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

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

研究成果: Article査読

抄録

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.

本文言語English
論文番号1044
ジャーナルCommunications Biology
4
1
DOI
出版ステータスPublished - 2021 12

ASJC Scopus subject areas

  • 医学(その他)
  • 生化学、遺伝学、分子生物学(全般)
  • 農業および生物科学(全般)

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