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).

• Integrated analysis of genetic and proteomic data identifies biomarkers associated with adverse events following smallpox vaccination.

  PMID 18923431 · PMC2692715 · Genes and immunity · 2009 · 7 claims · 6 setups

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  A two-stage strategy (Random Forest filtering followed by decision tree modeling) can integrate categorical genetic and continuous proteomic data to identify biomarkers of AE risk

  ebv-transformed serum combined rf-filtered snp serum random

• Identification of deleterious non-synonymous single nucleotide polymorphisms using sequence-derived information.

  PMID 18588693 · PMC2446391 · BMC bioinformatics · 2008 · 8 claims · 5 setups

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  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.

  sap sequence-derived humvar same decision independent greedy

• Randomised trial of a decision aid and its timing for women being tested for a BRCA1/2 mutation.

  PMID 14735173 · PMC2410151 · British journal of cancer · 2004 · 6 claims · 3 setups

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  The DA had no impact on well-being (anxiety, depression, cancer-related distress, general health)

  women brca1 same randomised comparative validated

• Application of serum SELDI proteomic patterns in diagnosis of lung cancer.

  PMID 16029516 · PMC1183195 · BMC cancer · 2005 · 6 claims · 2 setups

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  A five-protein-peak SELDI decision-tree pattern (11493, 6429, 8245, 5335, 2538 Da) distinguishes lung cancer sera from healthy control sera with relatively high sensitivity and specificity

  serum seldi-tof-ms electrochemiluminescent

• Speeding disease gene discovery by sequence based candidate prioritization.

  PMID 15766383 · PMC1274252 · BMC bioinformatics · 2005 · 7 claims · 8 setups

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  Disease genes (OMIM) differ significantly from non-disease genes in sequence-based features including gene/cDNA/protein size, exon number, homolog conservation, secretion signal, 3' UTR length, CpG islands, and distance to nearest gene.

  ensembl training same hgmd comparative machine tenfold

• Machine-learning approaches for classifying haplogroup from Y chromosome STR data.

  PMID 18551166 · PMC2396484 · PLoS computational biology · 2008 · 8 claims · 5 setups

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  Y-STR allelic variability is partitioned more by differences among haplogroups than by differences among populations, suggesting Y-STRs carry haplogroup information

  globally same computational publicly y-str snp supervised

• Application of machine learning in SNP discovery.

  PMID 16398931 · PMC1955739 · BMC bioinformatics · 2006 · 8 claims · 6 setups

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  PolyBayes produces high false-positive SNP predictions even with stringent parameters

  diverse soybean candidate same sts polybayes polyphred

• Putting science over supposition in the arena of personalized genomics.

  PMID 18665132 · PMC2531214 · Nature genetics · 2008 · 6 claims · 3 setups

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  There is a rapidly widening gap between gene-disease association discovery and research into the public health/clinical utility of that information.

  adults same tested baseline multiplex telephone-based

• 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