ArticleJournal of synchrotron radiation2018
A convolutional neural network-based screening tool for X-ray serial crystallography.
Article in Journal of synchrotron radiation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
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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.
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Who cites it
24 citing papers in PubMed, 63 citations in OpenAlex.
- A short history of Auto-Rickshaw.Acta crystallographica. Section D, Structural biology · 2026Review
- General-Purpose Models for the Chemical Sciences: LLMs and Beyond.Chemical reviews · 2026Review
- Application of machine learning for nanodiamonds shape and surface classification based on X-ray pattern analysis.Scientific reports · 2025Article
- Machine learning for the classification of serial electron diffraction patterns: synthetic data.Acta crystallographica. Section A, Foundations and advances · 2025Article
- Deep-learning map segmentation for protein X-ray crystallographic structure determination.Acta crystallographica. Section D, Structural biology · 2024Article
- Bragg Spot Finder (BSF): a new machine-learning-aided approach to deal with spot finding for rapidly filtering diffraction pattern images.Journal of applied crystallography · 2024Article
- Neural networks for rapid phase quantification of cultural heritage X-ray powder diffraction data.Journal of applied crystallography · 2024Article
- Robust image descriptor for machine learning based data reduction in serial crystallography.Journal of applied crystallography · 2024Article
- TheJournal of applied crystallography · 2024Article
- Deep learning applications in protein crystallography.Acta crystallographica. Section A, Foundations and advances · 2024Review
- Deep residual networks for crystallography trained on synthetic data.Acta crystallographica. Section D, Structural biology · 2024Article
- Article
- Machine learning for analysis of experimental scattering and spectroscopy data in materials chemistry.Chemical science · 2023Review
- Explainable machine learning for diffraction patterns.Journal of applied crystallography · 2023Article
- Data reduction for X-ray serial crystallography using machine learning.Journal of applied crystallography · 2023Article
- Jungfraujoch: hardware-accelerated data-acquisition system for kilohertz pixel-array X-ray detectors.Journal of synchrotron radiation · 2023Article
- Automated matching of two-time X-ray photon correlation maps from phase-separating proteins with Cahn-Hilliard-type simulations using auto-encoder networks.Journal of applied crystallography · 2022Article
- Best practices for time-resolved serial synchrotron crystallography.Acta crystallographica. Section D, Structural biology · 2022Review
- Symmetry prediction and knowledge discovery from X-ray diffraction patterns using an interpretable machine learning approach.Scientific reports · 2020Article
- Beyond integration: modeling every pixel to obtain better structure factors from stills.IUCrJ · 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
6 authors at 3 institutions in 1 country.
Funding
Abstract
A new tool is introduced for screening macromolecular X-ray crystallography diffraction images produced at an X-ray free-electron laser light source. Based on a data-driven deep learning approach, the proposed tool executes a convolutional neural network to detect Bragg spots. Automatic image processing algorithms described can enable the classification of large data sets, acquired under realistic conditions consisting of noisy data with experimental artifacts. Outcomes are compared for different data regimes, including samples from multiple instruments and differing amounts of training data for neural network optimization.
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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.