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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Identification of deleterious non-synonymous single nucleotide polymorphisms using sequence-derived information.
PMID 18588693 · PMC2446391 · BMC bioinformatics · 2008 · 8 claims · 5 setups
A decision tree built on 10 selected sequence-derived features classifies SAPs as Disease or Polymorphism with 82.6% accuracy and 0.607 MCC in cross-validation.
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Predicting deleterious nsSNPs: an analysis of sequence and structural attributes.
PMID 16630345 · PMC1489951 · BMC bioinformatics · 2006 · 8 claims · 7 setups
Sequence conservation (PSIC score difference) at the nsSNP position is the single most useful attribute for predicting deleterious vs neutral status.
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Has reproduction · 48
Comparative analysis of molecular signatures reveals a hybrid approach in breast cancer: Combining the Nottingham Prognostic Index with gene expressions into a hybrid signature.
PMID 35143511 · PMC8830616 · PloS one · 2022 · 8 claims · 6 setups
A hybrid signature combining the Nottingham Prognostic Index with SIS-selected gene expressions can be built in a data-driven fashion (NPI treated as a gene expression during feature selection).
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Full-text index only
In silico analysis of missense substitutions using sequence-alignment based methods.
PMID 18951440 · PMC3431198 · Human mutation · 2008 · 8 claims · 7 setups
Carefully validated PMSA-based computational algorithms can achieve predictive values of ~75-95% for classifying missense substitutions as pathogenic or neutral.
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Has reproduction · 53
Combining evidence of preferential gene-tissue relationships from multiple sources.
PMID 23950964 · PMC3741196 · PloS one · 2013 · 8 claims · 8 setups
A high-level integration approach combining three methods across four human microarray datasets, merged by consensus voting and a rule-based inner/total score, predicts preferentially expressed genes while reducing method- and study-specific bias.