Turning the Pump Handle: Evolving Methods for Integrating the Evidence on Gene-Disease Association
Turning the Pump Handle: Evolving Methods for Integrating the Evidence on Gene-Disease Association is a research paper published in American Journal of Epidemiology (2007). On theSindex it has a DataRank of 1.3. It has been cited 27 times, with 12 citing works in its 1-hop citation network.
Abstract
Recent findings from genome-wide association studies have demonstrated their considerable potential for identify-ing genetic determinants of common diseases of public health significance such as cancer, heart disease, and diabetes (1), but they have also highlighted the continued importance of targeted genotyping to replicate genome-wide association findings (2).Approaches to the integration of evidence in human genome epidemiology have evolved rapidly in the last few years.The combination of results from
›Data sources & pipeline
FAIR Checklist
Context only (not used in score)- Has DOI
- Open Access
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
DataRank Breakdown
Base Score Contribution
0.500
From this paper's citation signal
Citation Network Contribution
0.819
From 9 citing papers with measurable signal
Top 5 citers driving the network score
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
- STrengthening the REporting of Genetic Association Studies (STREGA)—an extension of the STROBE statementGenetic Epidemiology2009339 citationsDataRank 0.874
- STrengthening the REporting of Genetic Association studies (STREGA) – an extension of the STROBE statementEuropean Journal of Clinical Investigation2009326 citationsDataRank 0.868
- Meta-Analysis in Genome-Wide Association StudiesPharmacogenomics2009282 citationsDataRank 0.847
- The Emergence of Translational Epidemiology: From Scientific Discovery to Population Health ImpactAmerican Journal of Epidemiology2010237 citationsDataRank 0.821
- Methods for meta-analysis in genetic association studies: a review of their potential and pitfallsHuman Genetics2007211 citationsDataRank 0.803
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 38% comes from its base citations and 62% from the citation network (9 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(Cq) ÷ max(outdegreeq, 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.
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