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scDILT: A Model-Based and Constrained Deep Learning Framework for Single-Cell Data Integration, Label Transferring, and Clustering

IEEE Transactions on Computational Biology and Bioinformatics(2025)10.1109/tcbbio.2025.3553068Source: DataRank Database

scDILT: A Model-Based and Constrained Deep Learning Framework for Single-Cell Data Integration, Label Transferring, and Clustering is a research paper published in IEEE Transactions on Computational Biology and Bioinformatics (2025). On theSindex it has a DataRank of 0.

N/A

0DataRank · unranked

Scored on demand from live citation data

Open Access0 citations

Cite:

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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 0.00)OA: greenTopics (9)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)

Jianlan Ren,Le Gao ORCID,Junwen Wang ORCID,Zhi Wei ORCID,Xiang Lin ORCID

Live enrichment

PubMed:40811359 PMC:PMC12421913 OpenAlex:https://openalex.org/W4408565368

National Institutes of Health

Grant: R15HG012087

National Institutes of Health

Grant: P01CA281867

National Institutes of Health

Grant: UM1TR004789

FWCI

0.00

Citation Percentile

0.0%

greenLicense: https://doi.org/10.15223/policy-029

repositoryhttps://pubmed.ncbi.nlm.nih.gov/40811359/

repositoryhttps://pubmed.ncbi.nlm.nih.gov/40811359/

publisherhttp://xplorestaging.ieee.org/ielx8/10723156/11025532/10932718.pdf?arnumber=10932718

journalhttps://doi.org/10.1109/tcbbio.2025.3553068

repositoryhttps://pubmed.ncbi.nlm.nih.gov/40811359

Fields of Study

Cell Image Analysis TechniquesGene Regulatory Network AnalysisSingle-cell and spatial transcriptomics

Keywords

Cluster analysisComputer scienceArtificial intelligenceData integrationMachine learningData mining