Pilot corpus: NIH-funded biomedical datasets. Any DOI can be scored on demand — network effects sharpen as coverage grows.

MultiSC: a deep learning pipeline for analyzing multiomics single-cell data

Briefings in Bioinformatics(2024)10.1093/bib/bbae492Source: DataRank Database

MultiSC: a deep learning pipeline for analyzing multiomics single-cell data is a research paper published in Briefings in Bioinformatics (2024). On theSindex it has a DataRank of 0. It has been cited 5 times.

N/A

0DataRank · unranked

Scored on demand from live citation data

Open Access5 citations

Download PDF

Cite:

DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?

›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (12 grants)OA: goldTopics (25)IDs (PubMed, PMC)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

FAIR checklist signals are shown for context only and do not affect DataRank scoring.

Run a calibrated FAIR evaluation for this paper →

We only score data papers we can read in full — never from an abstract alone.

Authors (5)

Siqi Jiang ORCID,Le Gao ORCID,Zhi Wei ORCID,Junwen Wang ORCID,Xiang Lin ORCID

Live enrichment

PubMed:39376034 PMC:PMC11458747

RGC of Hong Kong

Grant: C7015-23G

University of Hong Kong

Grant: 2207101590

NIH of USA

Grant: R01LM013438

National Science Foundation

Grant: ACI-1548562

National Institutes of Health

Grant: UM1TR004789

National Institutes of Health

Grant: P01CA281867

National Institutes of Health

Grant: R15HG012087

National Institutes of Health

Grant: 5R01LM013438-03

Discover and Analyze Germline-Somatic Interactions in Cancer

National Institutes of Health

Grant: 1UM1TR004789-01

NJ ACTS: A Platform for Translational Science in New Jersey

National Institutes of Health

Grant: 2R15HG012087-02

Deep Learning Methods to Integrate Biological Information for Analysis of Single-cell RNAseq Data

Mayo Clinic

Mayo Clinic Comprehensive Cancer Center

goldLicense: cc-by-nc

publisherhttps://doi.org/10.1093/bib/bbae492

publisherhttps://academic.oup.com/bib/article-pdf/25/6/bbae492/59619957/bbae492.pdf

repositoryhttps://pmc.ncbi.nlm.nih.gov/articles/PMC11458747/pdf/bbae492.pdf

Europe_PMChttps://europepmc.org/articles/PMC11458747

Europe_PMChttps://europepmc.org/articles/PMC11458747?pdf=render

Fields of Study

0206 medical engineering02 engineering and technology

MeSH Terms

HumansTranscription FactorsComputational BiologyGene Regulatory NetworksSingle-Cell AnalysisDeep LearningMultiomics

Keywords

Causal InferencePathway analysisMediation AnalysisGene Regulatory NetworkMulti-omicsScrna-seqScatac-seqCell Type AnnotationDeep LearningProblem Solving ProtocolHumansComputational BiologyGene Regulatory NetworksSingle-Cell AnalysisMultiomicsTranscription Factors