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Transformer and graph variational autoencoder to identify microenvironments: A deep learning protocol for spatial transcriptomics

STAR Protocols(2025)10.1016/j.xpro.2025.104206Source: DataRank Database

Transformer and graph variational autoencoder to identify microenvironments: A deep learning protocol for spatial transcriptomics is a research paper published in STAR Protocols (2025). On theSindex it has a DataRank of 0.104. It has been cited 1 time.

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0.104DataRank · 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 (6 grants)Impact (FWCI 0.40)OA: goldTopics (26)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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DataRank Breakdown

Base Score 100%Citation Network 0%

Base Score Contribution

0.104

From this paper's citation signal

Citation Network Contribution

Citation network not refreshed for this result

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Authors (6)

Yufei Huang ORCID,Shou‐Jiang Gao ORCID,Yidong Chen ORCID,Yu-Fang Jin,Mario Flores ORCID

Live enrichment

PubMed:41313684 PMC:PMC12702057 OpenAlex:https://openalex.org/W4416827215

National Institutes of Health

Grant: 3R01CA124332-13A1S1

National Institutes of Health

Grant: P30 CA054174

National Science Foundation

Grant: 2051113

Cancer Prevention and Research Institute of Texas

Grant: RP220662

Cancer Prevention and Research Institute of Texas

Grant: R01 CA284554

NCI NIH HHS

Grant: R01 CA124332

FWCI

0.40

Citation Percentile

0.6%

Citation Trend

2026

goldLicense: cc-by

journalhttps://doi.org/10.1016/j.xpro.2025.104206

publisherhttps://doi.org/10.1016/j.xpro.2025.104206

publisherhttps://api.elsevier.com/content/article/PII:S2666166725006124?httpAccept=text/xml

publisherhttps://api.elsevier.com/content/article/PII:S2666166725006124?httpAccept=text/plain

repositoryhttps://pubmed.ncbi.nlm.nih.gov/41313684

Fields of Study

Single-cell and spatial transcriptomicsGene expression and cancer classificationCell Image Analysis Techniques

MeSH Terms

Deep LearningAutoencoderAnimalsHumansImage Processing, Computer-AssistedSoftwareComputational BiologyGene Expression ProfilingTumor MicroenvironmentTranscriptome

Keywords

AutoencoderDeep learningCluster analysisTransformerFeature learningGraphPattern recognition (psychology)CancerSequence analysisSingle cellBioinformaticsGenomicsGene Expression