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Automated, high-throughput quantification of EGFP-expressing neutrophils in zebrafish by machine learning and a highly-parallelized microscope

bioRxiv (Cold Spring Harbor Laboratory)(2023)10.1101/2023.08.16.553550Source: DataRank Database

Automated, high-throughput quantification of EGFP-expressing neutrophils in zebrafish by machine learning and a highly-parallelized microscope is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2023). On theSindex it has a DataRank of 0.

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (2 grants)OA: greenTopics (20)IDs (PubMed, OpenAlex)

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Open Access

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Reusable (0/3)

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Authors (11)

Giuliano Ferrero ORCID,Aurélien Bègue ORCID,Thomas Jedidiah Jenks Doman ORCID,Clay Dugo ORCID,Andi Barker

Live enrichment

PubMed:37645798 OpenAlex:https://openalex.org/W4385971380

NIEHS NIH HHS

Grant: P42 ES031009

NIH HHS

Grant: R44 OD024879

greenLicense: cc-by

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2023/08/18/2023.08.16.553550.full.pdf

GREENhttps://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0295711&type=printable

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2023/08/18/2023.08.16.553550.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2023.08.16.553550

repositoryhttp://dx.doi.org/10.1101/2023.08.16.553550

Fields of Study

Digital Imaging for Blood DiseasesCell Image Analysis TechniquesImmune Response and InflammationBiologyMedicineComputer Science

Keywords

ThroughputZebrafishMicroscopeGreen fluorescent proteinCell biologyComputer scienceComputational biologyChemistryBiologyBiochemistryGenePathologyMedicineOperating system