Hostname: page-component-78c5997874-j824f Total loading time: 0 Render date: 2024-11-14T04:30:59.812Z Has data issue: false hasContentIssue false
  • English
  • Français

Automatic analysis of cryo-electron tomography using computer vision and machine learning

Published online by Cambridge University Press:  30 July 2021

Min Xu
Min Xu*
Affiliation:
Carnegie Mellon University, Pittsburgh, United States

Abstract

An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
3D Structures: From Macromolecular Assemblies to Whole Cells (3DEM FIG)
Information
Microscopy and Microanalysis , Volume 27 , Supplement S1 , August 2021 , pp. 80 - 81
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America

References

Du, X, Wang, H, Zhu, Z, Zeng, X, Chang, Y, Zhang, J, Xing, E, Xu, M. Active learning to classify macromolecular structures in situ for less supervision in cryo-electron tomography. Bioinformatics. doi:10.1093/bioinformatics/btab123Google Scholar
Li, R, Yu, L, Zhou, B, Zeng, X, Wang, Z, Yang, X, Zhang, J, Gao, X, Jang, R, Xu, M. Few-shot learning for classification of novel macromolecular structures in cryo-electron tomograms. PLOS Computational Biology. doi:10.1371/journal.pcbi.1008227Google Scholar
Zeng, X, Xu, M. Gum-Net: Unsupervised geometric matching for fast and accurate 3D subtomogram image alignment and averaging. CVPR 2020.Google Scholar
Du, X, Zeng, X, Zhou, B, Singh, A, Xu, M. Open-set Recognition of Unseen Macromolecules in Cellular Electron Cryo-Tomograms by Soft Large Margin Centralized Cosine Loss. BMVC 2019.Google Scholar
Zhao, G, Zhou, B, Wang, K, Jiang, R, Xu, M. Respond-CAM: Analyzing Deep Models for 3D Imaging Data by Visualizations. Medical Image Computing & Computer Assisted Intervention (MICCAI) 2018. arXiv:1806.00102Google Scholar
Zhao, Y, Zeng, X, Guo, Q, Xu, M. An integration of fast alignment and maximum-likelihood methods for electron subtomogram averaging and classification. ISMB 2018. Bioinformatics. 2018 Jul 1; 34(13): i227–i236. doi:10.1093/bioinformatics/bty267.Google ScholarPubMed
Zeng, X, Leung, M, Zeev-Ben-Mordehai, T, Xu, M. A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation . Journal of Structural Biology. 2018 May;202(2):150-160. doi:10.1016/j.jsb.2017.12.015CrossRefGoogle Scholar
Xu, M, Singla, J, Tocheva, E, Chang, Y, Stevens, R, Jensen, G, Alber, F. De novo structural pattern mining in cellular electron cryo-tomograms. Structure. 2019 Apr 2;27(4):679-691.e14. doi:10.1016/j.str.2019.01.005. (Appeared on Structure volume cover and highlighted in Nature Methods 16, page 285 (2019), doi:10.1038/s41592-019-0382-2)CrossRefGoogle Scholar
Xu, M, Beck, M, Alber, F. High-throughput subtomogram alignment and classification by Fourier space constrained fast volumetric matching. Journal of Structural Biology. 2012 May;178(2):152-64.CrossRefGoogle Scholar