regression and analyzes entire pathways simultaneously by using the biomarker-pathway hierarchical structure. HisCoM-Kernel is a flexible model that can be applied to various omics data. It was successfully applied to three omics datasets generated by different technologies. Our simulation studies showed that HisCoM-Kernel provided higher statistical power than other existing pathway-based methods in all datasets. The application of HisCoM-Kernel to three types of omics dataset showed its superior performance compared to existing methods in identifying more biologically meaningful pathways, including those reported in previous studies. | - |
dc.language | 영어 | - |
dc.publisher | Oxford University Press | - |
dc.title | Kernel-based hierarchical structural component models for pathway analysis | - |
dc.type | Article | - |
dc.identifier.doi | 10.1093/bioinformatics/btac276 | - |
dc.citation.journaltitle | Bioinformatics | - |
dc.identifier.wosid | 000791501200001 | - |
dc.identifier.scopusid | 2-s2.0-85132218217 | - |
dc.citation.endpage | 3086 | - |
dc.citation.number | 11 | - |
dc.citation.startpage | 3078 | - |
dc.citation.volume | 38 | - |
dc.description.isOpenAccess | N | - |
dc.contributor.affiliatedAuthor | Park, Taesung | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
- Appears in Collections:
- Files in This Item:
- There are no files associated with this item.
Item View & Download Count
Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.