ArticleNature communications2024
DeepETPicker: Fast and accurate 3D particle picking for cryo-electron tomography using weakly supervised deep learning.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Ghost Neuron preparation unlocks high-resolution structural mapping of intracellular architecture in axons.Nature communications · 2026Article
- MemBrain v2: an end-to-end tool for the analysis of membranes in cryo-electron tomography.Nature methods · 2026Article
- Graph identification of proteins in tomograms (GRIP-Tomo) 2.0: Topologically aware classification for proteins.Protein science : a publication of the Protein Society · 2026Article
- Lessons learned from a Kaggle challenge for particle picking in cryo-electron tomography.Nature methods · 2026Article
- Integrating in situ single-particle cryo-electron microscopy with cryo-electron tomography for high-resolution structural biology in native cellular contexts.Current opinion in structural biology · 2026Review
- Vesicle3D: An Integrative Platform for 3D Segmentation and Analysis of Vesicles in Cryo-electron Tomograms.Neuroscience bulletin · 2026Article
- Advances in cryo-EM that have shaped mechanistic models of membrane-attack-complex assembly and regulation.IUCrJ · 2026Review
- TomoSwin3D: a Swin3D Transformer for the Identification and Classification of Macromolecules in 3D Cryo-ET Tomograms.bioRxiv : the preprint server for biology · 2026Article
- Deep-learning methods for contrast enhancement and artifact reduction in cryo-electron tomography: a systematic analysis of the state of the art and proposed improvements.Acta crystallographica. Section D, Structural biology · 2026Article
- TANGO: Analysis and curation of particles in cryo-electron tomography.Nature communications · 2026Article
- Translating Nature's Design Rules: How Catalysis and Life Science Guide Molecular Catassembly.JACS Au · 2026Review
- Fully genetically encoded low-molecular-weight protein tags with defined shapes for direct molecular identification by cryo-electron tomography.bioRxiv : the preprint server for biology · 2026Article
- MVGFormer: Multi-view perspective with graph-guided transformer for cryo-ET segmentation.Knowledge-based systems · 2026Article
- Dimeric gold nanoparticles enable multiplexed labeling in cryoelectron tomography.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- 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
- cryoTIGER: deep-learning based tilt interpolation generator for enhanced reconstruction in cryo electron tomography.Communications biology · 2025Article
- DeepEM Playground: Bringing deep learning to electron microscopy labs.Journal of microscopy · 2025Article
- PickET: An unsupervised method for localizing macromolecules in cryo-electron tomograms.bioRxiv : the preprint server for biology · 2025Article
- A practical look at cryo-electron tomography image processing: Key considerations for new biological discoveries.Current opinion in structural biology · 2025Review
Corrections and comments
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Authors and funding
6 authors.
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Abstract
To solve three-dimensional structures of biological macromolecules in situ, large numbers of particles often need to be picked from cryo-electron tomograms. However, adoption of automated particle-picking methods remains limited because of their technical limitations. To overcome the limitations, we develop DeepETPicker, a deep learning model for fast and accurate picking of particles from cryo-electron tomograms. Training of DeepETPicker requires only weak supervision with low numbers of simplified labels, reducing the burden of manual annotation. The simplified labels combined with the customized and lightweight model architecture of DeepETPicker and accelerated pooling enable substantial performance improvement. When tested on simulated and real tomograms, DeepETPicker outperforms the competing state-of-the-art methods by achieving the highest overall accuracy and speed, which translate into higher authenticity and coordinates accuracy of picked particles and higher resolutions of final reconstruction maps. DeepETPicker is provided in open source with a user-friendly interface to support cryo-electron tomography in situ.
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