2022Donnat GAN

From 3DEM-Methods
Revision as of 13:49, 4 July 2023 by WikiSysop (talk | contribs) (Created page with "== Citation == Donnat, Claire / Levy, Axel / Poitevin, Frederic / Zhong, Ellen D. / Miolane, Nina. Deep generative modeling for volume reconstruction in cryo-electron microsc...")
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to navigation Jump to search

Citation

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

Abstract

Advances in cryo-electron microscopy (cryo-EM) for high-resolution imaging of biomolecules in solution have provided new challenges and opportunities for algorithm development for 3D reconstruction. Next-generation volume reconstruction algorithms that combine generative modelling with end-to-end unsupervised deep learning techniques have shown promise, but many technical and theoretical hurdles remain, especially when applied to experimental cryo-EM images. In light of the proliferation of such methods, we propose here a critical review of recent advances in the field of deep generative modelling for cryo-EM reconstruction. The present review aims to (i) provide a unified statistical framework using terminology familiar to machine learning researchers with no specific background in cryo-EM, (ii) review the current methods in this framework, and (iii) outline outstanding bottlenecks and avenues for improvements in the field.

Keywords

Links

https://www.sciencedirect.com/science/article/pii/S1047847722000909

Related software

Related methods

Comments