Application of maximin correlation analysis to classifying protein environments for function prediction
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Taehoon | - |
dc.contributor.author | Min, Hyeyoung | - |
dc.contributor.author | Kim, Seung Jean | - |
dc.contributor.author | Yoon, Sungroh | - |
dc.date.accessioned | 2021-09-08T00:13:27Z | - |
dc.date.available | 2021-09-08T00:13:27Z | - |
dc.date.created | 2021-06-14 | - |
dc.date.issued | 2010-09-17 | - |
dc.identifier.issn | 0006-291X | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/115673 | - |
dc.description.abstract | More and more protein structures are being discovered, but most of these still have little functional information. Based on the assumption that structural resemblance would lead to functional similarity, researchers computationally compare a new structure with functionally annotated structures, for high-throughput function prediction. The effectiveness of this approach depends critically upon the quality of comparison. In particular, robust classification often becomes difficult when a function class is an aggregate of multiple subclasses, as is the case with protein annotations. For such multiple-subclass classification problems, an optimal method termed the maximin correlation analysis (MCA) was proposed. However, MCA has never been applied to automated protein function prediction although MCA can minimize the misclassification risk in the correlation-based nearest neighbor classification, thus increasing classification accuracy. In this article, we apply MCA to classifying three-dimensional protein local environment data derived from a subset of the protein data bank (PDB). In our framework, the MCA-based classifier outperformed the compared alternatives by 7-19% and 6-27% in terms of average sensitivity and specificity, respectively. Given that correlation-based similarity measures have been widely used for mining protein data, we expect that MCA would be employed to enhance other types of automated function prediction methods. (C) 2010 Elsevier Inc. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ACADEMIC PRESS INC ELSEVIER SCIENCE | - |
dc.subject | DATA-BANK | - |
dc.subject | ANNOTATION | - |
dc.subject | PROSITE | - |
dc.subject | SITES | - |
dc.subject | SIMILARITY | - |
dc.subject | HOMOLOGY | - |
dc.subject | DATABASE | - |
dc.subject | GENOME | - |
dc.title | Application of maximin correlation analysis to classifying protein environments for function prediction | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Yoon, Sungroh | - |
dc.identifier.doi | 10.1016/j.bbrc.2010.08.042 | - |
dc.identifier.scopusid | 2-s2.0-77956748251 | - |
dc.identifier.wosid | 000282200600006 | - |
dc.identifier.bibliographicCitation | BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS, v.400, no.2, pp.219 - 224 | - |
dc.relation.isPartOf | BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS | - |
dc.citation.title | BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS | - |
dc.citation.volume | 400 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 219 | - |
dc.citation.endPage | 224 | - |
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 | Biophysics | - |
dc.relation.journalWebOfScienceCategory | Biochemistry & Molecular Biology | - |
dc.relation.journalWebOfScienceCategory | Biophysics | - |
dc.subject.keywordPlus | DATA-BANK | - |
dc.subject.keywordPlus | ANNOTATION | - |
dc.subject.keywordPlus | PROSITE | - |
dc.subject.keywordPlus | SITES | - |
dc.subject.keywordPlus | SIMILARITY | - |
dc.subject.keywordPlus | HOMOLOGY | - |
dc.subject.keywordPlus | DATABASE | - |
dc.subject.keywordPlus | GENOME | - |
dc.subject.keywordAuthor | Bioinformatics | - |
dc.subject.keywordAuthor | Pattern recognition | - |
dc.subject.keywordAuthor | Supervised classification | - |
dc.subject.keywordAuthor | Maximin correlation analysis | - |
dc.subject.keywordAuthor | Protein function prediction | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.