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Rapid Synthesis of Cryo-ET Data for Training Deep Learning Models

bioRxiv (Cold Spring Harbor Laboratory)(2023)10.1101/2023.04.28.538636Source: DataRank Database

Rapid Synthesis of Cryo-ET Data for Training Deep Learning Models is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2023). On theSindex it has a DataRank of 0. It has been cited 33 times.

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Open Access33 citations

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Enrichment:Funding (4 grants)OA: greenTopics (16)IDs (PubMed, OpenAlex)SDGs (1)

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Authors (10)

Jessica Heebner,Michael T. Swulius,Ryan Hylton ORCID,Seth Kabonick,Michael A. Grillo ORCID

Live enrichment

PubMed:37162972 OpenAlex:https://openalex.org/W4367305598

National Science Foundation

Grant: 1817929

Mechanisms of Interleaflet Coupling in Asymmetric Lipid Membranes

National Institutes of Health

Grant: 5R01GM138887-02

Coupling of lateral and transverse organization in complex biomembranes

National Science Foundation

Grant: 1911940

The four dimensions of nucleosome chain folding

National Institutes of Health

Grant: 5R01NS126448-02

Using cryo-electron tomography and live-cell fluorescent imaging to study the role of cofilin in regulating neuronal filopodial structure and dynamics

greenLicense: cc-by-nc-nd

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2023/04/28/2023.04.28.538636.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2023.04.28.538636

repositoryhttps://doi.org/10.1101/2023.04.28.538636

repositoryhttps://pubmed.ncbi.nlm.nih.gov/37162972

Fields of Study

Advanced Electron Microscopy Techniques and ApplicationsAdvanced X-ray Imaging TechniquesElectron and X-Ray Spectroscopy Techniques0202 electrical engineering, electronic engineering, information engineering02 engineering and technology

Keywords

Computer scienceArtificial intelligenceSegmentationDeep learningGround truthRange (aeronautics)Pattern recognition (psychology)Computer visionMachine learningMaterials scienceArticle

Sustainable Development Goals

SDG 0: Sustainable cities and communities