title: Automatic particle selection from electron micrographs using machine learning techniques.
abstract: The 3D reconstruction of biological specimens using Electron Microscopy is currently capable of achieving subnanometer resolution. Unfortunately, this goal requires gathering tens of thousands of projection images that are frequently selected manually from micrographs. In this paper we introduce a new automatic particle selection that learns from the user which particles are of interest. The training phase is semi-supervised so that the user can correct the algorithm during picking and specifically identify incorrectly picked particles. By treating such errors specially, the algorithm attempts to minimize the number of false positives. We show that our algorithm is able to produce datasets with fewer wrongly selected particles than previously reported methods. Another advantage is that we avoid the need for an initial reference volume from which to generate picking projections by instead learning which particles to pick from the user. This package has been made publicly available in the open-source package Xmipp.
authors: Unidad de Biocomputación, Centro Nacional de Biotecnología (CSIC), Campus Universidad Autónoma s/n, 28049 Cantoblanco, Madrid, Spain. coss@cnb.csic.es
authors: C O S
authors: CO
authors: Sorzano
authors: E
authors: E
authors: Recarte
authors: M
authors: M
authors: Alcorlo
authors: J R
authors: JR
authors: Bilbao-Castro
authors: C
authors: C
authors: San-Martín
authors: R
authors: R
authors: Marabini
authors: J M
authors: JM
authors: Carazo
mesh_terms: D000256:Adenoviridae / Q000737:chemistry; D000465:Algorithms; D000957:Antigens, Viral, Tumor / Q000737:chemistry; D001185:Artificial Intelligence; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008854:Microscopy, Electron; D010316:Particle Size; D051716:Replication Protein A / Q000737:chemistry
chemical_list: D000957:Antigens, Viral, Tumor; D051716:Replication Protein A