Machine learning approaches to cryoEM density modification differentially affect biomacromolecule and ligand density quality
The application of machine learning to cryogenic electron microscopy (cryoEM) data analysis has added a valuable set of tools to the cryoEM data processing pipeline. As these tools become more accessible and widely available, the implications of their use should be assessed. We noticed that machine...
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| Huvudupphov: | , , |
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| Materialtyp: | Artigo |
| Språk: | Inglês |
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Frontiers Media S.A.
2024-04-01
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| Serie: | Frontiers in Molecular Biosciences |
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| Länkar: | https://www.frontiersin.org/articles/10.3389/fmolb.2024.1404885/full |
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