🏆 Finalist — NIH Data Sharing Index (“S-Index”) Challenge

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

Learning to Estimate Sample-specific Transcriptional Networks for 7000 Tumors

bioRxiv (Cold Spring Harbor Laboratory)(2023)10.1101/2023.12.01.569658Source: DataRank Database

Learning to Estimate Sample-specific Transcriptional Networks for 7000 Tumors is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2023). On theSindex it has a DataRank of 0.308. It has been cited 6 times, with 4 citing works in its 1-hop citation network.

N/A

0.308DataRank · unranked

Scored on demand from live citation data

Open Access6 citations · 4 citing works in its network

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 (2 grants)OA: greenTopics (10)IDs (PubMed)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)

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.

DataRank Breakdown

Base Score 95%Citation Network 5%

Base Score Contribution

0.292

From this paper's citation signal

Citation Network Contribution

0.0157

From 2 citing papers with measurable signal

Learn more about DataRank methodology →

Top 4 citers driving the network score

Ranked by each citer's contribution to N(p) — log1p(C_q) divided by its reference count — out of 4 citers.

Discovering Root Causal Genes with High Throughput Perturbations
bioRxiv (Cold Spring Harbor Laboratory)20241 citations71 referencesContributes 0.010
Discovering root causal genes with high-throughput perturbations
eLife20241 citations80 referencesContributes 0.009
Discovering Root Causal Genes with High Throughput Perturbations
eLife20250 citations82 referencesContributes 0.000
Discovering root causal genes with high-throughput perturbations
eLife20250 citations79 referencesContributes 0.000

Authors (9)

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

Live enrichment

PubMed:40408406

National Institutes of Health

Grant: R01GM140467

National Institutes of Health

Grant: 1R01GM140467-01

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

greenLicense: cc-by

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2023/12/04/2023.12.01.569658.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2023.12.01.569658

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

https://doi.org/10.1101/2023.12.01.569658

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

Fields of Study

0206 medical engineering02 engineering and technology

Keywords

Gene Expression Regulation, NeoplasticMachine LearningNeoplasmsPhysical SciencesMutationTumor MicroenvironmentHumansGene Regulatory Networks

Sustainable Development Goals

SDG 3: 3. Good health