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Generative adversarial networks as a tool to recover structural information from cryo-electron microscopy data

Min Su, Hantian Zhang, Kevin Schawinski, Ce Zhang, View ORCID ProfileMichael A. Cianfrocco
doi: https://doi.org/10.1101/256792
Min Su
1Life Sciences Institute, University of Michigan, Ann Arbor, MI, USA;
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Hantian Zhang
2Systems Group, Department of Computer Science, ETH Zurich, Zurich, Switzerland;
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Kevin Schawinski
3Institute for Astronomy, Department of Physics, ETH Zurich, Zurich, Switzerland;
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Ce Zhang
2Systems Group, Department of Computer Science, ETH Zurich, Zurich, Switzerland;
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  • For correspondence: ce.zhang@inf.ethz.ch mcianfro@umich.edu
Michael A. Cianfrocco
1Life Sciences Institute, University of Michigan, Ann Arbor, MI, USA;
4Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA.
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  • ORCID record for Michael A. Cianfrocco
  • For correspondence: ce.zhang@inf.ethz.ch mcianfro@umich.edu
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ABSTRACT

Cryo-electron microscopy (cryo-EM) is a powerful structural biology technique capable of determining atomic-resolution structures of biological macromolecules. Despite this ability, the low signal-to-noise ratio of cryo-EM data continues to remain a hurdle for assessing raw cryo-EM micrographs and subsequent image analysis. To help address this problem, we have performed proof-of-principle studies with generative adversarial networks, a form of artificial intelligence, to denoise individual particles. This approach effectively recovers global structural information for both synthetic and real cryo-EM data, facilitating per-particle assessment from noisy raw images. Our results suggest that generative adversarial networks may be able to provide an approach to denoise raw cryo-EM images to facilitate particle selection and raw particle interpretation for single particle and tomography cryo-EM data.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted February 12, 2018.
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Generative adversarial networks as a tool to recover structural information from cryo-electron microscopy data
Min Su, Hantian Zhang, Kevin Schawinski, Ce Zhang, Michael A. Cianfrocco
bioRxiv 256792; doi: https://doi.org/10.1101/256792
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Generative adversarial networks as a tool to recover structural information from cryo-electron microscopy data
Min Su, Hantian Zhang, Kevin Schawinski, Ce Zhang, Michael A. Cianfrocco
bioRxiv 256792; doi: https://doi.org/10.1101/256792

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