ArticleJournal of applied crystallography2021
Data reduction for serial crystallography using a robust peak finder.
Article in Journal of applied crystallography, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Benchmarking deep learning for automated peak detection on GIWAXS data.Journal of applied crystallography · 2025Article
- Application of signal separation to diffraction image compression and serial crystallography.Journal of applied crystallography · 2025Article
- Structural biology in the age of X-ray free-electron lasers and exascale computing.Current opinion in structural biology · 2024Review
- Robust image descriptor for machine learning based data reduction in serial crystallography.Journal of applied crystallography · 2024Article
- Characterization of Biological Samples Using Ultra-Short and Ultra-Bright XFEL Pulses.Advances in experimental medicine and biology · 2024Article
- Explainable machine learning for diffraction patterns.Journal of applied crystallography · 2023Article
- A Python package based on robust statistical analysis for serial crystallography data processing.Acta crystallographica. Section D, Structural biology · 2023Article
- Data reduction for X-ray serial crystallography using machine learning.Journal of applied crystallography · 2023Article
- Automatic bad-pixel mask maker for X-ray pixel detectors with application to serial crystallography.Journal of applied crystallography · 2022Article
- A multi-million image Serial Femtosecond Crystallography dataset collected at the European XFEL.Scientific data · 2022Article
Corrections and comments
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Authors and funding
19 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A peak-finding algorithm for serial crystallography (SX) data analysis based on the principle of 'robust statistics' has been developed. Methods which are statistically robust are generally more insensitive to any departures from model assumptions and are particularly effective when analysing mixtures of probability distributions. For example, these methods enable the discretization of data into a group comprising inliers (
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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.