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VP-Detector: A 3D convolutional neural network for automated macromolecule localization and classification in cryo-electron tomograms
[article]
2021
bioRxiv
pre-print
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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
2020
PLoS Computational Biology
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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]
2020
bioRxiv
pre-print
Preserved Fulltext
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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
2022
Frontiers in Molecular Biosciences
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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]
2021
bioRxiv
pre-print
Preserved Fulltext
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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