Full metadata record
DC Field | Value | Language |
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dc.citation.number | 1 | - |
dc.citation.startPage | 352 | - |
dc.citation.title | BMC GENOMICS | - |
dc.citation.volume | 20 | - |
dc.contributor.author | Yoon, Sora | - |
dc.contributor.author | Kim, Jinhwan | - |
dc.contributor.author | Kim, Seon-Kyu | - |
dc.contributor.author | Baik, Bukyung | - |
dc.contributor.author | Chi, Sang-Mun | - |
dc.contributor.author | Kim, Seon-Young | - |
dc.contributor.author | Nam, Dougu | - |
dc.date.accessioned | 2023-12-21T19:10:04Z | - |
dc.date.available | 2023-12-21T19:10:04Z | - |
dc.date.created | 2019-05-29 | - |
dc.date.issued | 2019-05 | - |
dc.description.abstract | Background: Gene-set analysis (GSA) has been commonly used to identify significantly altered pathways or functions from omics data. However, GSA often yields a long list of gene-sets, necessitating efficient post-processing for improved interpretation. Existing methods cluster the gene-sets based on the extent of their overlap to summarize the GSA results without considering interactions between gene-sets. Results: Here, we presented a novel network-weighted gene-set clustering that incorporates both the gene-set overlap and protein-protein interaction (PPI) networks. Three examples were demonstrated for microarray gene expression, GWAS summary, and RNA-sequencing data to which different GSA methods were applied. These examples as well as a global analysis show that the proposed method increases PPI densities and functional relevance of the resulting clusters. Additionally, distinct properties of gene-set distance measures were compared. The methods are implemented as an R/Shiny package GScluster that provides gene-set clustering and diverse functions for visualization of gene-sets and PPI networks. Conclusions: Network-weighted gene-set clustering provides functionally more relevant gene-set clusters and related network analysis. | - |
dc.identifier.bibliographicCitation | BMC GENOMICS, v.20, no.1, pp.352 | - |
dc.identifier.doi | 10.1186/s12864-019-5738-6 | - |
dc.identifier.issn | 1471-2164 | - |
dc.identifier.scopusid | 2-s2.0-85065531603 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/27482 | - |
dc.identifier.url | https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-019-5738-6 | - |
dc.identifier.wosid | 000467573900009 | - |
dc.language | 영어 | - |
dc.publisher | BioMed Central Ltd. | - |
dc.title | GScluster: Network-weighted gene-set clustering analysis | - |
dc.type | Article | - |
dc.description.isOpenAccess | TRUE | - |
dc.relation.journalWebOfScienceCategory | Biotechnology & Applied Microbiology; Genetics & Heredity | - |
dc.relation.journalResearchArea | Biotechnology & Applied Microbiology; Genetics & Heredity | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | Gene-set analysis | - |
dc.subject.keywordAuthor | Gene-set clustering | - |
dc.subject.keywordAuthor | Network | - |
dc.subject.keywordAuthor | Protein-protein interaction | - |
dc.subject.keywordPlus | ACUTE MYELOID-LEUKEMIA | - |
dc.subject.keywordPlus | CELL-CYCLE REGULATION | - |
dc.subject.keywordPlus | COLORECTAL-CANCER | - |
dc.subject.keywordPlus | POTASSIUM CHANNELS | - |
dc.subject.keywordPlus | INSULIN-RESISTANCE | - |
dc.subject.keywordPlus | EXPRESSION | - |
dc.subject.keywordPlus | ASSOCIATION | - |
dc.subject.keywordPlus | ONTOLOGY | - |
dc.subject.keywordPlus | PATHWAY | - |
dc.subject.keywordPlus | GROWTH | - |
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