Experiments
Searchable full-text extractions: founding hypothesis, core claims, experimental setups, key results and statistics — pulled out of each paper as structure. Search a cell line, an assay or an entity (e.g. HUH7) and find every paper that worked with it. This corpus stands on its own: most entries carry no reproduction assessment (yet).
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Utah's Family High Risk Program: bridging the gap between genomics and public health.
PMID 15888235 · PMC1327718 · Preventing chronic disease · 2005 · 8 claims · 6 setups
Collection of family history through the Family High Risk Program (FHRP) is a cost-effective method for identifying and intervening with high-risk populations for chronic disease
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Has reproduction · 83
Gene-expression patterns in peripheral blood classify familial breast cancer susceptibility.
PMID 26538066 · PMC4634735 · BMC medical genomics · 2015 · 8 claims · 5 setups
A multigene peripheral-blood gene-expression biomarker accurately classifies which women from high-risk families develop familial breast cancer.
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Has reproduction · 67
Integrative analyses reveal signaling pathways underlying familial breast cancer susceptibility.
PMID 26969729 · PMC4812528 · Molecular systems biology · 2016 · 7 claims · 6 setups
Cell adhesion (cell-cell and cell-ECM) pathways are significantly and consistently dysregulated in women who develop familial breast cancer across multiple omic data types and tissues.
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Genomics and the prevention and control of common chronic diseases: emerging priorities for public health action.
PMID 15888216 · PMC1327699 · Preventing chronic disease · 2005 · 8 claims · 6 setups
Family history is the most consistent risk factor for almost all human diseases across the lifespan.
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PedGenie: an analysis approach for genetic association testing in extended pedigrees and genealogies of arbitrary size.
PMID 16620382 · PMC1459209 · BMC bioinformatics · 2006 · 7 claims · 3 setups
PedGenie is a valid, flexible statistical tool for genetic association analysis in pedigrees of arbitrary size and structure using Monte Carlo significance testing