Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs

@article{Bepler2019PositiveunlabeledCN,
  title={Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs},
  author={Tristan Bepler and Andrew Morin and A. Noble and J. Brasch and Lawrence Shapiro and B. Berger},
  journal={Nature Methods},
  year={2019},
  pages={1-8}
}
  • Tristan Bepler, Andrew Morin, +3 authors B. Berger
  • Published 2019
  • Biology, Computer Science, Mathematics, Medicine
  • Nature Methods
  • Cryo-electron microscopy is a popular method for the determination of protein structures; however, identifying a sufficient number of particles for analysis can take months of manual effort. Current computational approaches find many false positives and require ad hoc postprocessing, especially for unusually shaped particles. To address these shortcomings, we develop Topaz, an efficient and accurate particle-picking pipeline using neural networks trained with a general-purpose positive… CONTINUE READING
    High-throughput cryo-EM enabled by user-free preprocessing routines
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