Classification of crystallization outcomes using deep convolutional neural networks

Autor: David R. So, Christopher J. Watkins, Shawn P. Williams, Julie Wilson, Vincent Vanhoucke, Andrew E. Bruno, Edward H. Snell, Janet Newman, Patrick Charbonneau
Jazyk: angličtina
Rok vydání: 2018
Předmět:
0301 basic medicine
FOS: Computer and information sciences
Computer Science - Machine Learning
Vision
Computer science
Social Sciences
Datasets as Topic
lcsh:Medicine
02 engineering and technology
Crystal structure
Crystallography
X-Ray

Convolutional neural network
law.invention
Machine Learning (cs.LG)
Crystal
law
Statistics - Machine Learning
Image Processing
Computer-Assisted

Chemical Precipitation
Psychology
Crystallization
lcsh:Science
Crystallography
Multidisciplinary
Contextual image classification
Artificial neural network
Physics
Chemical Reactions
Condensed Matter Physics
021001 nanoscience & nanotechnology
Chemistry
Physical Sciences
Crystal Structure
Sensory Perception
0210 nano-technology
Protein crystallization
Algorithms
Research Article
Macromolecule
Computer and Information Sciences
Neural Networks
Materials by Structure
Imaging Techniques
Materials Science
Image processing
Machine Learning (stat.ML)
Image Analysis
Research and Analysis Methods
Crystals
Precipitates
03 medical and health sciences
Solid State Physics
business.industry
Precipitation (chemistry)
lcsh:R
Biology and Life Sciences
Pattern recognition
Biomolecules (q-bio.BM)
Data set
030104 developmental biology
Quantitative Biology - Biomolecules
FOS: Biological sciences
Key (cryptography)
lcsh:Q
Neural Networks
Computer

Artificial intelligence
business
Neuroscience
Zdroj: PLoS ONE, Vol 13, Iss 6, p e0198883 (2018)
PLoS ONE
ISSN: 1932-6203
Popis: 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.
11 pages, 4 figures, minor text and figure updates
Databáze: OpenAIRE