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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Comparing protein abundance and mRNA expression levels on a genomic scale.
PMID 12952525 · PMC193646 · Genome biology · 2003 · 8 claims · 8 setups
Correlations between mRNA expression and protein abundance are generally poor or limited across most studies reviewed, including in yeast and human cancers
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Filtering high-throughput protein-protein interaction data using a combination of genomic features.
PMID 15833142 · PMC1127019 · BMC bioinformatics · 2005 · 8 claims · 8 setups
A combination of three genomic features (interacting Pfam domains, GO annotations, sequence homology) using naive Bayesian networks predicts true protein-protein interactions with high sensitivity and good specificity.
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Genome-wide prioritization of disease genes and identification of disease-disease associations from an integrated human functional linkage network.
PMID 19728866 · PMC2768980 · Genome biology · 2009 · 6 claims · 6 setups
Integrating 16 genomic features (32 sub-features) via a naïve Bayes classifier produces a genome-scale FLN of 21,657 human genes and 22,388,609 weighted links that outperforms any individual data source for inferring functional linkages.
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Nucleosome deposition and DNA methylation at coding region boundaries.
PMID 19723310 · PMC2768978 · Genome biology · 2009 · 8 claims · 8 setups
Nucleosomes and DNA methylation form distinct peaks just downstream of the start codon and just upstream of the stop codon, marking both ends of protein coding units genome-wide.
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Prioritization of candidate cancer genes--an aid to oncogenomic studies.
PMID 18710882 · PMC2566894 · Nucleic acids research · 2008 · 8 claims · 8 setups
Computational classifiers using combinations of protein conservation, gene structure, protein domains, protein interactions, and regulatory data can distinguish known cancer genes (CD/CR) from unlabelled human genes