{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Selvaraj J"],"funding":["National Institutes of Health","NIH HHS","NIGMS NIH HHS"],"pagination":["btaf092"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11906401"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["41(3)"],"pubmed_abstract":["Motivation
Cryogenic electron microscopy (cryo-EM) is a core experimental technique used to determine the structure of macromolecules such as proteins. However, the effectiveness of cryo-EM is often hindered by the noise and missing density values in cryo-EM density maps caused by experimental conditions such as low contrast and conformational heterogeneity. Although various global and local map-sharpening techniques are widely employed to improve cryo-EM density maps, it is still challenging to efficiently improve their quality for building better protein structures from them.Results
In this study, we introduce CryoTEN-a 3D UNETR++ style transformer to improve cryo-EM maps effectively. CryoTEN is trained using a diverse set of 1295 cryo-EM maps as inputs and their correspo"],"journal":["Bioinformatics (Oxford, England)"],"pubmed_title":["CryoTEN: efficiently enhancing cryo-EM density maps using transformers."],"pmcid":["PMC11906401"],"funding_grant_id":["R01GM146340","R01 GM146340"],"pubmed_authors":["Selvaraj J","Wang L","Cheng J"],"additional_accession":[]},"is_claimable":false,"name":"CryoTEN: efficiently enhancing cryo-EM density maps using transformers.","description":"Motivation
Cryogenic electron microscopy (cryo-EM) is a core experimental technique used to determine the structure of macromolecules such as proteins. However, the effectiveness of cryo-EM is often hindered by the noise and missing density values in cryo-EM density maps caused by experimental conditions such as low contrast and conformational heterogeneity. Although various global and local map-sharpening techniques are widely employed to improve cryo-EM density maps, it is still challenging to efficiently improve their quality for building better protein structures from them.Results
In this study, we introduce CryoTEN-a 3D UNETR++ style transformer to improve cryo-EM maps effectively. CryoTEN is trained using a diverse set of 1295 cryo-EM maps as inputs and their correspo","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Mar","modification":"2026-06-01T10:11:30.962Z","creation":"2025-04-04T14:47:15.323Z"},"accession":"S-EPMC11906401","cross_references":{"pubmed":["40036588"],"doi":["10.1093/bioinformatics/btaf092"]}}