Deep learning improves macromolecule identification in 3D cellular cryo-electron tomograms

Cryogenic electron tomography (cryo-ET) visualizes the 3D spatial distribution of macromolecules at nanometer resolution inside native cells. However, automated identification of macromolecules inside cellular tomograms is challenged by noise and reconstruction artifacts, as well as the presence of...

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Vydáno v:	Nature methods Ročník 18; číslo 11; s. 1386 - 1394
Hlavní autoři:	Moebel, Emmanuel, Martinez-Sanchez, Antonio, Lamm, Lorenz, Righetto, Ricardo D, Wietrzynski, Wojciech, Albert, Sahradha, Larivière, Damien, Fourmentin, Eric, Pfeffer, Stefan, Ortiz, Julio, Baumeister, Wolfgang, Peng, Tingying, Engel, Benjamin D, Kervrann, Charles
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Nature Publishing Group 01.11.2021
Témata:	Algorithms Annotations Artificial neural networks Chlamydomonas reinhardtii - metabolism Computer applications Cryoelectron Microscopy - methods Datasets Deep Learning Electron Microscope Tomography - methods Electrons Image Processing, Computer-Assisted - methods Machine learning Macromolecular Substances - chemistry Macromolecules Membranes Neural networks Neural Networks, Computer Oxygenase Photosystem II Photosystem II Protein Complex - chemistry Ribosomes Ribosomes - chemistry Ribulose-Bisphosphate Carboxylase - chemistry Spatial distribution Template matching
ISSN:	1548-7091, 1548-7105, 1548-7105
On-line přístup:	Získat plný text
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