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

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.

SUBMITTER: Langlois R

PROVIDER: S-EPMC3205936 | biostudies-literature | 2011 Sep

REPOSITORIES: biostudies-literature

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

Langlois Robert R Pallesen Jesper J Frank Joachim J

Journal of structural biology 20110617 3

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-le ...[more]

PMID: 21708269

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