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

• Decision forest analysis of 61 single nucleotide polymorphisms in a case-control study of esophageal cancer; a novel method.

  PMID 16026601 · PMC1637030 · BMC bioinformatics · 2005 · 8 claims · 2 setups

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  DF-SNPs, a novel adaptation of the Decision Forest method, can classify esophageal cancer cases vs. controls based on SNP genotype data with high concordance, sensitivity, and specificity.

  genomic snp high-throughput decision

• 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

• 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

• A comparison of classification methods for predicting Chronic Fatigue Syndrome based on genetic data.

  PMID 19772600 · PMC2765429 · Journal of translational medicine · 2009 · 7 claims · 3 setups

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  The naive Bayes model with the wrapper-based feature selection approach performed best among all predictive models tested for distinguishing CFS from controls.

  subjects naive svm c4.5

• 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