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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

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Type
Image Processing Developments in Cryo-EM
Information
Microscopy and Microanalysis , Volume 26 , Supplement S2 , August 2020 , pp. 1822
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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