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Localization of macromolecules in crowded cellular cryo-electron tomograms from extremely sparse labels

Briefings in Bioinformatics(2025)10.1093/bib/bbaf630Source: DataRank Database

Localization of macromolecules in crowded cellular cryo-electron tomograms from extremely sparse labels is a research paper published in Briefings in Bioinformatics (2025). On theSindex it has a DataRank of 0.

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Enrichment:Funding (16 grants)OA: hybridTopics (21)IDs (PubMed, PMC)Annotations (1)

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Authors (7)

Ajmain Yasar Ahmed ORCID,H. M. Shadman Tabib,Md Toki Tahmid ORCID,Md. Zarif Ul Alam,Zachary Freyberg ORCID

Live enrichment

PubMed:41313604 PMC:PMC12661946

National Institutes of Health

Grant: R35GM158094

National Institutes of Health

Grant: R01GM134020

National Institutes of Health

Grant: P41GM103712

National Science Foundation

Grant: DBI-2238093

National Science Foundation

Grant: DBI-2422619

National Science Foundation

Grant: DBI-1949629

National Science Foundation

Grant: IIS-2007595

National Science Foundation

Grant: IIS-2211597

National Science Foundation

Grant: MCB-2205148

National Science Foundation

Grant: 1949629

IIBR Informatics: Reducing the training data annotation cost for learning-based macromolecule identification in cellular electron cryo-tomography

National Institutes of Health

Grant: 1R01GM134020-01A1

Novel machine learning approaches for improving structural discrimination in cryo-electron tomography

National Institutes of Health

Grant: 5P41GM103712-08

High Performance Computing for Multiscale Modeling of Biological Systems

National Science Foundation

Grant: 2205148

Tools4Cells: Machine-learning aided morphodynamics characterization of stem cell differentiation using label-free microscopies

National Science Foundation

Grant: 2211597

Collaborative Research: III: Medium: Systematic De Novo Identification of Macromolecular Complexes in Cryo-Electron Tomography Images

National Science Foundation

Grant: 2007595

III: Small: Improving automation and speed of macromolecule recognition and localization in cryo-electron tomography using unsupervised deep learning

National Science Foundation

Grant: 2238093

CAREER: Cryo-electron tomography derived multiscale integrative modeling of subcellular organization

hybridLicense: CC BY NC

publisherhttps://academic.oup.com/bib/article-pdf/26/6/bbaf630/65613675/bbaf630.pdf

Europe_PMChttps://europepmc.org/articles/PMC12661946

Europe_PMChttps://europepmc.org/articles/PMC12661946?pdf=render

https://doi.org/10.1093/bib/bbaf630

https://doi.org/10.1101/2024.11.04.620735

Fields of Study

Cryoelectron MicroscopyElectron Microscope TomographyMacromolecular SubstancesImage Processing, Computer-AssistedAlgorithms

MeSH Terms

Macromolecular SubstancesCryoelectron MicroscopyAlgorithmsImage Processing, Computer-AssistedElectron Microscope Tomography

Keywords

Cryo-electron Tomography3D ClassificationCell And Structural BiologyMacromolecule LocalizationPositive-unlabeled LearningElectron Microscope TomographyMacromolecular SubstancesCryoelectron MicroscopyImage Processing, Computer-AssistedProblem Solving ProtocolAlgorithms

Localization of macromolecules in crowded cellular cryo-electron tomograms from extremely sparse labels

FAIR Checklist

Authors (7)

Funding (16 grants)

Open Access

Topics

Annotations (Europe PMC)