Few-shot learning for classification of novel macromolecular structures in cryo-electron tomograms

Ran Li, Liangyong Yu, Bo Zhou, Xiangrui Zeng, Zhenyu Wang, Xiaoyan Yang, Jing Zhang, Xin Gao, Rui Jiang, Min Xu

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Cryo-electron tomography (cryo-ET) provides 3D visualization of subcellular components in the near-native state and at sub-molecular resolutions in single cells, demonstrating an increasingly important role in structural biology in situ. However, systematic recognition and recovery of macromolecular structures in cryo-ET data remain challenging as a result of low signal-to-noise ratio (SNR), small sizes of macromolecules, and high complexity of the cellular environment. Subtomogram structural classification is an essential step for such task. Although acquisition of large amounts of subtomograms is no longer an obstacle due to advances in automation of data collection, obtaining the same number of structural labels is both computation and labor intensive. On the other hand, existing deep learning based supervised classification approaches are highly demanding on labeled data and have limited ability to learn about new structures rapidly from data containing very few labels of such new structures. In this work, we propose a novel approach for subtomogram classification based on few-shot learning. With our approach, classification of unseen structures in the training data can be conducted given few labeled samples in test data through instance embedding. Experiments were performed on both simulated and real datasets. Our experimental results show that we can make inference on new structures given only five labeled samples for each class with a competitive accuracy (> 0.86 on the simulated dataset with SNR = 0.1), or even one sample with an accuracy of 0.7644. The results on real datasets are also promising with accuracy > 0.9 on both conditions and even up to 1 on one of the real datasets. Our approach achieves significant improvement compared with the baseline method and has strong capabilities of generalizing to other cellular components.
Original languageEnglish (US)
Pages (from-to)e1008227
JournalPLOS Computational Biology
Issue number11
StatePublished - Nov 11 2020

Bibliographical note

KAUST Repository Item: Exported on 2020-11-13
Acknowledgements: This work was supported in part by U.S. National Institutes of Health (NIH) grants P41GM103712 and R01GM134020, U.S. National
Science Foundation (NSF) grants DBI-1949629 and IIS-2007595, and Mark Foundation 19-044-ASP. XZ was supported by a fellowship from Carnegie


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