OutlierD: an R package for outlier detection using quantile regression on mass spectrometry data
DC Field | Value | Language |
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dc.contributor.author | Cho, HyungJun | - |
dc.contributor.author | Kim, Yang-Jin | - |
dc.contributor.author | Jung, Hee Jung | - |
dc.contributor.author | Lee, Sang-Won | - |
dc.contributor.author | Lee, Jae Won | - |
dc.date.accessioned | 2021-09-09T10:45:46Z | - |
dc.date.available | 2021-09-09T10:45:46Z | - |
dc.date.created | 2021-06-10 | - |
dc.date.issued | 2008-03 | - |
dc.identifier.issn | 1367-4803 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/123983 | - |
dc.description.abstract | It is important to preprocess high-throughput data generated from mass spectrometry experiments in order to obtain a successful proteomics analysis. Outlier detection is an important preprocessing step. A naive outlier detection approach may miss many true outliers and instead select many non-outliers because of the heterogeneity of the variability observed commonly in high-throughput data. Because of this issue, we developed a outlier detection software program accounting for the heterogeneous variability by utilizing linear, non-linear and non-parametric quantile regression techniques. Our program was developed using the R computer language. As a consequence, it can be used interactively and conveniently in the R environment. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | OXFORD UNIV PRESS | - |
dc.title | OutlierD: an R package for outlier detection using quantile regression on mass spectrometry data | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Cho, HyungJun | - |
dc.contributor.affiliatedAuthor | Lee, Sang-Won | - |
dc.contributor.affiliatedAuthor | Lee, Jae Won | - |
dc.identifier.doi | 10.1093/bioinformatics/btn012 | - |
dc.identifier.scopusid | 2-s2.0-40749141227 | - |
dc.identifier.wosid | 000254010400027 | - |
dc.identifier.bibliographicCitation | BIOINFORMATICS, v.24, no.6, pp.882 - 884 | - |
dc.relation.isPartOf | BIOINFORMATICS | - |
dc.citation.title | BIOINFORMATICS | - |
dc.citation.volume | 24 | - |
dc.citation.number | 6 | - |
dc.citation.startPage | 882 | - |
dc.citation.endPage | 884 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
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 | - |
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