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MACE: mutation-oriented profiling of chemical response and gene expression in cancers

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dc.contributor.authorJeong, Euna-
dc.contributor.authorHe, Ningning-
dc.contributor.authorPark, Hyerin-
dc.contributor.authorSong, Mee-
dc.contributor.authorKim, Nayoung-
dc.contributor.authorLee, Seongjoon-
dc.contributor.authorYoon, Sukjoon-
dc.date.available2021-02-22T11:35:38Z-
dc.date.issued2015-05-
dc.identifier.issn1367-4803-
dc.identifier.issn1460-2059-
dc.identifier.urihttps://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/10535-
dc.description.abstractA Summary: The mutational status of specific cancer lineages can affect the sensitivity to or resistance against cancer drugs. The MACE database provides web-based interactive tools for interpreting large chemical screening and gene expression datasets of cancer cell lines in terms of mutation and lineage categories. GI50 data of chemicals against individual NCI60 cell lines were normalized and organized to statistically identify mutation- or lineage-specific chemical responses. Similarly, DNA microarray data on NCI60 cell lines were processed to analyze mutation- or lineage-specific gene expression signatures. A combined analysis of GI50 and gene expression data to find potential associations between chemicals and genes is also a capability of this system. This database will provide extensive, systematic information to identify lineage- or mutation-specific anticancer agents and related gene targets.-
dc.format.extent7-
dc.language영어-
dc.language.isoENG-
dc.publisherOXFORD UNIV PRESS-
dc.titleMACE: mutation-oriented profiling of chemical response and gene expression in cancers-
dc.typeArticle-
dc.publisher.location영국-
dc.identifier.doi10.1093/bioinformatics/btu835-
dc.identifier.scopusid2-s2.0-84940108977-
dc.identifier.wosid000355665800032-
dc.identifier.bibliographicCitationBIOINFORMATICS, v.31, no.9, pp 1508 - 1514-
dc.citation.titleBIOINFORMATICS-
dc.citation.volume31-
dc.citation.number9-
dc.citation.startPage1508-
dc.citation.endPage1514-
dc.type.docTypeArticle-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClasssci-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaBiochemistry & Molecular Biology-
dc.relation.journalResearchAreaBiotechnology & Applied Microbiology-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaMathematical & Computational Biology-
dc.relation.journalResearchAreaMathematics-
dc.relation.journalWebOfScienceCategoryBiochemical Research Methods-
dc.relation.journalWebOfScienceCategoryBiotechnology & Applied Microbiology-
dc.relation.journalWebOfScienceCategoryComputer Science, Interdisciplinary Applications-
dc.relation.journalWebOfScienceCategoryMathematical & Computational Biology-
dc.relation.journalWebOfScienceCategoryStatistics & Probability-
dc.subject.keywordPlusDRUG-SENSITIVITY-
dc.subject.keywordPlusCOMPOUND RESPONSE-
dc.subject.keywordPlusDATABASE-
dc.subject.keywordPlusCELLS-
dc.subject.keywordPlusINACTIVATION-
dc.subject.keywordPlusPATTERNS-
dc.subject.keywordPlusUPDATE-
dc.subject.keywordPlusTOOLS-
dc.subject.keywordPlusLUNG-
dc.subject.keywordPlusSET-
dc.identifier.urlhttps://academic.oup.com/bioinformatics/article/31/9/1508/200245-
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