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EPicker is an exemplar-based continual learning approach for knowledge accumulation in cryoEM particle picking

Author

Listed:
  • Xinyu Zhang

    (Tsinghua University
    Tsinghua University)

  • Tianfang Zhao

    (Tsinghua University
    Tsinghua University)

  • Jiansheng Chen

    (University of Science and Technology Beijing)

  • Yuan Shen

    (Tsinghua University
    Tsinghua University)

  • Xueming Li

    (Tsinghua University
    Tsinghua-Peking Joint Center for Life Sciences
    Beijing Frontier Research Center for Biological Structure
    Advanced Innovation Center for Structural Biology)

Abstract

Deep learning is a popular method for facilitating particle picking in single-particle cryo-electron microscopy (cryo-EM), which is essential for developing automated processing pipelines. Most existing deep learning algorithms for particle picking rely on supervised learning where the features to be identified must be provided through a training procedure. However, the generalization performance of these algorithms on unseen datasets with different features is often unpredictable. In addition, while they perform well on the latest training datasets, these algorithms often fail to maintain the knowledge of old particles. Here, we report an exemplar-based continual learning approach, which can accumulate knowledge from the new dataset into the model by training an existing model on only a few new samples without catastrophic forgetting of old knowledge, implemented in a program called EPicker. Therefore, the ability of EPicker to identify bio-macromolecules can be expanded by continuously learning new knowledge during routine particle picking applications. Powered by the improved training strategy, EPicker is designed to pick not only protein particles but also general biological objects such as vesicles and fibers.

Suggested Citation

  • Xinyu Zhang & Tianfang Zhao & Jiansheng Chen & Yuan Shen & Xueming Li, 2022. "EPicker is an exemplar-based continual learning approach for knowledge accumulation in cryoEM particle picking," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-29994-y
    DOI: 10.1038/s41467-022-29994-y
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    References listed on IDEAS

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    1. Tristan Bepler & Kotaro Kelley & Alex J. Noble & Bonnie Berger, 2020. "Topaz-Denoise: general deep denoising models for cryoEM and cryoET," Nature Communications, Nature, vol. 11(1), pages 1-12, December.
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    Cited by:

    1. Xiaofeng Yan & Shudong Li & Weilin Huang & Hao Wang & Tianfang Zhao & Mingtao Huang & Niyun Zhou & Yuan Shen & Xueming Li, 2025. "MPicker: visualizing and picking membrane proteins for cryo-electron tomography," Nature Communications, Nature, vol. 16(1), pages 1-13, December.

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