DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yongsoo Kim | - |
dc.contributor.author | Kim, TK | - |
dc.contributor.author | Kim, Y | - |
dc.contributor.author | Yoo, J | - |
dc.contributor.author | Sungyong You | - |
dc.contributor.author | Lee, I | - |
dc.contributor.author | Carlson, G | - |
dc.contributor.author | Hood, L | - |
dc.contributor.author | Seungjin Choi | - |
dc.contributor.author | Hwang, D | - |
dc.date.accessioned | 2016-03-31T09:32:02Z | - |
dc.date.available | 2016-03-31T09:32:02Z | - |
dc.date.created | 2011-04-04 | - |
dc.date.issued | 2011-02-01 | - |
dc.identifier.issn | 1367-4803 | - |
dc.identifier.other | 2011-OAK-0000023852 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/17307 | - |
dc.description.abstract | Motivation: Systems biology attempts to describe complex systems behaviors in terms of dynamic operations of biological networks. However, there is lack of tools that can effectively decode complex network dynamics over multiple conditions. Results: We present principal network analysis (PNA) that can automatically capture major dynamic activation patterns over multiple conditions and then generate protein and metabolic subnetworks for the captured patterns. We first demonstrated the utility of this method by applying it to a synthetic dataset. The results showed that PNA correctly captured the subnetworks representing dynamics in the data. We further applied PNA to two time-course gene expression profiles collected from (i) MCF7 cells after treatments of HRG at multiple doses and (ii) brain samples of four strains of mice infected with two prion strains. The resulting subnetworks and their interactions revealed network dynamics associated with HRG dose-dependent regulation of cell proliferation and differentiation and early PrPSc accumulation during prion infection. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | Oxford : Oxford University Press, c199 | - |
dc.relation.isPartOf | BIOINFORMATICS | - |
dc.title | Principal network analysis: identification of subnetworks representing major dynamics using gene expression data. | - |
dc.type | Article | - |
dc.contributor.college | 정보전자융합공학부 | - |
dc.identifier.doi | 10.1093/BIOINFORMATICS/BTQ670 | - |
dc.author.google | Kim, Y | - |
dc.author.google | Kim, TK | - |
dc.author.google | Yoo, J | - |
dc.author.google | You, S | - |
dc.author.google | Lee, I | - |
dc.author.google | Carlson, G | - |
dc.author.google | Hood, L | - |
dc.author.google | Choi, S | - |
dc.author.google | Hwang, D | - |
dc.relation.volume | 27 | - |
dc.relation.issue | 3 | - |
dc.relation.startpage | 391 | - |
dc.relation.lastpage | 398 | - |
dc.contributor.id | 10077620 | - |
dc.relation.journal | BIOINFORMATICS | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCI | - |
dc.collections.name | Journal Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | BIOINFORMATICS, v.27, no.3, pp.391 - 398 | - |
dc.identifier.wosid | 000286991300013 | - |
dc.date.tcdate | 2019-01-01 | - |
dc.citation.endPage | 398 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 391 | - |
dc.citation.title | BIOINFORMATICS | - |
dc.citation.volume | 27 | - |
dc.contributor.affiliatedAuthor | Seungjin Choi | - |
dc.contributor.affiliatedAuthor | Hwang, D | - |
dc.identifier.scopusid | 2-s2.0-79551600768 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 27 | - |
dc.description.scptc | 30 | * |
dc.date.scptcdate | 2018-05-121 | * |
dc.type.docType | Article | - |
dc.relation.journalWebOfScienceCategory | Biochemical Research Methods | - |
dc.relation.journalWebOfScienceCategory | Biotechnology & Applied Microbiology | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Mathematical & Computational Biology | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Biochemistry & Molecular Biology | - |
dc.relation.journalResearchArea | Biotechnology & Applied Microbiology | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Mathematical & Computational Biology | - |
dc.relation.journalResearchArea | Mathematics | - |
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