Unsupervised learning approaches to characterizing heterogeneous samples using X-ray single-particle imaging

One of the outstanding analytical problems in X-ray single-particle imaging (SPI) is the classification of structural heterogeneity, which is especially difficult given the low signal-to-noise ratios of individual patterns and the fact that even identical objects can yield patterns that vary greatly...

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Detalles Bibliográficos
Autores principales: Zhuang, Yulong, Awel, Salah, Barty, Anton, Bean, Richard, Bielecki, Johan, Bergemann, Martin, Daurer, Benedikt J., Ekeberg, Tomas, Estillore, Armando D., Fangohr, Hans, Giewekemeyer, Klaus, Hunter, Mark S., Karnevskiy, Mikhail, Kirian, Richard A., Kirkwood, Henry, Kim, Yoonhee, Koliyadu, Jayanath, Lange, Holger, Letrun, Romain, Lübke, Jannik, Mall, Abhishek, Michelat, Thomas, Morgan, Andrew J., Roth, Nils, Samanta, Amit K., Sato, Tokushi, Shen, Zhou, Sikorski, Marcin, Schulz, Florian, Spence, John C. H., Vagovic, Patrik, Wollweber, Tamme, Worbs, Lena, Xavier, P. Lourdu, Yefanov, Oleksandr, Maia, Filipe R. N. C., Horke, Daniel A., Küpper, Jochen, Loh, N. Duane, Mancuso, Adrian P., Chapman, Henry N., Ayyer, Kartik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Union of Crystallography 2022
Materias:
Research Papers
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8895023/
https://www.ncbi.nlm.nih.gov/pubmed/35371510
http://dx.doi.org/10.1107/S2052252521012707

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8895023/
https://www.ncbi.nlm.nih.gov/pubmed/35371510
http://dx.doi.org/10.1107/S2052252521012707

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