Demo corpus. Scores are computed on a select set of biomedical paper/datasets and may be inaccurate for papers outside this corpus — DataRank relies on network effects that improve with scale. We aim to expand this into a fully open resource pending additional funding.

GWAS summary-based pathway analysis correcting for the genetic confounding impact of environmental exposures

Briefings in Bioinformatics(2018)10.1093/bib/bbx025Source: DataRank Database

GWAS summary-based pathway analysis correcting for the genetic confounding impact of environmental exposures is a research paper published in Briefings in Bioinformatics (2018). On theSindex it has a DataRank of 0.291. It has been cited 4 times, with 4 citing works in its 1-hop citation network.

N/A

0.291DataRank · unranked

0.291

4 citations · base score 1.6

Cite:

datarank_citation_only_1hop_v6· scope data_onlyMethodology

›Data sources & pipeline

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Pending

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (0/2)

Interoperable (0/2)

Reusable (0/3)

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

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DataRank Breakdown

Base Score 83%Citation Network 17%

Base Score Contribution

0.241

From this paper's citation signal

Citation Network Contribution

0.0500

From 3 citing papers with measurable signal

Learn more about DataRank methodology →

Top 3 citers driving the network score

Ranked by citation count — the same ordering the engine uses when summing log1p(C_q) over citers.

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles
Proceedings of the National Academy of Sciences200555,906 citationsDataRank 1.6
A global reference for human genetic variation
Nature201519,823 citationsDataRank 11.1Top 19%
Gene and pathway-based second-wave analysis of genome-wide association studies
European Journal of Human Genetics2010240 citationsDataRank 10.9

Why this DataRank?

DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 83% comes from its base citations and 17% from the citation network (3 citing papers contributed measurable signal).

Base score B(p): log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
Network N(p): Σ over citers of log1p(C_q) ÷ max(outdegree_q, 1). Being cited by a highly-cited paper with few references counts most.
Damping factor d = 0.85: DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
Self-citations excluded: Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.

Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.

Read the full methodology →

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Node colors:CenterData PaperData + Open AccessNon-dataSelected & links| Node size = percentile rank