https://3demmethods.i2pc.es/index.php?action=history&feed=atom&title=2022Donnat_GAN 2026-05-24T21:06:52Z Revision history for this page on the wiki MediaWiki 1.44.2 https://3demmethods.i2pc.es/index.php?title=2022Donnat_GAN&diff=4359&oldid=prev 2023-07-04T13:49:34Z

<p>Created page with &quot;== Citation == Donnat, Claire / Levy, Axel / Poitevin, Frederic / Zhong, Ellen D. / Miolane, Nina. Deep generative modeling for volume reconstruction in cryo-electron microsc...&quot;</p> <p><b>New page</b></p><div>== Citation ==<br /> <br /> Donnat, Claire / Levy, Axel / Poitevin, Frederic / Zhong, Ellen D. / Miolane, Nina. Deep generative modeling for volume reconstruction in cryo-electron microscopy. 2022. J. Structural Biology, p. 107920 <br /> <br /> == Abstract ==<br /> <br /> Advances in cryo-electron microscopy (cryo-EM) for high-resolution imaging of biomolecules in solution have<br /> provided new challenges and opportunities for algorithm development for 3D reconstruction. Next-generation<br /> volume reconstruction algorithms that combine generative modelling with end-to-end unsupervised deep<br /> learning techniques have shown promise, but many technical and theoretical hurdles remain, especially when<br /> applied to experimental cryo-EM images. In light of the proliferation of such methods, we propose here a critical<br /> review of recent advances in the field of deep generative modelling for cryo-EM reconstruction. The present review<br /> aims to (i) provide a unified statistical framework using terminology familiar to machine learning researchers<br /> with no specific background in cryo-EM, (ii) review the current methods in this framework, and (iii) outline<br /> outstanding bottlenecks and avenues for improvements in the field.<br /> <br /> == Keywords ==<br /> <br /> == Links ==<br /> <br /> https://www.sciencedirect.com/science/article/pii/S1047847722000909<br /> <br /> == Related software ==<br /> <br /> == Related methods ==<br /> <br /> == Comments ==</div>

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