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A probabilistic modeling framework for genomic networks incorporating sample heterogeneity

Cell Reports Methods(2025)10.1016/j.crmeth.2025.100984Source: DataRank Database

A probabilistic modeling framework for genomic networks incorporating sample heterogeneity is a research paper published in Cell Reports Methods (2025). On theSindex it has a DataRank of 0.269. It has been cited 5 times.

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0.269DataRank · unranked

Scored on demand from live citation data

Open Access5 citations

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (15 grants)Impact (FWCI 1.92)OA: goldTopics (43)IDs (PubMed, PMC, OpenAlex)Annotations (1)

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

0.269

From this paper's citation signal

Citation Network Contribution

Citation network not refreshed for this result

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Authors (5)

Satwik Acharyya ORCID,Chunyu Luo ORCID,Yang Ni ORCID,Veerabhadran Baladandayuthapani ORCID,Liying Chen ORCID

Live enrichment

PubMed:39954675 PMC:PMC11955270 OpenAlex:https://openalex.org/W4407564549

Rogel Cancer Center, University of Michigan

Grant: DMS-2112943

Rogel Cancer Center, University of Michigan

Grant: 1R01GM148974-01

Bayesian Differential Causal Network and Clustering Methods for Single-Cell Data

National Institutes of Health National Cancer Institute

Grant: R01CA244845-01A1

National Institutes of Health National Cancer Institute

Grant: P30 CA46592

National Institutes of Health National Cancer Institute

Grant: R01-CA160736

National Science Foundation

Grant: 1463233

Collaborative Research: New Bayesian Nonparametric Paradigms of Personalized Medicine for Lung Cancer

NCI NIH HHS

Grant: P30 CA046592

NCI NIH HHS

Grant: R01 CA160736

NCI NIH HHS

Grant: R01 CA244845

National Institutes of Health

Grant: 1R01CA160736-01

Integrative methods for high-dimensional genomics data

National Institutes of Health

Grant: 5R01CA244845-02

Bayesian Network-Based Integrative Genomics Methods for Precision Medicine

National Institutes of Health

Grant: 3P30CA046592-31S7

Cancer Center Support Grant 2018-2023

National Science Foundation

Grant: 2112943

Automated Causal Discovery with Observational Data via Directed Graphical Models - New Theory and Methods

National Institutes of Health

University of Maryland School of Public Health

FWCI

1.92

Citation Percentile

0.8%

Citation Trend

2025

2026

goldLicense: cc-by

journalhttps://doi.org/10.1016/j.crmeth.2025.100984

publisherhttps://doi.org/10.1016/j.crmeth.2025.100984

publisherhttps://api.elsevier.com/content/article/PII:S2667237525000207?httpAccept=text/xml

publisherhttps://api.elsevier.com/content/article/PII:S2667237525000207?httpAccept=text/plain

repositoryhttps://pubmed.ncbi.nlm.nih.gov/39954675

Fields of Study

Gene expression and cancer classificationBioinformatics and Genomic NetworksGene Regulatory Network Analysis0206 medical engineering02 engineering and technologyModels, StatisticalHumansGene Regulatory NetworksGenomicsBayes TheoremAlgorithms

MeSH Terms

AlgorithmsBayes TheoremHumansSoftwareModels, StatisticalComputational BiologyGene Expression ProfilingGenomicsGene Regulatory NetworksTranscriptome

Keywords

Probabilistic logicSample (material)Computer scienceComputational biologyData scienceArtificial intelligenceBiologyGenomicsVariable selectionGene Regulatory NetworksVariational BayesNetwork ModelingSpatial TranscriptomicsCp: Systems BiologySpatial Graphical ModelsGraphical RegressionHeterogeneous Graphical ModelsModels, StatisticalHumansBayes TheoremArticleAlgorithms

A probabilistic modeling framework for genomic networks incorporating sample heterogeneity

FAIR Checklist

DataRank Breakdown

Authors (5)

Funding (15 grants)

Impact

Open Access

Topics

Annotations (Europe PMC)