Unknown

Dataset Information

0

Image-based crystal detection: a machine-learning approach.


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.

SUBMITTER: Liu R 

PROVIDER: S-EPMC2585161 | biostudies-literature |

REPOSITORIES: biostudies-literature

Similar Datasets

| S-EPMC6657638 | biostudies-literature
| S-EPMC8336856 | biostudies-literature
2023-04-01 | GSE226159 | GEO
2022-09-14 | E-MTAB-11607 | biostudies-arrayexpress
| S-EPMC7591033 | biostudies-literature
| S-EPMC7732714 | biostudies-literature
| S-EPMC8590647 | biostudies-literature
| S-EPMC5471192 | biostudies-literature
| S-EPMC8372004 | biostudies-literature
| S-EPMC6664779 | biostudies-literature