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== Citation ==

Wang, Xiangwen / Lu, Yonggang / Lin, Xianghong / Li, Jianwei / Zhang, Zequn.
An Unsupervised Classification Algorithm for Heterogeneous Cryo-EM Projection Images Based on Autoencoders.
2023. Intl. J. of Molecular Sciences, Vol. 24, No. 9, p. 8380

== Abstract ==

Heterogeneous three-dimensional (3D) reconstruction in single-particle cryo-electron microscopy
(cryo-EM) is an important but very challenging technique for recovering the conformational
heterogeneity of flexible biological macromolecules such as proteins in different functional states.
Heterogeneous projection image classification is a feasible solution to solve the structural heterogeneity
problem in single-particle cryo-EM. The majority of heterogeneous projection image classification
methods are developed using supervised learning technology or require a large amount of a priori
knowledge, such as the orientations or common lines of the projection images, which leads to
certain limitations in their practical applications. In this paper, an unsupervised heterogeneous
cryo-EM projection image classification algorithm based on autoencoders is proposed, which only
needs to know the number of heterogeneous 3D structures in the dataset and does not require any
labeling information of the projection images or other a priori knowledge. A simple autoencoder
with multi-layer perceptrons trained in iterative mode and a complex autoencoder with residual
networks trained in one-pass learning mode are implemented to convert heterogeneous projection
images into latent variables. The extracted high-dimensional features are reduced to two dimensions
using the uniform manifold approximation and projection dimensionality reduction algorithm, and
then clustered using the spectral clustering algorithm. The proposed algorithm is applied to two
heterogeneous cryo-EM datasets for heterogeneous 3D reconstruction. Experimental results show
that the proposed algorithm can effectively extract category features of heterogeneous projection
images and achieve high classification and reconstruction accuracy, indicating that the proposed
algorithm is effective for heterogeneous 3D reconstruction in single-particle cryo-EM.

== Keywords ==

== Links ==

https://www.mdpi.com/1422-0067/24/9/8380

== Related software ==

== Related methods ==

== Comments ==

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WikiSysop: Created page with "== Citation == Wang, Xiangwen / Lu, Yonggang / Lin, Xianghong / Li, Jianwei / Zhang, Zequn. An Unsupervised Classification Algorithm for Heterogeneous Cryo-EM Projection Imag..."