BioVLAB-Cancer-Pharmacogenomics: tumor heterogeneity and pharmacogenomics analysis of multi-omics data from tumor on the cloud
DC Field | Value | Language |
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dc.contributor.author | Park, Sungjoon | - |
dc.contributor.author | Lee, Dohoon | - |
dc.contributor.author | Kim, Youngkuk | - |
dc.contributor.author | Lim, Sangsoo | - |
dc.contributor.author | Chae, Heejoon | - |
dc.contributor.author | Kim, Sun | - |
dc.date.accessioned | 2022-04-19T08:44:19Z | - |
dc.date.available | 2022-04-19T08:44:19Z | - |
dc.date.issued | 2022-01 | - |
dc.identifier.issn | 1367-4803 | - |
dc.identifier.issn | 1460-2059 | - |
dc.identifier.uri | https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/145951 | - |
dc.description.abstract | Multi-omics data in molecular biology has accumulated rapidly over the years. Such data contains valuable information for research in medicine and drug discovery. Unfortunately, data-driven research in medicine and drug discovery is challenging for a majority of small research labs due to the large volume of data and the complexity of analysis pipeline. Results: We present BioVLAB-Cancer-Pharmacogenomics, a bioinformatics system that facilitates analysis of multi-omics data from breast cancer to analyze and investigate intratumor heterogeneity and pharmacogenomics on Amazon Web Services. Our system takes multi-omics data as input to perform tumor heterogeneity analysis in terms of TCGA data and deconvolve-and-match the tumor gene expression to cell line data in CCLE using DNA methylation profiles. We believe that our system can help small research labs perform analysis of tumor multi-omics without worrying about computational infrastructure and maintenance of databases and tools. | - |
dc.format.extent | 3 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | OXFORD UNIV PRESS | - |
dc.title | BioVLAB-Cancer-Pharmacogenomics: tumor heterogeneity and pharmacogenomics analysis of multi-omics data from tumor on the cloud | - |
dc.type | Article | - |
dc.publisher.location | 영국 | - |
dc.identifier.doi | 10.1093/bioinformatics/btab478 | - |
dc.identifier.scopusid | 2-s2.0-85143072833 | - |
dc.identifier.wosid | 000736120000043 | - |
dc.identifier.bibliographicCitation | BIOINFORMATICS, v.38, no.1, pp 275 - 277 | - |
dc.citation.title | BIOINFORMATICS | - |
dc.citation.volume | 38 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 275 | - |
dc.citation.endPage | 277 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
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 | - |
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.subject.keywordPlus | MICRORNA | - |
dc.subject.keywordPlus | MMIA | - |
dc.identifier.url | https://academic.oup.com/bioinformatics/article-abstract/38/1/275/6311261?redirectedFrom=fulltext | - |
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