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Overcoming the preferred orientation problem in cryoEM with self-supervised deep-learning

bioRxiv (Cold Spring Harbor Laboratory)(2024)10.1101/2024.04.11.588921Source: DataRank Database

Overcoming the preferred orientation problem in cryoEM with self-supervised deep-learning is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2024). On theSindex it has a DataRank of 0. It has been cited 14 times.

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Open Access14 citations

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Enrichment:Funding (1 grants)OA: greenTopics (26)IDs (PubMed, OpenAlex)

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Authors (4)

Hongcheng Fan ORCID,Jason J. Hu ORCID,Z. Hong Zhou ORCID,Yun-Tao Liu ORCID

Live enrichment

PubMed:38645074 OpenAlex:https://openalex.org/W4394797326

National Institutes of Health

Grant: 3R01GM071940-16S1

High-Resolution CryoEM Reconstruction of Large Complexes

greenLicense: cc-by-nc-nd

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2024/04/14/2024.04.11.588921.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2024.04.11.588921

repositoryhttps://doi.org/10.1101/2024.04.11.588921

repositoryhttps://pubmed.ncbi.nlm.nih.gov/38645074

Fields of Study

Advanced Electron Microscopy Techniques and ApplicationsElectron and X-Ray Spectroscopy TechniquesIon-surface interactions and analysis0202 electrical engineering, electronic engineering, information engineering02 engineering and technology

Keywords

Orientation (vector space)IsotropyArtificial intelligenceParticle (ecology)Computer scienceComputer visionGeometryMathematicsPhysicsOpticsBiologyDeep LearningImaging, Three-DimensionalMacromolecular SubstancesCryoelectron MicroscopyImage Processing, Computer-AssistedAnisotropyRibosomesArticleSoftwareAlgorithms