ArticlePloS one2018
Classification of crystallization outcomes using deep convolutional neural networks.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
What it found
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Who cites it
28 citing papers in PubMed.
- The evolving role of structural biology in pharma: integration of X-ray crystallography, cryo-electron microscopy and beyond.Acta crystallographica. Section D, Structural biology · 2026Review
- AXIS: a Lab-in-the-Loop machine learning approach for automated detection of macromolecular crystals.IUCrJ · 2026Article
- Perspectives Towards AI and ML.Advances in biochemical engineering/biotechnology · 2026Review
- CHiMP: deep-learning tools trained on protein crystallization micrographs to enable automation of experiments.Acta crystallographica. Section D, Structural biology · 2024Article
- Managing macromolecular crystallographic data with a laboratory information management system.Acta crystallographica. Section D, Structural biology · 2024Article
- Robust image descriptor for machine learning based data reduction in serial crystallography.Journal of applied crystallography · 2024Article
- Integrating machine learning to advance epitope mapping.Frontiers in immunology · 2024Review
- X-Ray Crystallography of Viruses.Sub-cellular biochemistry · 2024Review
- 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
- High-Throughput Screening to Obtain Crystal Hits for Protein Crystallography.Journal of visualized experiments : JoVE · 2023Article
- 20 years of crystal hits: progress and promise in ultrahigh-throughput crystallization screening.Acta crystallographica. Section D, Structural biology · 2023Article
- Moving beyond MARCO.PloS one · 2023Article
- Not getting in too deep: A practical deep learning approach to routine crystallisation image classification.PloS one · 2023Article
- Article
- IceBear: an intuitive and versatile web application for research-data tracking from crystallization experiment to PDB deposition.Acta crystallographica. Section D, Structural biology · 2021Article
- 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
- A Searchable Database of Crystallization Cocktails in the PDB: Analyzing the Chemical Condition Space.Patterns (New York, N.Y.) · 2020Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Machine Recognition of Crystallization Outcomes (MARCO) initiative has assembled roughly half a million annotated images of macromolecular crystallization experiments from various sources and setups. Here, state-of-the-art machine learning algorithms are trained and tested on different parts of this data set. We find that more than 94% of the test images can be correctly labeled, irrespective of their experimental origin. Because crystal recognition is key to high-density screening and the systematic analysis of crystallization experiments, this approach opens the door to both industrial and fundamental research applications.
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Registered trials
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