TY - JOUR
T1 - Machine Learning for Single-Molecule Localization Microscopy
T2 - From Data Analysis to Quantification
AU - Liu, Jianli
AU - Li, Yumian
AU - Chen, Tailong
AU - Zhang, Fa
AU - Xu, Fan
N1 - Publisher Copyright:
© 2024 American Chemical Society
PY - 2024/7/16
Y1 - 2024/7/16
N2 - Single-molecule localization microscopy (SMLM) is a versatile tool for realizing nanoscale imaging with visible light and providing unprecedented opportunities to observe bioprocesses. The integration of machine learning with SMLM enhances data analysis by improving efficiency and accuracy. This tutorial aims to provide a comprehensive overview of the data analysis process and theoretical aspects of SMLM, while also highlighting the typical applications of machine learning in this field. By leveraging advanced analytical techniques, SMLM is becoming a powerful quantitative analysis tool for biological research.
AB - Single-molecule localization microscopy (SMLM) is a versatile tool for realizing nanoscale imaging with visible light and providing unprecedented opportunities to observe bioprocesses. The integration of machine learning with SMLM enhances data analysis by improving efficiency and accuracy. This tutorial aims to provide a comprehensive overview of the data analysis process and theoretical aspects of SMLM, while also highlighting the typical applications of machine learning in this field. By leveraging advanced analytical techniques, SMLM is becoming a powerful quantitative analysis tool for biological research.
UR - http://www.scopus.com/inward/record.url?scp=85197245389&partnerID=8YFLogxK
U2 - 10.1021/acs.analchem.3c05857
DO - 10.1021/acs.analchem.3c05857
M3 - Article
AN - SCOPUS:85197245389
SN - 0003-2700
VL - 96
SP - 11103
EP - 11114
JO - Analytical Chemistry
JF - Analytical Chemistry
IS - 28
ER -