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MUSE: A Multi-slice Joint Analysis Method for Spatial Transcriptomics Experiments

PubMed(2025)10.1145/3746252.3761240Source: DataRank Database

MUSE: A Multi-slice Joint Analysis Method for Spatial Transcriptomics Experiments is a research paper published in PubMed (2025). On theSindex it has a DataRank of 0. It has been cited 1 time.

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0DataRank · unranked

Scored on demand from live citation data

Open Access1 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (3 grants)Impact (FWCI 11.26)OA: goldTopics (18)IDs (PubMed, PMC, OpenAlex)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (5)

Xi Li ORCID,Zhiqing Xiao ORCID,Rex Ying ORCID,Jing Zhang ORCID,Ziheng Duan ORCID

Live enrichment

PubMed:41527556 PMC:PMC12790625 OpenAlex:https://openalex.org/W4416017067

National Institutes of Health

Grant: 5R01DA063316, 5R01HG012572

NHGRI NIH HHS

Grant: R01 HG012572

NIDA NIH HHS

Grant: R01 DA063316

FWCI

11.26

Citation Percentile

1.0%

Citation Trend

2026

goldLicense: cc-by

https://doi.org/10.1145/3746252.3761240

publisherhttps://dl.acm.org/doi/pdf/10.1145/3746252.3761240

repositoryhttps://pubmed.ncbi.nlm.nih.gov/41527556

repositoryhttps://pmc.ncbi.nlm.nih.gov/articles/PMC12790625/

Fields of Study

Single-cell and spatial transcriptomicsGene expression and cancer classificationBioinformatics and Genomic Networks

Keywords

Leverage (statistics)Spatial analysisData integrationDomain (mathematical analysis)GraphSoftwareDiscriminative modelExtensibilityOptimal TransportSpatial TranscriptomicsGene Expression ImputationApplied Computing → Computational GenomicsSpatial Domain IdentificationComputing Methodologies → Machine Learning ApproachesMulti-Slice Joint Analysis