WikiSysop: Created page with "== Citation == Shekarforoush, S., Lindell, D.B., Brubaker, M.A. and Fleet, D.J. 2024. Cryospin: improving ab-initio cryo-EM reconstruction with semi-amortized pose inference. Advances in Neural Information Processing Systems. 37, (2024), 55785–55809. == Abstract == Cryo-EM is an increasingly popular method for determining the atomic resolution 3D structure of macromolecular complexes (eg, proteins) from noisy 2D images captured by an electron microscope. The computa..."

2025-11-14T17:24:14Z

Created page with "== Citation == Shekarforoush, S., Lindell, D.B., Brubaker, M.A. and Fleet, D.J. 2024. Cryospin: improving ab-initio cryo-EM reconstruction with semi-amortized pose inference. Advances in Neural Information Processing Systems. 37, (2024), 55785–55809. == Abstract == Cryo-EM is an increasingly popular method for determining the atomic resolution 3D structure of macromolecular complexes (eg, proteins) from noisy 2D images captured by an electron microscope. The computa..."

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== Citation ==

Shekarforoush, S., Lindell, D.B., Brubaker, M.A. and Fleet, D.J. 2024. Cryospin: improving ab-initio cryo-EM reconstruction with semi-amortized pose inference. Advances in Neural Information Processing Systems. 37, (2024), 55785–55809.

== Abstract ==

Cryo-EM is an increasingly popular method for determining the atomic resolution
3D structure of macromolecular complexes (eg, proteins) from noisy 2D images
captured by an electron microscope. The computational task is to reconstruct
the 3D density of the particle, along with 3D pose of the particle in each 2D
image, for which the posterior pose distribution is highly multi-modal. Recent
developments in cryo-EM have focused on deep learning for which amortized
inference has been used to predict pose. Here, we address key problems with
this approach, and propose a new semi-amortized method, cryoSPIN, in which
reconstruction begins with amortized inference and then switches to a form of
auto-decoding to refine poses locally using stochastic gradient descent. Through
evaluation on synthetic datasets, we demonstrate that cryoSPIN is able to handle
multi-modal pose distributions during the amortized inference stage, while the later,
more flexible stage of direct pose optimization yields faster and more accurate
convergence of poses compared to baselines. On experimental data, we show
that cryoSPIN outperforms the state-of-the-art cryoAI in speed and reconstruction
quality.

== Keywords ==

== Links ==

https://proceedings.neurips.cc/paper_files/paper/2024/hash/6521937507d78f327cd402401be73bf2-Abstract-Conference.html

== Related software ==

== Related methods ==

== Comments ==

2024Shekarforoush CryoSPIN - Revision history