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Learning to estimate sample-specific transcriptional networks for 7,000 tumors

Proceedings of the National Academy of Sciences(2025)10.1073/pnas.2411930122Source: DataRank Database

Learning to estimate sample-specific transcriptional networks for 7,000 tumors is a research paper published in Proceedings of the National Academy of Sciences (2025). On theSindex it has a DataRank of 0.

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0DataRank · unranked

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Open Access0 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (2 grants)Impact (FWCI 0.38)OA: hybridTopics (44)IDs (PubMed, OpenAlex)SDGs (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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Authors (11)

Benjamin J. Lengerich ORCID,Thomas B. K. Watkins,Jiekun Yang ORCID,Abhinav K Adduri,Sazan Mahbub ORCID

Live enrichment

PubMed:40408406 OpenAlex:https://openalex.org/W4410638674

HHS | National Institutes of Health

Grant: R01GM140467

National Institutes of Health

Grant: 1R01GM140467-01

Sample-specific Models for Molecular Portraits of Diseases in Precision Medicine

FWCI

0.38

Citation Percentile

0.6%

Citation Trend

2026

hybridLicense: cc-by

journalhttps://doi.org/10.1073/pnas.2411930122

publisherhttps://doi.org/10.1073/pnas.2411930122

publisherhttps://pnas.org/doi/pdf/10.1073/pnas.2411930122

repositoryhttps://pubmed.ncbi.nlm.nih.gov/40408406

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/12130817

Fields of Study

Gene expression and cancer classificationBioinformatics and Genomic NetworksCancer Genomics and Diagnostics0206 medical engineering02 engineering and technologyHumansGene Regulatory NetworksNeoplasmsTumor MicroenvironmentGene Expression Regulation, NeoplasticMutationMachine Learning

MeSH Terms

Machine LearningHumansMutationNeoplasmsGene Expression Regulation, NeoplasticGene Regulatory NetworksTumor Microenvironment

Keywords

Computer scienceGene regulatory networkContext (archaeology)InferenceGenerative modelMachine learningArtificial intelligenceComputational biologyBiologyGeneticsGeneGenerative grammarGene expressionGene Expression Regulation, NeoplasticNeoplasmsPhysical SciencesMutationTumor MicroenvironmentHumansGene Regulatory Networkscancerheterogeneitymultitask learningnetworkspersonalized models

Sustainable Development Goals

SDG 3: 3. Good health