Pattern recognition and classification of images of biological macromolecules using artificial neural networks
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
Cell PressDate
1994Citation
10.1016/S0006-3495(94)80974-9
Biophysical Journal 66.6 (1994): 1804–1814
ISSN
0006-3495 (print); 1542-0086 (online)DOI
10.1016/S0006-3495(94)80974-9Funded by
This work was supported in part by Grant PB91-0910 from Plan General de Promoción del Conocimiento (Dirección General de Investigación Cientifica y Tecnica, DGICYT, Spain). R. Marabini holds a CSIC predoctoral fellowship.Editor's Version
http://dx.doi.org/10.1016/S0006-3495(94)80974-9Subjects
InformáticaNote
This is the author’s version of a work that was accepted for publication in Biophysical Journal. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Biophysical Journal, 66, 6, (1994) DOI: 10.1016/S0006-3495(94)80974-9Rights
Copyright © 1994 The Biophysical Society. Published by Elsevier Inc. All rights reserved.Abstract
The goal of this work was to analyze an image data set and to detect the structural variability within this set. Two algorithms for pattern recognition based on neural networks are presented, one that performs an unsupervised classification (the self-organizing map) and the other a supervised classification (the learning vector quantization). The approach has a direct impact in current strategies for structural determination from electron microscopic images of biological macromolecules. In this work we performed a classification of both aligned but heterogeneous image data sets as well as basically homogeneous but otherwise rotationally misaligned image populations, in the latter case completely avoiding the typical reference dependency of correlation-based alignment methods. A number of examples on chaperonins are presented. The approach is computationally fast and robust with respect to noise. Programs are available through ftp.
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Google Scholar:Marabini Ruiz, Roberto
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Carazo García, José María
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