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).

• Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes.

  PMID 16757574 · PMC1475747 · Nucleic acids research · 2006 · 6 claims · 8 setups

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  Applying seven independent computational disease-gene prioritization methods in concert to 9556 positional candidate genes identifies a prioritized set of likely T2D and obesity candidate genes

  pubmed genes same randomly geneseeker evoc disease

• ADaCGH: A parallelized web-based application and R package for the analysis of aCGH data.

  PMID 17710137 · PMC1940324 · PloS one · 2007 · 8 claims · 4 setups

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  ADaCGH implements eight CNA detection methods, including the best-performing ones from recent reviews (CBS, GLAD, CGHseg, HMM)

  30-node web-based cluster simulated computational web comparison

• BioGPS: an extensible and customizable portal for querying and organizing gene annotation resources.

  PMID 19919682 · PMC3091323 · Genome biology · 2009 · 8 claims · 4 setups

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  BioGPS aggregates distributed, third-party gene annotation resources into a single customizable portal for human, mouse, and rat genes.

  biogps diverse tissues specified web reference exon

• MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features.

  PMID 17553836 · PMC1933124 · Nucleic acids research · 2007 · 8 claims · 8 setups

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  A hybrid feature combining local contiguous triplet structure-sequence composition, MFE of the secondary structure, and P-value of a randomization test improves classification of real vs pseudo pre-miRNAs

  real combined pre-mirna training random independent repeated

• Local combinational variables: an approach used in DNA-binding helix-turn-helix motif prediction with sequence information.

  PMID 19651875 · PMC2761287 · Nucleic acids research · 2009 · 8 claims · 7 setups

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  The LCV approach predicts HTH motifs with 93.29% accuracy, 93.93% sensitivity and 92.66% specificity using only primary sequence information

  quintessentialset-88 hth same newly lcs svm traditional