A self-supervised workflow for particle picking in cryo-EM
Abstract
High-resolution single-particle cryo-EM data analysis relies on accurate particle picking. To facilitate the particle picking process, a self-supervised workflow has been developed. This includes an iterative strategy, which uses a 2D class average to improve training particles, and a progressively improved convolutional neural network for particle picking. To automate the selection of particles, a threshold is defined (%/Res) using the ratio of...
Paper Details
Title
A self-supervised workflow for particle picking in cryo-EM
Published Date
Jun 23, 2020
Journal
Volume
7
Issue
4
Pages
719 - 727
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