ArticleBMC bioinformatics2020
DeepCryoPicker: fully automated deep neural network for single protein particle picking in cryo-EM.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- AI-assisted endoscopic detection of Helicobacter pylori infection in atrophic gastritis: a multicenter diagnostic study.Surgical endoscopy · 2026Article
- CryoSIP: unleashing protein high-resolution Cryo-EM via semantic-instance collaborative picking.Briefings in bioinformatics · 2026Article
- Multi-region and multi-image convolutional neural network model for detecting gastric helicobacter pylori infection.Surgical endoscopy · 2026Article
- MVGFormer: Multi-view perspective with graph-guided transformer for cryo-ET segmentation.Knowledge-based systems · 2026Article
- Advances in Crystallography and Cryo-Electron Microscopy for the Study of Membrane Proteins.Advances in experimental medicine and biology · 2026Review
- Automated removal of corrupted tilts in cryo-electron tomography.Journal of structural biology: X · 2025Article
- Multimodal deep learning integration of cryo-EM and AlphaFold3 for high-accuracy protein structure determination.Communications chemistry · 2025Article
- Few-Shot Classification of Cryo-EM Micrographs Using Triplet Loss Embeddings.bioRxiv : the preprint server for biology · 2025Article
- DeepEM Playground: Bringing deep learning to electron microscopy labs.Journal of microscopy · 2025Article
- Self-supervised learning for generalizable particle picking in cryo-EM micrographs.Cell reports methods · 2025Article
- A large-scale curated and filterable dataset for cryo-EM foundation model pre-training.Scientific data · 2025Article
- RNA sample optimization for cryo-EM analysis.Nature protocols · 2025Review
- AI-based methods for biomolecular structure modeling for Cryo-EM.Current opinion in structural biology · 2025Review
- Practical Guide for Implementing Cryogenic Electron Microscopy Structure Determination in Dermatology Research.The Journal of investigative dermatology · 2025Review
- Artificial intelligence in cryo-EM protein particle picking: recent advances and remaining challenges.Briefings in bioinformatics · 2024Review
- CryoSegNet: accurate cryo-EM protein particle picking by integrating the foundational AI image segmentation model and attention-gated U-Net.Briefings in bioinformatics · 2024Article
- Accurate cryo-EM protein particle picking by integrating the foundational AI image segmentation model and specialized U-Net.bioRxiv : the preprint server for biology · 2024Article
- CryoTransformer: a transformer model for picking protein particles from cryo-EM micrographs.Bioinformatics (Oxford, England) · 2024Article
- Diffusion models in bioinformatics and computational biology.Nature reviews bioengineering · 2024Article
- Joint micrograph denoising and protein localization in cryo-electron microscopy.Biological imaging · 2024Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundCryo-electron microscopy (Cryo-EM) is widely used in the determination of the three-dimensional (3D) structures of macromolecules. Particle picking from 2D micrographs remains a challenging early step in the Cryo-EM pipeline due to the diversity of particle shapes and the extremely low signal-to-noise ratio of micrographs. Because of these issues, significant human intervention is often required to generate a high-quality set of particles for input to the downstream structure determination steps.
resultsHere we propose a fully automated approach (DeepCryoPicker) for single particle picking based on deep learning. It first uses automated unsupervised learning to generate particle training datasets. Then it trains a deep neural network to classify particles automatically. Results indicate that the DeepCryoPicker compares favorably with semi-automated methods such as DeepEM, DeepPicker, and RELION, with the significant advantage of not requiring human intervention.
conclusionsOur framework combing supervised deep learning classification with automated un-supervised clustering for generating training data provides an effective approach to pick particles in cryo-EM images automatically and accurately.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.