Machine learning-based real-time object locator/evaluator for cryo-EM data collection.
Commun Biol
; 4(1): 1044, 2021 09 07.
Article
in En
| MEDLINE
| ID: mdl-34493805
ABSTRACT
In cryo-electron microscopy (cryo-EM) data collection, locating a target object is error-prone. Here, we present a machine learning-based approach with a real-time object locator named yoneoLocr using YOLO, a well-known object detection system. Implementation shows its effectiveness in rapidly and precisely locating carbon holes in single particle cryo-EM and in locating crystals and evaluating electron diffraction (ED) patterns in automated cryo-electron crystallography (cryo-EX) data collection. The proposed approach will advance high-throughput and accurate data collection of images and diffraction patterns with minimal human operation.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Image Processing, Computer-Assisted
/
Data Collection
/
Crystallography, X-Ray
/
Cryoelectron Microscopy
/
Machine Learning
Language:
En
Journal:
Commun Biol
Year:
2021
Document type:
Article
Affiliation country:
Japan