ArticleNature communications2022
EPicker is an exemplar-based continual learning approach for knowledge accumulation in cryoEM particle picking.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- MemBrain v2: an end-to-end tool for the analysis of membranes in cryo-electron tomography.Nature methods · 2026Article
- CryoFSL: an annotation-efficient, few-shot learning framework for robust protein particle picking in cryo-electron microscopy micrographs.Briefings in bioinformatics · 2026Article
- Automated Cryo-EM and Supervised Machine Learning Enable Reproducible Characterization of Extracellular Vesicles and Co-Isolating Particles.Journal of extracellular vesicles · 2026Article
- 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
- A large-scale curated and filterable dataset for cryo-EM foundation model pre-training.Scientific data · 2025Article
- MPicker: visualizing and picking membrane proteins for cryo-electron tomography.Nature communications · 2025Article
- Accurate size-based protein localization from cryo-ET tomograms.Journal of structural biology: X · 2024Article
- UPicker: a semi-supervised particle picking transformer method for cryo-EM micrographs.Briefings in bioinformatics · 2024Article
- REliable PIcking by Consensus (REPIC): a consensus methodology for harnessing multiple cryo-EM particle pickers.Communications biology · 2024Article
- BlobCUT: A Contrastive Learning Method to Support Small Blob Detection in Medical Imaging.Bioengineering (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning is a popular method for facilitating particle picking in single-particle cryo-electron microscopy (cryo-EM), which is essential for developing automated processing pipelines. Most existing deep learning algorithms for particle picking rely on supervised learning where the features to be identified must be provided through a training procedure. However, the generalization performance of these algorithms on unseen datasets with different features is often unpredictable. In addition, while they perform well on the latest training datasets, these algorithms often fail to maintain the knowledge of old particles. Here, we report an exemplar-based continual learning approach, which can accumulate knowledge from the new dataset into the model by training an existing model on only a few new samples without catastrophic forgetting of old knowledge, implemented in a program called EPicker. Therefore, the ability of EPicker to identify bio-macromolecules can be expanded by continuously learning new knowledge during routine particle picking applications. Powered by the improved training strategy, EPicker is designed to pick not only protein particles but also general biological objects such as vesicles and fibers.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.