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VP-Detector: A 3D convolutional neural network for automated macromolecule localization and classification in cryo-electron tomograms [article]

Yu Hao, Biao Zhang, Xiaohua Wan, Rui Yan, Zhiyong Liu, Jintao Li, Shihua Zhang, Xuefeng Cui, Fa Zhang
2021 bioRxiv   pre-print
Motivation: Cryo-electron tomography (Cryo-ET) with sub-tomogram averaging (STA) is indispensable when studying macromolecule structures and functions in their native environments.  ...  VP-Detector is efficient because classification performs on the pre-calculated coordinates instead of a sliding window. Results: We evaluated the VP-Detector on simulated tomograms.  ...  SHREC challenge provides a benchmark to compare and evaluate different methods for particle localization and classification in Cryo-ET data (Gubins et al., 2019; Gubins et al., 2020) .  ... 
doi:10.1101/2021.05.25.443703 fatcat:g42zjaooyvbm3mslcz53njnys4

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, Turkan Haliloglu
2020 PLoS Computational Biology  
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  ...  In this work, we propose a novel approach for subtomogram classification based on few-shot learning.  ...  For the recovery of novel structures in cryo-electron tomograms, reference- free approaches for subtomogram averaging, classification and pattern mining have been developed, including methods based on  ... 
doi:10.1371/journal.pcbi.1008227 pmid:33175839 fatcat:4zwm6iz7ijhr5ajtmj3bhf42zu

Deep Learning Improves Macromolecules Localization and Identification in 3D Cellular Cryo-Electron Tomograms [article]

Emmanuel Moebel, Antonio Martinez-Sanchez, Damien Larivière, Eric Fourmentin, Julio Ortiz, Wolfgang Baumeister, Charles Kervrann
2020 bioRxiv   pre-print
Hence, we present a computational procedure that uses artificial neural networks to accurately localize several macromolecular species in cellular cryo-electron tomograms.  ...  abstractCryo-electron tomography (cryo-ET) allows one to visualize and study the 3D spatial distribution of macromolecules, in their native states and at nanometer resolution in single cells.  ...  Killinger for fruitful discussions about cryo-ET data analysis, and deep learning applied to large 3D volumes analysis, respectively.  ... 
doi:10.1101/2020.04.15.042747 fatcat:rgtheocj3racfcz5mi2aru57jm

FSCC: Few-Shot Learning for Macromolecule Classification Based on Contrastive Learning and Distribution Calibration in Cryo-Electron Tomography

Shan Gao, Shan Gao, Xiangrui Zeng, Min Xu, Fa Zhang
2022 Frontiers in Molecular Biosciences  
Cryo-electron tomography (Cryo-ET) is an emerging technology for three-dimensional (3D) visualization of macromolecular structures in the near-native state.  ...  To recover structures of macromolecules, millions of diverse macromolecules captured in tomograms should be accurately classified into structurally homogeneous subsets.  ...  C., Förster, F., Hao, Y., et al. (2020). Shrec 2020: Classification in Cryo-Electron Tomograms. Comput.  ... 
doi:10.3389/fmolb.2022.931949 pmid:35865006 pmcid:PMC9294403 doaj:dc350e948bbe4f44aecf88b3fd98aed1 fatcat:cw4udo7nc5cdtpcmwv6vp7prhy

DISCA: high-throughput cryo-ET structural pattern mining by deep unsupervised clustering [article]

Xiangrui Zeng, Anson Kahng, Liang Xue, Julia Mahamid, Yi-Wei Chang, Min Xu
2021 bioRxiv   pre-print
Cryo-electron tomography directly visualizes heterogeneous macromolecular structures in complex cellular environments, but existing computer-assisted sorting approaches are low-throughput or inherently  ...  Diverse structures emerging from sorted subsets enable systematic unbiased recognition of macromolecular complexes in situ.  ...  Acknowledgements This work was supported in part by U.S. NIH grants R01GM134020 and P41GM103712, NSF  ... 
doi:10.1101/2021.05.16.444381 fatcat:qul2vproqreexka46mttrri4tu