Identifying novel NF-kB-regulated immune genes in the human genome using a Discrete Kernel Structured Support Vector Machine
DC Field | Value | Language |
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dc.contributor.author | Sohn, I. | - |
dc.contributor.author | Kim, S. | - |
dc.contributor.author | Lee, J.W. | - |
dc.contributor.author | Koo, J.-Y. | - |
dc.contributor.author | Ko, J. | - |
dc.date.accessioned | 2021-09-03T14:03:47Z | - |
dc.date.available | 2021-09-03T14:03:47Z | - |
dc.date.created | 2021-06-17 | - |
dc.date.issued | 2017 | - |
dc.identifier.issn | 1574-1699 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/86103 | - |
dc.description.abstract | NF-kB is a transcription factor (TF) which is an important mediator of immune responses. To understand human immune systems and related diseases, identifying novel NF-kB-responsive genes in the human genome is important. For this purpose, we developed a Discrete Kernel Structured Support Vector Machine (DSSVM) and applied this method to the promoters of 58 known NF-kB-target genes to find characteristic patterns of transcription factor binding sites (TFBSs) in their promoters. We searched these TFBS patterns against the promoters of 6440 genes previously identified in the human genome and identified 80 genes with the similar TFBS patterns in their promoters. Among them, 21 genes are well known NF-kB-target genes and 49 are putative NF-kB target genes associated with the immune system or inflammatory response. We confirmed that the selected genes function as NF-kB-target genes from a biological point of view. Our method of assigning functions to genes, based on distinctive patterns of TFBSs in their promoters, could be applicable to identification of, not only genes associated with a specific function, but genes with unique expression patterns. © 2017 IOS Press and the authors. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IOS Press | - |
dc.title | Identifying novel NF-kB-regulated immune genes in the human genome using a Discrete Kernel Structured Support Vector Machine | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, J.W. | - |
dc.identifier.doi | 10.3233/MAS-170395 | - |
dc.identifier.scopusid | 2-s2.0-85029461535 | - |
dc.identifier.bibliographicCitation | Model Assisted Statistics and Applications, v.12, no.3, pp.209 - 216 | - |
dc.relation.isPartOf | Model Assisted Statistics and Applications | - |
dc.citation.title | Model Assisted Statistics and Applications | - |
dc.citation.volume | 12 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 209 | - |
dc.citation.endPage | 216 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | NF-kB | - |
dc.subject.keywordAuthor | Structured Support Vector Machine | - |
dc.subject.keywordAuthor | transcription factor | - |
dc.subject.keywordAuthor | transcription factor binding site | - |
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