WikiSysop: Created page with "== Citation == Evans, L., Murad, O.-V., Dingeldein, L., Cossio, P., Covino, R. and Meila, M. 2025. Cryo-EM images are intrinsically low dimensional. PRX Life. 3, 3 (2025), 33025. == Abstract == Simulation-based inference provides a powerful framework for cryoelectron microscopy, employing neural networks in methods like CryoSBI to infer biomolecular conformations via learned latent representations. This latent space represents a rich opportunity, encoding valuable inf..."

2026-05-25T06:06:43Z

Created page with "== Citation == Evans, L., Murad, O.-V., Dingeldein, L., Cossio, P., Covino, R. and Meila, M. 2025. Cryo-EM images are intrinsically low dimensional. PRX Life. 3, 3 (2025), 33025. == Abstract == Simulation-based inference provides a powerful framework for cryoelectron microscopy, employing neural networks in methods like CryoSBI to infer biomolecular conformations via learned latent representations. This latent space represents a rich opportunity, encoding valuable inf..."

New page

== Citation ==

Evans, L., Murad, O.-V., Dingeldein, L., Cossio, P., Covino, R. and Meila, M. 2025. Cryo-EM images are intrinsically low dimensional. PRX Life. 3, 3 (2025), 33025.

== Abstract ==

Simulation-based inference provides a powerful framework for cryoelectron microscopy, employing neural networks in methods like CryoSBI to infer biomolecular conformations via learned latent representations. This latent space represents a rich opportunity, encoding valuable information about the physical system and the inference process. Harnessing this potential hinges on understanding the underlying geometric structure of these representations. We investigate this structure by applying manifold learning techniques to CryoSBI representations of a simulated benchmark dataset and both simulated and experimental images of hemagglutinin. We reveal that these high-dimensional data inherently populate low-dimensional, smooth manifolds, with simulated data effectively covering the experimental counterpart. By characterizing the manifold's geometry using Diffusion Maps and identifying its principal axes of variation via coordinate interpretation methods, we establish a direct link between the latent structure and key physical parameters. Discovering this intrinsic low-dimensionality and interpretable geometric organization not only validates the CryoSBI approach but also enables us to learn more from the data structure and provides opportunities for improving future inference strategies by exploiting this revealed manifold geometry.

== Keywords ==

== Links ==

https://journals.aps.org/prxlife/abstract/10.1103/txrb-fw3z

== Related software ==

== Related methods ==

== Comments ==

2025Evans LowDim - Revision history