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SHADE: A Multilevel Bayesian Approach to Modeling Directional Spatial Associations in Tissues

bioRxiv (Cold Spring Harbor Laboratory)(2025)10.1101/2025.06.24.661393Source: DataRank Database

SHADE: A Multilevel Bayesian Approach to Modeling Directional Spatial Associations in Tissues is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2025). On theSindex it has a DataRank of 0.

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (4 grants)OA: greenTopics (13)IDs (PubMed, OpenAlex)

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Open Access

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Authors (3)

Michele Peruzzi ORCID,Arvind Rao ORCID,Joel Eliason ORCID

Live enrichment

PubMed:40667165 OpenAlex:https://openalex.org/W4411721333

NCI NIH HHS

Grant: P30 CA046592

NCI NIH HHS

Grant: R01 CA268426

NCI NIH HHS

Grant: R37 CA214955

NCI NIH HHS

Grant: T32 CA140044

greenLicense: cc-by

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2025/06/27/2025.06.24.661393.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2025.06.24.661393

repositoryhttps://doi.org/10.1101/2025.06.24.661393

repositoryhttps://pubmed.ncbi.nlm.nih.gov/40667165

Fields of Study

Single-cell and spatial transcriptomicsMathematical Biology Tumor GrowthRadiomics and Machine Learning in Medical Imaging

Keywords

Bayesian probabilityMultilevel modelBayesian hierarchical modelingComputer scienceBayesian inferenceGeographyEconometricsArtificial intelligenceMathematicsMachine learning