WikiSysop: Created page with "== Citation == Chung, Szu-Chi. Cryo-forum: A framework for orientation recovery with uncertainty measure with the application in cryo-EM image analysis. 2024. J. Structural Biology, Vol. 216, No. 1, p. 108058 == Abstract == In single-particle cryo-electron microscopy (cryo-EM), efficient determination of orientation parameters for particle images poses a significant challenge yet is crucial for reconstructing 3D structures. This task is complicated by the high noise l..."

2024-08-06T06:43:38Z

Created page with "== Citation == Chung, Szu-Chi. Cryo-forum: A framework for orientation recovery with uncertainty measure with the application in cryo-EM image analysis. 2024. J. Structural Biology, Vol. 216, No. 1, p. 108058 == Abstract == In single-particle cryo-electron microscopy (cryo-EM), efficient determination of orientation parameters for particle images poses a significant challenge yet is crucial for reconstructing 3D structures. This task is complicated by the high noise l..."

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== Citation ==

Chung, Szu-Chi. Cryo-forum: A framework for orientation recovery with uncertainty measure with the application in cryo-EM image analysis. 2024. J. Structural Biology, Vol. 216, No. 1, p. 108058

== Abstract ==

In single-particle cryo-electron microscopy (cryo-EM), efficient determination of orientation parameters for
particle images poses a significant challenge yet is crucial for reconstructing 3D structures. This task is
complicated by the high noise levels in the datasets, which often include outliers, necessitating several timeconsuming
2D clean-up processes. Recently, solutions based on deep learning have emerged, offering a more
streamlined approach to the traditionally laborious task of orientation estimation. These solutions employ
amortized inference, eliminating the need to estimate parameters individually for each image. However, these
methods frequently overlook the presence of outliers and may not adequately concentrate on the components
used within the network. This paper introduces a novel method using a 10-dimensional feature vector for
orientation representation, extracting orientations as unit quaternions with an accompanying uncertainty metric.
Furthermore, we propose a unique loss function that considers the pairwise distances between orientations,
thereby enhancing the accuracy of our method. Finally, we also comprehensively evaluate the design choices in
constructing the encoder network, a topic that has not received sufficient attention in the literature. Our numerical
analysis demonstrates that our methodology effectively recovers orientations from 2D cryo-EM images in
an end-to-end manner. Notably, the inclusion of uncertainty quantification allows for direct clean-up of the
dataset at the 3D level. Lastly, we package our proposed methods into a user-friendly software suite named cryoforum,
designed for easy access by developers.

== Keywords ==

== Links ==

https://www.sciencedirect.com/science/article/pii/S1047847723001211

== Related software ==

== Related methods ==

== Comments ==

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