Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-26T16:30:08.791Z Has data issue: false hasContentIssue false
  • English
  • Français

CryoDRGN: Deep Generative Models for Reconstructing Heterogeneous 3D Structures from Cryo-electron Micrographs

Published online by Cambridge University Press:  30 July 2020

Ellen Zhong ,
Tristan Bepler ,
Bonnie Berger  and
Joseph Davis
Ellen Zhong
Affiliation:
Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
Tristan Bepler
Affiliation:
Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
Bonnie Berger
Affiliation:
Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
Joseph Davis
Affiliation:
Massachusetts Institute of Technology, Cambridge, Massachusetts, United States

Abstract

An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Image Processing Developments in Cryo-EM
Information
Microscopy and Microanalysis , Volume 26 , Supplement S2 , August 2020 , pp. 1822
Check for updates
Copyright
Copyright © Microscopy Society of America 2020

References

Cheng, Y. (2018). Single-particle cryo-EM—How did it get here and where will it go. Science, 361(6405), 876–880. http://doi.org/10.1126/science.aat4346CrossRefGoogle Scholar
Scheres, S. H. W., Valle, M., Nuñez, R., Sorzano, C. O. S., Marabini, R., Herman, G. T., & Carazo, J.-M. (2005). Maximum-likelihood Multi-reference Refinement for Electron Microscopy Images. Journal of Molecular Biology, 348(1), 139–149. http://doi.org/10.1016/j.jmb.2005.02.031CrossRefGoogle ScholarPubMed
Zhong, E. D., Bepler, T., Davis, J. H., & Berger, B. (2019, September 11). Reconstructing continuous distributions of 3D protein structure from cryo-EM images. arXiv.org.Google Scholar