ArticleProceedings of the National Academy of Sciences of the United States of America2023
High-throughput cryo-ET structural pattern mining by unsupervised deep iterative subtomogram clustering.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Semi-supervised clustering with knowledge-guided representation learning in cryo-electron tomography.PLOS digital health · 2026Article
- From Unsupervised to Zero-Shot: 3D Domain Adaptation for Cryo-Electron Tomography Segmentation.International journal of computer vision · 2026Article
- MVGFormer: Multi-view perspective with graph-guided transformer for cryo-ET segmentation.Knowledge-based systems · 2026Article
- Strategies for studying discrete heterogeneity in situ using cryo-electron tomography.Current opinion in structural biology · 2025Review
- Localization of macromolecules in crowded cellular cryo-electron tomograms from extremely sparse labels.Briefings in bioinformatics · 2025Article
- PickET: An unsupervised method for localizing macromolecules in cryo-electron tomograms.Research square · 2025Article
- Template Learning: Deep learning with domain randomization for particle picking in cryo-electron tomography.Nature communications · 2025Article
- PickET: An unsupervised method for localizing macromolecules in cryo-electron tomograms.bioRxiv : the preprint server for biology · 2025Article
- A hybrid YOLO-UNet3D framework for automated protein particle annotation in Cryo-ET images.Scientific reports · 2025Article
- A new age in structural S-layer biology: Experimental and in silico milestones.The Journal of biological chemistry · 2025Review
- 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
- TomoCPT: a generalizable model for 3D particle detection and localization in cryo-electron tomograms.Acta crystallographica. Section D, Structural biology · 2025Article
- Frontiers in integrative structural modeling of macromolecular assemblies.QRB discovery · 2025Review
- Visualizing the virus world inside the cell by cryo-electron tomography.Journal of virology · 2024Review
- Few-shot classification of Cryo-ET subvolumes with deep Brownian distance covariance.Briefings in bioinformatics · 2024Article
- TomoPicker: Annotation-Efficient Particle Picking in cryo-electron Tomograms.bioRxiv : the preprint server for biology · 2024Article
- Bridging structural biology and clinical research through in-tissue cryo-electron tomography.The EMBO journal · 2024Article
- MiLoPYP: self-supervised molecular pattern mining and particle localization in situ.Nature methods · 2024Article
- CryoSAM: Training-free CryoET Tomogram Segmentation with Foundation Models.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Cryoelectron tomography directly visualizes heterogeneous macromolecular structures in their native and complex cellular environments. However, existing computer-assisted structure sorting approaches are low throughput or inherently limited due to their dependency on available templates and manual labels. Here, we introduce a high-throughput template-and-label-free deep learning approach, Deep Iterative Subtomogram Clustering Approach (DISCA), that automatically detects subsets of homogeneous structures by learning and modeling 3D structural features and their distributions. Evaluation on five experimental cryo-ET datasets shows that an unsupervised deep learning based method can detect diverse structures with a wide range of molecular sizes. This unsupervised detection paves the way for systematic unbiased recognition of macromolecular complexes in situ.
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