https://3demmethods.i2pc.es/index.php?action=history&feed=atom&title=2026Li_Atomic2026Li Atomic - Revision history2026-04-05T10:13:59ZRevision history for this page on the wikiMediaWiki 1.44.2https://3demmethods.i2pc.es/index.php?title=2026Li_Atomic&diff=5161&oldid=prevWikiSysop: Created page with "== Citation == Li, T. and Huang, S.-Y. 2026. Deep learning–based postprocessing and model building for cryo-electron microscopy maps. Current Opinion in Structural Biology. 96, (2026), 103196. == Abstract == Cryo-electron microscopy (cryo-EM) has emerged as one of the most powerful techniques for determining the structures of biological macromolecules. The ultimate goal of cryo-EM is to determine the atomic structure of target molecules, where map postprocessing and..."2026-02-18T07:26:09Z<p>Created page with "== Citation == Li, T. and Huang, S.-Y. 2026. Deep learning–based postprocessing and model building for cryo-electron microscopy maps. Current Opinion in Structural Biology. 96, (2026), 103196. == Abstract == Cryo-electron microscopy (cryo-EM) has emerged as one of the most powerful techniques for determining the structures of biological macromolecules. The ultimate goal of cryo-EM is to determine the atomic structure of target molecules, where map postprocessing and..."</p>
<p><b>New page</b></p><div>== Citation ==<br />
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Li, T. and Huang, S.-Y. 2026. Deep learning–based postprocessing and model building for cryo-electron microscopy maps. Current Opinion in Structural Biology. 96, (2026), 103196.<br />
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== Abstract ==<br />
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Cryo-electron microscopy (cryo-EM) has emerged as one of the most powerful techniques for determining the structures of biological macromolecules. The ultimate goal of cryo-EM is to determine the atomic structure of target molecules, where map postprocessing and atomic-model building are two crucial final steps of the cryo-EM pipeline. With the fast development of artificial intelligence, deep learning has been implemented in various stages of cryo-EM. Here, we present a comprehensive overview of recent advances in map postprocessing and model building for cryo-EM maps with focuses on deep learning–based methods. We also discuss the advantages and limitations of current approaches as well as challenges that are left for future research.<br />
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== Links ==<br />
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https://www.sciencedirect.com/science/article/pii/S0959440X25002143<br />
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== Comments ==</div>WikiSysop