Recent development of computational cluster analysis methods for single-molecule localization microscopy imagesopen access
- Authors
- Hyun, Yoonsuk; Kim, Doory
- Issue Date
- Jan-2023
- Publisher
- ELSEVIER
- Keywords
- Super-resolution fluorescence microscopy; Single-molecule localization microscopy; Cluster analysis; Machine learning
- Citation
- COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, v.21, pp.879 - 888
- Indexed
- SCIE
SCOPUS
- Journal Title
- COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
- Volume
- 21
- Start Page
- 879
- End Page
- 888
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/185043
- DOI
- 10.1016/j.csbj.2023.01.006
- ISSN
- 2001-0370
- 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.
- Files in This Item
-
- Appears in
Collections - 서울 자연과학대학 > 서울 화학과 > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.