Recent development of computational cluster analysis methods for single-molecule localization microscopy images
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hyun, Yoonsuk | - |
dc.contributor.author | Kim, Doory | - |
dc.date.accessioned | 2023-05-03T10:07:50Z | - |
dc.date.available | 2023-05-03T10:07:50Z | - |
dc.date.created | 2023-02-08 | - |
dc.date.issued | 2023-01 | - |
dc.identifier.issn | 2001-0370 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/185043 | - |
dc.description.abstract | With the development of super-resolution imaging techniques, it is crucial to understand protein structure at the nanoscale in terms of clustering and organization in a cell. However, cluster analysis from single-molecule localization microscopy (SMLM) images remains challenging because the classical computational cluster analysis methods developed for conventional microscopy images do not apply to pointillism SMLM data, necessitating the development of distinct methods for cluster analysis from SMLM images. In this review, we discuss the development of computational cluster analysis methods for SMLM images by categorizing them into classical and machine-learning-based methods. Finally, we address possible future directions for machine learning-based cluster analysis methods for SMLM data. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER | - |
dc.title | Recent development of computational cluster analysis methods for single-molecule localization microscopy images | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Doory | - |
dc.identifier.doi | 10.1016/j.csbj.2023.01.006 | - |
dc.identifier.scopusid | 2-s2.0-85147089707 | - |
dc.identifier.wosid | 000925075600001 | - |
dc.identifier.bibliographicCitation | COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, v.21, pp.879 - 888 | - |
dc.relation.isPartOf | COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL | - |
dc.citation.title | COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL | - |
dc.citation.volume | 21 | - |
dc.citation.startPage | 879 | - |
dc.citation.endPage | 888 | - |
dc.type.rims | ART | - |
dc.type.docType | Review | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Biochemistry & Molecular Biology | - |
dc.relation.journalResearchArea | Biotechnology & Applied Microbiology | - |
dc.relation.journalWebOfScienceCategory | Biochemistry & Molecular Biology | - |
dc.relation.journalWebOfScienceCategory | Biotechnology & Applied Microbiology | - |
dc.subject.keywordPlus | SUPERRESOLUTION IMAGING REVEALS | - |
dc.subject.keywordPlus | QUANTITATIVE-ANALYSIS | - |
dc.subject.keywordPlus | RECONSTRUCTION | - |
dc.subject.keywordPlus | LIMIT | - |
dc.subject.keywordPlus | PALM | - |
dc.subject.keywordAuthor | Super-resolution fluorescence microscopy | - |
dc.subject.keywordAuthor | Single-molecule localization microscopy | - |
dc.subject.keywordAuthor | Cluster analysis | - |
dc.subject.keywordAuthor | Machine learning | - |
dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S2001037023000077?via%3Dihub | - |
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