ArticleBioinformatics (Oxford, England)2017
Deep learning-based subdivision approach for large scale macromolecules structure recovery from electron cryo tomograms.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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26 citing papers in PubMed.
- AITom: AI-guided cryo-electron tomography image analyses toolkit.Journal of structural biology · 2025Article
- A noise-robust classification method for cryo-ET subtomograms with out-of-distribution detection.Bioinformatics (Oxford, England) · 2025Article
- Few-shot classification of Cryo-ET subvolumes with deep Brownian distance covariance.Briefings in bioinformatics · 2024Article
- An Approach to Developing Benchmark Datasets for Protein Secondary Structure Segmentation from Cryo-EM Density Maps.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2023Article
- Computational methods forFrontiers in cellular and infection microbiology · 2023Review
- Isotropic reconstruction for electron tomography with deep learning.Nature communications · 2022Article
- Volumetric macromolecule identification in cryo-electron tomograms using capsule networks.BMC bioinformatics · 2022Article
- Cryo-shift: reducing domain shift in cryo-electron subtomograms with unsupervised domain adaptation and randomization.Bioinformatics (Oxford, England) · 2022Article
- SimCP-NS: similarity-based copy paste for semi-supervised 3D EM neuron segmentation.Bioinformatics (Oxford, England) · 2022Article
- Active learning to classify macromolecular structures in situ for less supervision in cryo-electron tomography.Bioinformatics (Oxford, England) · 2021Article
- Few shot domain adaptation for in situ macromolecule structural classification in cryoelectron tomograms.Bioinformatics (Oxford, England) · 2021Article
- Three-dimensional deconvolution processing for STEM cryotomography.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Few-shot learning for classification of novel macromolecular structures in cryo-electron tomograms.PLoS computational biology · 2020Article
- One-Shot Learning With Attention-Guided Segmentation in Cryo-Electron Tomography.Frontiers in molecular biosciences · 2020Article
- In situ structure determination by subtomogram averaging.Current opinion in structural biology · 2019Review
- Adversarial domain adaptation for cross data source macromolecule in situ structural classification in cellular electron cryo-tomograms.Bioinformatics (Oxford, England) · 2019Article
- De Novo Structural Pattern Mining in Cellular Electron Cryotomograms.Structure (London, England : 1993) · 2019Article
- Feature Decomposition Based Saliency Detection in Electron Cryo-Tomograms.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2018Article
- Improved deep learning-based macromolecules structure classification from electron cryo-tomograms.Machine vision and applications · 2018Article
- Multi-task Learning for Macromolecule Classification, Segmentation and Coarse Structural Recovery in Cryo-Tomography.BMVC : proceedings of the British Machine Vision Conference. British Machine Vision Conference · 2018Article
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7 authors.
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Abstract
motivationCellular Electron CryoTomography (CECT) enables 3D visualization of cellular organization at near-native state and in sub-molecular resolution, making it a powerful tool for analyzing structures of macromolecular complexes and their spatial organizations inside single cells. However, high degree of structural complexity together with practical imaging limitations makes the systematic de novo discovery of structures within cells challenging. It would likely require averaging and classifying millions of subtomograms potentially containing hundreds of highly heterogeneous structural classes. Although it is no longer difficult to acquire CECT data containing such amount of subtomograms due to advances in data acquisition automation, existing computational approaches have very limited scalability or discrimination ability, making them incapable of processing such amount of data.
resultsTo complement existing approaches, in this article we propose a new approach for subdividing subtomograms into smaller but relatively homogeneous subsets. The structures in these subsets can then be separately recovered using existing computation intensive methods. Our approach is based on supervised structural feature extraction using deep learning, in combination with unsupervised clustering and reference-free classification. Our experiments show that, compared with existing unsupervised rotation invariant feature and pose-normalization based approaches, our new approach achieves significant improvements in both discrimination ability and scalability. More importantly, our new approach is able to discover new structural classes and recover structures that do not exist in training data. AVAILABILITY AND IMPLEMENTATION: Source code freely available at http://www.cs.cmu.edu/∼mxu1/software . CONTACT: mxu1@cs.cmu.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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