Machine learning-based real-time object locator/evaluator for cryo-EM data collection

Koji Yonekura, Saori Maki-Yonekura, Hisashi Naitow, Tasuku Hamaguchi, Kiyofumi Takaba

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Article number1044
JournalCommunications Biology
Issue number1
Publication statusPublished - 2021 Dec
Externally publishedYes

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)


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