https://3demmethods.i2pc.es/index.php?action=history&feed=atom&title=2025Tang_CryoLike2025Tang CryoLike - Revision history2026-05-01T07:51:28ZRevision history for this page on the wikiMediaWiki 1.44.2https://3demmethods.i2pc.es/index.php?title=2025Tang_CryoLike&diff=5134&oldid=prevWikiSysop: Created page with "== Citation == Tang, W.S., Soules, J., Rangan, A. and Cossio, P. 2025. CryoLike: a Python package for cryo-electron microscopy image-to-structure likelihood calculations. Biological Crystallography. 81, 12 (2025). == Abstract == Extracting conformational heterogeneity from cryo-electron microscopy (cryo-EM) images is particularly challenging for flexible biomolecules, where traditional 3D classification approaches often fail. Over the past few decades, advancements in..."2026-01-07T08:06:14Z<p>Created page with "== Citation == Tang, W.S., Soules, J., Rangan, A. and Cossio, P. 2025. CryoLike: a Python package for cryo-electron microscopy image-to-structure likelihood calculations. Biological Crystallography. 81, 12 (2025). == Abstract == Extracting conformational heterogeneity from cryo-electron microscopy (cryo-EM) images is particularly challenging for flexible biomolecules, where traditional 3D classification approaches often fail. Over the past few decades, advancements in..."</p>
<p><b>New page</b></p><div>== Citation ==<br />
<br />
Tang, W.S., Soules, J., Rangan, A. and Cossio, P. 2025. CryoLike: a Python package for cryo-electron microscopy image-to-structure likelihood calculations. Biological Crystallography. 81, 12 (2025).<br />
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== Abstract ==<br />
<br />
Extracting conformational heterogeneity from cryo-electron microscopy<br />
(cryo-EM) images is particularly challenging for flexible biomolecules, where<br />
traditional 3D classification approaches often fail. Over the past few decades,<br />
advancements in experimental and computational techniques have been made<br />
to tackle this challenge, especially Bayesian-based approaches that provide<br />
physically interpretable insights into cryo-EM heterogeneity. To reduce the<br />
computational cost for Bayesian approaches, and building upon previously<br />
developed Fourier–Bessel image-representation methods, we created CryoLike,<br />
computationally efficient software for evaluating image-to-structure (or imageto-<br />
volume) likelihoods across large image data sets, packaged in a user-friendly<br />
Python workflow.<br />
<br />
== Keywords ==<br />
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== Links ==<br />
<br />
https://journals.iucr.org/paper?bar5002<br />
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== Related software ==<br />
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== Related methods ==<br />
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== Comments ==</div>WikiSysop