ArticleMachine vision and applications2018
Improved deep learning-based macromolecules structure classification from electron cryo-tomograms.
Article in Machine vision and applications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- AITom: AI-guided cryo-electron tomography image analyses toolkit.Journal of structural biology · 2025Article
- KNN algorithm for accurate identification of IFP lesions in the knee joint: a multimodal MRI study.Scientific reports · 2025Article
- A noise-robust classification method for cryo-ET subtomograms with out-of-distribution detection.Bioinformatics (Oxford, England) · 2025Article
- Accurate size-based protein localization from cryo-ET tomograms.Journal of structural biology: X · 2024Article
- Knowledge Transfer from Macro-world to Micro-world: Enhancing 3D Cryo-ET Classification through Fine-Tuning Video-based Deep Models.Bioinformatics (Oxford, England) · 2024Article
- DUAL: deep unsupervised simultaneous simulation and denoising for cryo-electron tomography.bioRxiv : the preprint server for biology · 2024Article
- Big data in contemporary electron microscopy: challenges and opportunities in data transfer, compute and management.Histochemistry and cell biology · 2023Review
- Computational Methods Toward Unbiased Pattern Mining and Structure Determination in Cryo-Electron Tomography Data.Journal of molecular biology · 2023Review
- High-throughput cryo-ET structural pattern mining by unsupervised deep iterative subtomogram clustering.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Computational methods forFrontiers in cellular and infection microbiology · 2023Review
- Electron microscopy holdings of the Protein Data Bank: the impact of the resolution revolution, new validation tools, and implications for the future.Biophysical reviews · 2022Review
- 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
- CryoETGAN: Cryo-Electron Tomography Image SynthesisFrontiers in physiology · 2022Article
- Self-supervised learning for macromolecular structure classification based on cryo-electron tomograms.Frontiers in physiology · 2022Article
- Deep learning improves macromolecule identification in 3D cellular cryo-electron tomograms.Nature methods · 2021Article
- UNSUPERVISED DOMAIN ALIGNMENT BASED OPEN SET STRUCTURAL RECOGNITION OF MACROMOLECULES CAPTURED BY CRYO-ELECTRON TOMOGRAPHY.Proceedings. International Conference on Image Processing · 2021Article
- Active learning to classify macromolecular structures in situ for less supervision in cryo-electron tomography.Bioinformatics (Oxford, England) · 2021Article
- Assessment of scoring functions to rank the quality of 3D subtomogram clusters from cryo-electron tomography.Journal of structural biology · 2021Article
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6 authors.
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Abstract
Cellular processes are governed by macromolecular complexes inside the cell. Study of the native structures of macromolecular complexes has been extremely difficult due to lack of data. With recent breakthroughs in Cellular Electron Cryo-Tomography (CECT) 3D imaging technology, it is now possible for researchers to gain accesses to fully study and understand the macro-molecular structures single cells. However, systematic recovery of macromolecular structures from CECT is very difficult due to high degree of structural complexity and practical imaging limitations. Specifically, we proposed a deep learning-based image classification approach for large-scale systematic macromolecular structure separation from CECT data. However, our previous work was only a very initial step toward exploration of the full potential of deep learning-based macromolecule separation. In this paper, we focus on improving classification performance by proposing three newly designed individual CNN models: an extended version of (Deep Small Receptive Field) DSRF3D, donated as DSRF3D-v2, a 3D residual block-based neural network, named as RB3D, and a convolutional 3D (C3D)-based model, CB3D. We compare them with our previously developed model (DSRF3D) on 12 datasets with different SNRs and tilt angle ranges. The experiments show that our new models achieved significantly higher classification accuracies. The accuracies are not only higher than 0.9 on normal datasets, but also demonstrate potentials to operate on datasets with high levels of noises and missing wedge effects presented.
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