title: 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.
authors.affiliation: Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA.
authors.forename: Robert
authors.initials: R
authors.lastname: Langlois
authors.forename: Jesper
authors.initials: J
authors.lastname: Pallesen
authors.forename: Joachim
authors.initials: J
authors.lastname: Frank
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D020285:Cryoelectron Microscopy / Q000379:methods*