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).

• 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

• Development of proteomic patterns for detecting lung cancer.

  PMID 14757945 · PMC3851077 · Disease markers · 2003 · 8 claims · 3 setups

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  A decision tree classification algorithm built on three serum protein mass peaks (8122Da, 1452Da, 1610Da) can discriminate lung cancer patients from healthy controls

  serum pooled seldi-tof-ms decision reproducibility

• Constructing support vector machine ensembles for cancer classification based on proteomic profiling.

  PMID 16689692 · PMC5173238 · Genomics, proteomics & bioinformatics · 2005 · 7 claims · 4 setups

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  CSVME, built by selecting a subset of base SVMs via SVM-RFE ranking and fusing them with a trained upper-layer SVM, achieves better classification performance than an ensemble of all base SVMs.

  serum computational seldi-tof svm-rfe-based 10-fold

• 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

• 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