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

BISON: bi-clustering of spatial omics data with feature selection

Bioinformatics(2025)10.1093/bioinformatics/btaf495Source: DataRank Database

BISON: bi-clustering of spatial omics data with feature selection is a research paper published in Bioinformatics (2025). On theSindex it has a DataRank of 0.

N/A

0DataRank · unranked

Scored on demand from live citation data

Open Access0 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 (6 grants)Impact (FWCI 0.00)OA: goldTopics (47)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)

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)

Alberto Cassese ORCID,Marina Vannucci ORCID,Michele Guindani ORCID,Qiwei Li ORCID,Bencong Zhu ORCID

Live enrichment

PubMed:40924538 PMC:PMC12463466 OpenAlex:https://openalex.org/W4414085049

National Science Foundation

Grant: # 1636933

National Science Foundation

Grant: # 1920920

National Institutes of Health

Grant: # 1R01GM141519

NIGMS NIH HHS

Grant: R01 GM141519

National Institutes of Health

Grant: 5R01GM141519-02

Developing novel algorithms for spatial molecular profiling technologies

National Science Foundation

Grant: 1920920

RII Track 2 FEC: Multi-Scale Integrative Approach to Digital Health: Collaborative Research and Education in Smart Health in West Virginia and Arkansas

FWCI

0.00

Citation Percentile

0.1%

goldLicense: cc-by

journalhttps://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaf495/64232068/btaf495.pdf

publisherhttps://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaf495/64232068/btaf495.pdf

publisherhttps://academic.oup.com/bioinformatics/article-pdf/41/9/btaf495/64232068/btaf495.pdf

journalhttps://doi.org/10.1093/bioinformatics/btaf495

repositoryhttps://pubmed.ncbi.nlm.nih.gov/40924538

Fields of Study

Single-cell and spatial transcriptomicsGene expression and cancer classificationFerroptosis and cancer prognosis0206 medical engineering02 engineering and technologySoftwareCluster AnalysisGene Expression ProfilingBayes TheoremHigh-Throughput Nucleotide SequencingGenomicsHumansAlgorithmsTranscriptomeComputational BiologyAnimals

MeSH Terms

AlgorithmsAnimalsBayes TheoremHumansSoftwareCluster AnalysisComputational BiologyGene Expression ProfilingGenomicsHigh-Throughput Nucleotide SequencingTranscriptome

Keywords

Feature selectionFeature (linguistics)Selection (genetic algorithm)Pattern recognition (psychology)SoftwareFOS: Computer and information sciencesOriginal PaperGene Expression ProfilingHigh-Throughput Nucleotide SequencingComputational BiologyDifferential gene expression analysis; high-dimensional count data; latent block model; Markov random field; spatially resolved transcriptomicsBayes TheoremGenomicsStatistics - ApplicationsCluster AnalysisHumansAnimalsApplications (stat.AP)TranscriptomeAlgorithms