A GA-optimized neural network for classification of biological particles from electron-microscopy images

1997 ◽  
pp. 1174-1182
Author(s):  
J. J. Merelo ◽  
A. Prieto ◽  
F. Morán ◽  
R. Marabini ◽  
J. M. Carazo
Keyword(s):  
Neural Network ◽  
Electron Microscopy ◽  
Biological Particles ◽  
Microscopy Images

scholarly journals Maskiton: Interactive, web-based classification of single-particle electron microscopy images

2013 ◽  
Vol 182 (2) ◽  
pp. 155-163 ◽  
Author(s):  
Craig Yoshioka ◽  
Dmitry Lyumkis ◽  
Bridget Carragher ◽  
Clinton S. Potter
Keyword(s):  
Electron Microscopy ◽  
Single Particle ◽  
Web Based ◽  
Single Particle Electron Microscopy ◽  
Microscopy Images

scholarly journals Pre-pro is a fast pre-processor for single-particle cryo-EM by enhancing 2D classification

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Szu-Chi Chung ◽  
Hsin-Hung Lin ◽  
Po-Yao Niu ◽  
Shih-Hsin Huang ◽  
I-Ping Tu ◽  
...  
Keyword(s):  
Neural Network ◽  
Electron Microscopy ◽  
3D Reconstruction ◽  
Dimension Reduction ◽  
Single Particle ◽  
Minimal Cost ◽  
Initial Model ◽  
Cryo Electron Microscopy ◽  
Class Average ◽  
Microscopy Images

Abstract2D classification plays a pivotal role in analyzing single particle cryo-electron microscopy images. Here, we introduce a simple and loss-less pre-processor that incorporates a fast dimension-reduction (2SDR) de-noiser to enhance 2D classification. By implementing this 2SDR pre-processor prior to a representative classification algorithm like RELION and ISAC, we compare the performances with and without the pre-processor. Tests on multiple cryo-EM experimental datasets show the pre-processor can make classification faster, improve yield of good particles and increase the number of class-average images to generate better initial models. Testing on the nanodisc-embedded TRPV1 dataset with high heterogeneity using a 3D reconstruction workflow with an initial model from class-average images highlights the pre-processor improves the final resolution to 2.82 Å, close to 0.9 Nyquist. Those findings and analyses suggest the 2SDR pre-processor, of minimal cost, is widely applicable for boosting 2D classification, while its generalization to accommodate neural network de-noisers is envisioned.

scholarly journals Viewing Angle Classification of Cryo-Electron Microscopy Images Using Eigenvectors

2011 ◽  
Vol 4 (2) ◽  
pp. 723-759 ◽  
Author(s):  
A. Singer ◽  
Z. Zhao ◽  
Y. Shkolnisky ◽  
R. Hadani
Keyword(s):  
Electron Microscopy ◽  
Viewing Angle ◽  
Cryo Electron Microscopy ◽  
Microscopy Images

scholarly journals Deep Learning and Handcrafted Features for Virus Image Classification

2020 ◽  
Vol 6 (12) ◽  
pp. 143
Author(s):  
Loris Nanni ◽  
Eugenio De Luca ◽  
Marco Ludovico Facin ◽  
Gianluca Maguolo
Keyword(s):  
Neural Network ◽  
Electron Microscopy ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Support Vector ◽  
Transmission Electron ◽  
Vector Machines ◽  
Multiple Support ◽  
Multiple Support Vector Machines

In this work, we present an ensemble of descriptors for the classification of virus images acquired using transmission electron microscopy. We trained multiple support vector machines on different sets of features extracted from the data. We used both handcrafted algorithms and a pretrained deep neural network as feature extractors. The proposed fusion strongly boosts the performance obtained by each stand-alone approach, obtaining state of the art performance.

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