ArticleActa crystallographica. Section D, Structural biology2022
Detecting ice artefacts in processed macromolecular diffraction data with machine learning.
Article in Acta crystallographica. Section D, Structural biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Artificial intelligence in structural biology: Preface.Structural dynamics (Melville, N.Y.) · 2025Article
- Robust and automatic beamstop shadow outlier rejection: combining crystallographic statistics with modern clustering under a semi-supervised learning strategy.Acta crystallographica. Section D, Structural biology · 2024Article
- The CCP4 suite: integrative software for macromolecular crystallography.Acta crystallographica. Section D, Structural biology · 2023Article
- Errors in structural biology are not the exception.Acta crystallographica. Section D, Structural biology · 2023Article
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5 authors.
Funding
Abstract
Contamination with diffraction from ice crystals can negatively affect, or even impede, macromolecular structure determination, and therefore detecting the resulting artefacts in diffraction data is crucial. However, once the data have been processed it can be very difficult to automatically recognize this problem. To address this, a set of convolutional neural networks named Helcaraxe has been developed which can detect ice-diffraction artefacts in processed diffraction data from macromolecular crystals. The networks outperform previous algorithms and will be available as part of the AUSPEX web server and the CCP4-distributed software.
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