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Convolutional neural networks for automated annotation of cellular cryo-electron tomograms
Open accessAbstract
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.
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LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012) · arXiv · doi:10.48550/arxiv.1207.0580 · Cited by 6,382
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Metadata
- Authors
- Chen, M., Dai, W., Sun, S. Y., Jonasch, D., He, C. Y., Schmid, M. F., Chiu, W., & Ludtke, S. J.
- Venue
- Nature Methods
- Published
- 2017-08-28
- Volume
- 14
- Issue
- 10
- Pages
- 983-985
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W2580555377
- Cited by
- 398
- References
- 2
- FWCI
- 11.01
- Type
- journal article
- Language
- en
- Open access
- green
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- Chen, M., Dai, W., Sun, S. Y., Jonasch, D., He, C. Y., Schmid, M. F., Chiu, W., & Ludtke, S. J. (2017). Convolutional neural networks for automated annotation of cellular cryo-electron tomograms. Nature Methods. https://doi.org/10.1038/nmeth.4405
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