ArticleACS omega2024
Determining Protein Secondary Structures in Heterogeneous Medium-Resolution Cryo-EM Images Using CryoSSESeg.
Article in ACS omega, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- A Novel Machine-Learning Based Method for Resolving Secondary Structure Topology in Medium-Resolution Cryo-EM Density Maps.International journal of molecular sciences · 2026Article
- Evaluating Deep Learning Architectures for Actin Filament Segmentation under Varying Noise Conditions in Simulated Cryo-ET Tomograms.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2025Article
- Connecting the dots: deep learning-based automated model building methods in cryo-EM.Frontiers in molecular biosciences · 2025Review
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While the acquisition of cryo-electron microscopy (cryo-EM) at near-atomic resolution is becoming more prevalent, a considerable number of density maps are still resolved only at intermediate resolutions (5-10 Å). Due to the large variation in quality among these medium-resolution density maps, extracting structural information from them remains a challenging task. This study introduces a convolutional neural network (CNN)-based framework, cryoSSESeg, to determine the organization of protein secondary structure elements in medium-resolution cryo-EM images. CryoSSESeg is trained on approximately 1300 protein chains derived from around 500 experimental cryo-EM density maps of varied quality. It demonstrates strong performance with residue-level
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