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DeepETPicker: Fast and accurate 3D particle picking for cryo-electron tomography using weakly supervised deep learning
Guole Liu1,2,3; Tongxin Niu4; Mengxuan Qiu1,2,3; Yun Zhu5; Fei Sun4,5,6,7; Ge Yang1,2,3
Source PublicationNature Communications
2024-03-07
Volume15Issue:1Pages:2090
Abstract

To solve three-dimensional structures of biological macromolecules in situ, large numbers of particles often need to be picked from cryo-electron tomo- grams. However, adoption of automated particle-picking methods remains limited because of their technical limitations. To overcome the limitations, we develop DeepETPicker, a deep learning model for fast and accurate picking of particles from cryo-electron tomograms. Training of DeepETPicker requires only weak supervision with low numbers of simplified labels, reducing the burden of manual annotation. The simplified labels combined with the cus- tomized and lightweight model architecture of DeepETPicker and accelerated pooling enable substantial performance improvement. When tested on simulated and real tomograms, DeepETPicker outperforms the competing state-of-the-art methods by achieving the highest overall accuracy and speed, which translate into higher authenticity and coordinates accuracy of picked particles and higher resolutions of final reconstruction maps. DeepETPicker is provided in open source with a user-friendly interface to support cryo-electron tomography in situ.

KeywordCryo-electron tomography particle picking deep learning weakly supervised learning
Indexed BySCI ; SCIE
Sub direction classification计算智能
planning direction of the national heavy laboratoryAI For Science
Paper associated data
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/57363
Collection多模态人工智能系统全国重点实验室
Corresponding AuthorFei Sun; Ge Yang
Affiliation1.State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences
2.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
3.School of Artificial Intelligence, University of Chinese Academy of Sciences
4.Center for Biological Imaging, Institute of Biophysics, Chinese Academy of Sciences
5.National Key Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics
6.School of Life Sciences, University of Chinese Academy of Sciences
7.Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory)
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences;  Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences;  Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Guole Liu,Tongxin Niu,Mengxuan Qiu,et al. DeepETPicker: Fast and accurate 3D particle picking for cryo-electron tomography using weakly supervised deep learning[J]. Nature Communications,2024,15(1):2090.
APA Guole Liu,Tongxin Niu,Mengxuan Qiu,Yun Zhu,Fei Sun,&Ge Yang.(2024).DeepETPicker: Fast and accurate 3D particle picking for cryo-electron tomography using weakly supervised deep learning.Nature Communications,15(1),2090.
MLA Guole Liu,et al."DeepETPicker: Fast and accurate 3D particle picking for cryo-electron tomography using weakly supervised deep learning".Nature Communications 15.1(2024):2090.
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