Donnat, C., Levy, A., Poitevin, F., Zhong, E., Miolane, N.
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.
Citation
Donnat, C., Levy, A., Poitevin, F., Zhong, E., & Miolane, N. (2023) Deep generative modeling for volume reconstruction in cryo-electron microscopy. Journal of Structural Biology
BibTeX
@article{donnat2022deep, title={Deep generative modeling for volume reconstruction in cryo-electron microscopy}, author={Donnat, Claire and Levy, Axel and Poitevin, Frederic and Zhong, Ellen D and Miolane, Nina}, journal={Journal of structural biology}, volume={214}, number={4}, pages={107920}, year={2022}, publisher={Elsevier} }
