ArticleJournal of structural and functional genomics2010
Protein crystallization analysis on the World Community Grid.
Article in Journal of structural and functional genomics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 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
- Crystal search - feasibility study of a real-time deep learning process for crystallization well images.Acta crystallographica. Section A, Foundations and advances · 2023Article
- Protein Crystallization Segmentation and Classification Using Subordinate Color Channel in Fluorescence Microscopy Images.Journal of fluorescence · 2020Article
- 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
- Evaluation of Normalization and PCA on the Performance of Classifiers for Protein Crystallization Images.Proceedings of IEEE Southeastcon. IEEE Southeastcon · 2014Article
- Evaluation of Semi-supervised Learning for Classification of Protein Crystallization Imagery.Proceedings of IEEE Southeastcon. IEEE Southeastcon · 2014Article
- Real-Time Protein Crystallization Image Acquisition and Classification System.Crystal growth & design · 2013Article
- Some practical guidelines for UV imaging in the protein crystallization laboratory.Acta crystallographica. Section F, Structural biology and crystallization communications · 2013Article
- Lessons from high-throughput protein crystallization screening: 10 years of practical experience.Expert opinion on drug discovery · 2011Article
- What's in a drop? Correlating observations and outcomes to guide macromolecular crystallization experiments.Crystal growth & design · 2011Article
- Super-Thresholding: Supervised Thresholding of Protein Crystal Images.IEEE/ACM transactions on computational biology and bioinformaticsArticle
- CrystPro: Spatiotemporal Analysis of Protein Crystallization Images.Crystal growth & designArticle
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
We have developed an image-analysis and classification system for automatically scoring images from high-throughput protein crystallization trials. Image analysis for this system is performed by the Help Conquer Cancer (HCC) project on the World Community Grid. HCC calculates 12,375 distinct image features on microbatch-under-oil images from the Hauptman-Woodward Medical Research Institute's High-Throughput Screening Laboratory. Using HCC-computed image features and a massive training set of 165,351 hand-scored images, we have trained multiple Random Forest classifiers that accurately recognize multiple crystallization outcomes, including crystals, clear drops, precipitate, and others. The system successfully recognizes 80% of crystal-bearing images, 89% of precipitate images, and 98% of clear drops.
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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.