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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Computer identification of snoRNA genes using a Mammalian Orthologous Intron Database.
PMID 16093549 · PMC1184218 · Nucleic acids research · 2005 · 8 claims · 5 setups
Created the Mammalian Orthologous Intron Database (MOID) containing orthologous introns of human, mouse and rat identified via conserved reading-frame position
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Canine tumor cross-species genomics uncovers targets linked to osteosarcoma progression.
PMID 20028558 · PMC2803201 · BMC genomics · 2009 · 8 claims · 7 setups
High expression of IL-8 and SLC1A3, identified via cross-species mining, is associated with poor outcome in an independent population of human osteosarcoma patients
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Has reproduction · 66
RNAseq analysis of the parasitic nematode Strongyloides stercoralis reveals divergent regulation of canonical dauer pathways.
PMID 23145190 · PMC3493385 · PLoS neglected tropical diseases · 2012 · 8 claims · 8 setups
S. stercoralis possesses homologs of nearly all C. elegans dauer genes, but with significant differences in protein structure, developmental regulation, and gene family expansion.
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Analysis of expressed sequence tags from Actinidia: applications of a cross species EST database for gene discovery in the areas of flavor, health, color and ripening.
PMID 18655731 · PMC2515324 · BMC genomics · 2008 · 7 claims · 6 setups
A collection of 132,577 ESTs from four Actinidia species was generated and clustered into 41,858 non-redundant clusters (18,070 TCs and 23,788 singletons)
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Has reproduction · 96
Scalable Prediction of Acute Myeloid Leukemia Using High-Dimensional Machine Learning and Blood Transcriptomics.
PMID 31918046 · PMC6992905 · iScience · 2020 · 8 claims · 8 setups
Data-driven, high-dimensional ML approaches that learn multivariate signatures directly from genome-wide transcriptomic data (no prior gene selection) yield accurate and robust AML classifiers.