WikiSysop: Created page with "== Citation == Giri, N., Chen, X., Wang, L. and Cheng, J. 2025. A labeled dataset for AI-based cryo-EM map enhancement. Computational and Structural Biotechnology Journal. 27, (2025), 2843–2850. == Abstract == Cryogenic electron microscopy (cryo-EM) has transformed structural biology by enabling near atomic resolution imaging of macromolecular complexes. However, cryo-EM density maps suffer from intrinsic noise arising from structural sources, shot noise, and digita..."

2026-04-30T06:42:34Z

Created page with "== Citation == Giri, N., Chen, X., Wang, L. and Cheng, J. 2025. A labeled dataset for AI-based cryo-EM map enhancement. Computational and Structural Biotechnology Journal. 27, (2025), 2843–2850. == Abstract == Cryogenic electron microscopy (cryo-EM) has transformed structural biology by enabling near atomic resolution imaging of macromolecular complexes. However, cryo-EM density maps suffer from intrinsic noise arising from structural sources, shot noise, and digita..."

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== Citation ==

Giri, N., Chen, X., Wang, L. and Cheng, J. 2025. A labeled dataset for AI-based cryo-EM map enhancement. Computational and Structural Biotechnology Journal. 27, (2025), 2843–2850.

== Abstract ==

Cryogenic electron microscopy (cryo-EM) has transformed structural biology by enabling near atomic resolution
imaging of macromolecular complexes. However, cryo-EM density maps suffer from intrinsic noise arising from
structural sources, shot noise, and digital recording, which complicates accurate model building. While various
methods for denoising cryo-EM density maps exist, there is a lack of standardized datasets for benchmarking
artificial intelligence (AI) approaches. Here, we present an open-source dataset for cryo-EM density map denoising
comprising 650 high-resolution (1-4 Å) experimental maps paired with three types of generated label maps:
regression maps capturing idealized density distributions, binary classification maps distinguishing structural
elements from background, and atom-type classification maps. Each map is standardized to 1 Å voxel size and
validated through Fourier Shell Correlation analysis, demonstrating substantial resolution improvements in label
maps compared to experimental maps. This resource bridges the gap between structural biology and artificial
intelligence communities, allowing researchers to develop and benchmark innovative methods for enhancing
cryo-EM density maps.

== Keywords ==

== Links ==

https://www.sciencedirect.com/science/article/pii/S2001037025002570

== Related software ==

== Related methods ==

== Comments ==

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