ArticleIUCrJ2018
Article in IUCrJ, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed.
- AI-based methods for biomolecular structure modeling for Cryo-EM.Current opinion in structural biology · 2025Review
- GTpick: A deep neural network for Cryo-EM particle detection.Computational and structural biotechnology journal · 2025Article
- Accurate size-based protein localization from cryo-ET tomograms.Journal of structural biology: X · 2024Article
- REliable PIcking by Consensus (REPIC): a consensus methodology for harnessing multiple cryo-EM particle pickers.Communications biology · 2024Article
- EMDB-the Electron Microscopy Data Bank.Nucleic acids research · 2024Review
- Integrating machine learning to advance epitope mapping.Frontiers in immunology · 2024Review
- The advent of preventive high-resolution structural histopathology by artificial-intelligence-powered cryogenic electron tomography.Frontiers in molecular biosciences · 2024Review
- Computational methods forFrontiers in cellular and infection microbiology · 2023Review
- Multiple-image super-resolution of cryo-electron micrographs based on deep internal learning.Biological imaging · 2023Article
- Emerging Themes in CryoEM─Single Particle Analysis Image Processing.Chemical reviews · 2022Review
- On bias, variance, overfitting, gold standard and consensus in single-particle analysis by cryo-electron microscopy.Acta crystallographica. Section D, Structural biology · 2022Article
- Labels in a haystack: Approaches beyond supervised learning in biomedical applications.Patterns (New York, N.Y.) · 2021Review
- Deep learning improves macromolecule identification in 3D cellular cryo-electron tomograms.Nature methods · 2021Article
- Advances in Xmipp for Cryo-Electron Microscopy: From Xmipp to Scipion.Molecules (Basel, Switzerland) · 2021Article
- Current data processing strategies for cryo-electron tomography and subtomogram averaging.The Biochemical journal · 2021Review
- Deep Learning-Based Advances in Protein Structure Prediction.International journal of molecular sciences · 2021Review
- Image Processing in Cryo-Electron Microscopy of Single Particles: The Power of Combining Methods.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Hybrid methods for combined experimental and computational determination of protein structure.The Journal of chemical physics · 2020Review
- Article
- Continuous flexibility analysis of SARS-CoV-2 Spike prefusion structures.bioRxiv : the preprint server for biology · 2020Article
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
Single-particle cryo-electron microscopy (cryo-EM) has recently become a mainstream technique for the structural determination of macromolecules. Typical cryo-EM workflows collect hundreds of thousands of single-particle projections from thousands of micrographs using particle-picking algorithms. However, the number of false positives selected by these algorithms is large, so that a number of different 'cleaning steps' are necessary to decrease the false-positive ratio. Most commonly employed techniques for the pruning of false-positive particles are time-consuming and require user intervention. In order to overcome these limitations, a deep learning-based algorithm named
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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.