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Reference-free particle selection enhanced with semi-supervised machine learning for cryo-electron microscopy

Authors
Journal
Journal of Structural Biology
1047-8477
Publisher
Elsevier
Volume
175
Issue
3
Identifiers
DOI: 10.1016/j.jsb.2011.06.004
Keywords
  • Particle Selection
  • Reference-Free
  • Semi-Supervised
  • Difference Of Gaussian
  • Cryo-Em
Disciplines
  • Computer Science

Abstract

Abstract Reference-based methods have dominated the approaches to the particle selection problem, proving fast, and accurate on even the most challenging micrographs. A reference volume, however, is not always available and compiling a set of reference projections from the micrographs themselves requires significant effort to attain the same level of accuracy. We propose a reference-free method to quickly extract particles from the micrograph. The method is augmented with a new semi-supervised machine-learning algorithm to accurately discriminate particles from contaminants and noise.

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