Convolutional Neural Networks for Automated Annotation of Cellular Cryo-Electron Tomograms
By Muyuan Chen, W. Dai, Stella Y. Sun, Darius Jonasch, Cynthia Y. He, M. Schmid, Wah Chiu, Steven J. Ludtke. Nature Methods, 2017
374 citations recorded by the source corpus.
Cellular electron cryotomography offers researchers the ability to observe macromolecules frozen in action in situ, but a primary challenge with this technique is identifying molecular components within the crowded cellular environment. We introduce a method that uses neural networks to dramatically reduce the time and human effort required for subcellular annotation and feature extraction. Subsequent subtomogram classification and averaging yield in situ structures of molecular components of interest. The method is available in the EMAN2.2 software package.
Subfields: Biophysics, Structural Biology, Surfaces, Coatings and Films
View Paper: https://www.semanticscholar.org/paper/c6527b7717b62a07abb0ae897203c5f2e1dada0b
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In Structural Biology
Citations in the corpus
- Respond-CAM: Analyzing Deep Models for 3D Imaging Data by Visualizations (2018, methodology): “[10,2] We applied our visualization methods to two important CECT CNN models, denoted CNN-1 (taken from [10]) and CNN-2.”
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- Molecular Architecture of the SARS-CoV-2 Virus (2020, background): “List of Supplementary Materials: Materials and Methods Figures S1-S5 Tables S1 References (41-58)”