ArticleActa crystallographica. Section D, Biological crystallography2008
Image-based crystal detection: a machine-learning approach.
Article in Acta crystallographica. Section D, Biological crystallography, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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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
22 citing papers in PubMed.
- AXIS: a Lab-in-the-Loop machine learning approach for automated detection of macromolecular crystals.IUCrJ · 2026Article
- CHiMP: deep-learning tools trained on protein crystallization micrographs to enable automation of experiments.Acta crystallographica. Section D, Structural biology · 2024Article
- Deep learning applications in protein crystallography.Acta crystallographica. Section A, Foundations and advances · 2024Review
- 20 years of crystal hits: progress and promise in ultrahigh-throughput crystallization screening.Acta crystallographica. Section D, Structural biology · 2023Article
- Machine learning-based protein crystal detection for monitoring of crystallization processes enabled with large-scale synthetic data sets of photorealistic images.Analytical and bioanalytical chemistry · 2022Article
- Article
- A fully automated crystallization apparatus for small protein quantities.Acta crystallographica. Section F, Structural biology communications · 2021Article
- The low-cost Shifter microscope stage transforms the speed and robustness of protein crystal harvesting.Acta crystallographica. Section D, Structural biology · 2021Article
- An automated platform forIUCrJ · 2020Article
- BraggNet: integrating Bragg peaks using neural networks.Journal of applied crystallography · 2019Article
- Volumetric SegmentationIEEE/ACM International Symposium on Cluster, Cloud and Grid Computing. IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing · 2019Article
- Article
- Feature analysis for classification of trace fluorescent labeled protein crystallization images.BioData mining · 2017Article
- Characterization of Protein Nanocrystals Based on the Reversibility of Crystallization.Crystal growth & design · 2016Article
- Using textons to rank crystallization droplets by the likely presence of crystals.Acta crystallographica. Section D, Biological crystallography · 2014Article
- Approaches to automated protein crystal harvesting.Acta crystallographica. Section F, Structural biology communications · 2014Article
- Real-Time Protein Crystallization Image Acquisition and Classification System.Crystal growth & design · 2013Article
- Second-order nonlinear optical imaging of chiral crystals.Annual review of analytical chemistry (Palo Alto, Calif.) · 2011Review
- Protein crystallization analysis on the World Community Grid.Journal of structural and functional genomics · 2010Article
- Nonlinear optical imaging of integral membrane protein crystals in lipidic mesophases.Analytical chemistry · 2010Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The ability of computers to learn from and annotate large databases of crystallization-trial images provides not only the ability to reduce the workload of crystallization studies, but also an opportunity to annotate crystallization trials as part of a framework for improving screening methods. Here, a system is presented that scores sets of images based on the likelihood of containing crystalline material as perceived by a machine-learning algorithm. The system can be incorporated into existing crystallization-analysis pipelines, whereby specialists examine images as they normally would with the exception that the images appear in rank order according to a simple real-valued score. Promising results are shown for 319 112 images associated with 150 structures solved by the Joint Center for Structural Genomics pipeline during the 2006-2007 year. Overall, the algorithm achieves a mean receiver operating characteristic score of 0.919 and a 78% reduction in human effort per set when considering an absolute score cutoff for screening images, while incurring a loss of five out of 150 structures.
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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.