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Title | Convolutional neural networks for automated annotation of cellular cryo-electron tomograms. |
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Journal, issue, pages | Nat Methods, Vol. 14, Issue 10, Page 983-985, Year 2017 |
Publish date | Aug 28, 2017 |
Authors | Muyuan Chen / Wei Dai / Stella Y Sun / Darius Jonasch / Cynthia Y He / Michael F Schmid / Wah Chiu / Steven J Ludtke / |
PubMed Abstract | 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 ...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. |
External links | Nat Methods / PubMed:28846087 / PubMed Central |
Methods | EM (subtomogram averaging) / EM (tomography) |
Resolution | 38.5 - 80.0 Å |
Structure data | EMDB-8589: EMDB-8590: EMDB-8591: EMDB-8592: EMDB-8593: EMDB-8594: |
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