ArticleScientific data2023
A large expert-curated cryo-EM image dataset for machine learning protein particle picking.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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30 citing papers in PubMed.
- EXPECTATION-MAXIMIZATION FOR STRUCTURE DETERMINATION DIRECTLY FROM CRYO-EM MICROGRAPHS.Inverse problems and imaging (Springfield, Mo.) · 2027Article
- Semi-supervised clustering with knowledge-guided representation learning in cryo-electron tomography.PLOS digital health · 2026Article
- An unattended image-processing pipeline for on-the-fly quality assessment and 3D exploration in cryo-EM.Acta crystallographica. Section D, Structural biology · 2026Article
- CryoPromptSeg: prompt-guided segmentation with integrated denoising for cryo-EM particle picking.Bioinformatics (Oxford, England) · 2026Article
- CryoFSL: an annotation-efficient, few-shot learning framework for robust protein particle picking in cryo-electron microscopy micrographs.Briefings in bioinformatics · 2026Article
- TomoSwin3D: a Swin3D Transformer for the Identification and Classification of Macromolecules in 3D Cryo-ET Tomograms.bioRxiv : the preprint server for biology · 2026Article
- Image-to-molecule benchmarking dataset with fractal pattern and hierarchical morphology recognition.Scientific data · 2026Article
- CryoSIP: unleashing protein high-resolution Cryo-EM via semantic-instance collaborative picking.Briefings in bioinformatics · 2026Article
- CryoVirusDB: An Annotated Dataset for AI-Based Virus Particle Identification in Cryo-EM Micrographs.Viruses · 2026Article
- A comprehensive foundation model for cryo-EM image processing.Nature methods · 2026Article
- The Inaugural Flatiron Institute Cryo-EM Conformational Heterogeneity Challenge.bioRxiv : the preprint server for biology · 2025Article
- Multimodal deep learning integration of cryo-EM and AlphaFold3 for high-accuracy protein structure determination.Communications chemistry · 2025Article
- CryoFSL: An Annotation-Efficient, Few-Shot Learning Framework for Robust Protein Particle Picking in Cryo-EM Micrographs.bioRxiv : the preprint server for biology · 2025Article
- Self-supervised learning for generalizable particle picking in cryo-EM micrographs.Cell reports methods · 2025Article
- GTpick: A deep neural network for Cryo-EM particle detection.Computational and structural biotechnology journal · 2025Article
- Structural Biology for Target Identification and Validation.Methods in molecular biology (Clifton, N.J.) · 2025Article
- High-resolution protein modeling through Cryo-EM and AI: current trends and future perspectives - a review.Frontiers in molecular biosciences · 2025Review
- UPicker: a semi-supervised particle picking transformer method for cryo-EM micrographs.Briefings in bioinformatics · 2024Article
- Artificial intelligence in cryo-EM protein particle picking: recent advances and remaining challenges.Briefings in bioinformatics · 2024Review
- REliable PIcking by Consensus (REPIC): a consensus methodology for harnessing multiple cryo-EM particle pickers.Communications biology · 2024Article
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Abstract
Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of biological macromolecular complexes. Picking single-protein particles from cryo-EM micrographs is a crucial step in reconstructing protein structures. However, the widely used template-based particle picking process is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) based particle picking can potentially automate the process, its development is hindered by lack of large, high-quality labelled training data. To address this bottleneck, we present CryoPPP, a large, diverse, expert-curated cryo-EM image dataset for protein particle picking and analysis. It consists of labelled cryo-EM micrographs (images) of 34 representative protein datasets selected from the Electron Microscopy Public Image Archive (EMPIAR). The dataset is 2.6 terabytes and includes 9,893 high-resolution micrographs with labelled protein particle coordinates. The labelling process was rigorously validated through 2D particle class validation and 3D density map validation with the gold standard. The dataset is expected to greatly facilitate the development of both AI and classical methods for automated cryo-EM protein particle picking.
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