ArticleJournal of applied crystallography2023
Data reduction for X-ray serial crystallography using machine learning.
Article in Journal of applied crystallography, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Jungfraujoch: 38 GB sJournal of applied crystallography · 2026Article
- Machine learning for the classification of serial electron diffraction patterns: synthetic data.Acta crystallographica. Section A, Foundations and advances · 2025Article
- 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
- Robust image descriptor for machine learning based data reduction in serial crystallography.Journal of applied crystallography · 2024Article
- Introduction to the virtual collection of papers onJournal of applied crystallography · 2024Article
- Integrating machine learning to advance epitope mapping.Frontiers in immunology · 2024Review
- Deep residual networks for crystallography trained on synthetic data.Acta crystallographica. Section D, Structural biology · 2024Article
- Explainable machine learning for diffraction patterns.Journal of applied crystallography · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
Serial crystallography experiments produce massive amounts of experimental data. Yet in spite of these large-scale data sets, only a small percentage of the data are useful for downstream analysis. Thus, it is essential to differentiate reliably between acceptable data (hits) and unacceptable data (misses). To this end, a novel pipeline is proposed to categorize the data, which extracts features from the images, summarizes these features with the 'bag of visual words' method and then classifies the images using machine learning. In addition, a novel study of various feature extractors and machine learning classifiers is presented, with the aim of finding the best feature extractor and machine learning classifier for serial crystallography data. The study reveals that the oriented FAST and rotated BRIEF (ORB) feature extractor with a multilayer perceptron classifier gives the best results. Finally, the ORB feature extractor with multilayer perceptron is evaluated on various data sets including both synthetic and experimental data, demonstrating superior performance compared with other feature extractors and classifiers.
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Registered trials
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