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).

• Predicting the phenotypic effects of non-synonymous single nucleotide polymorphisms based on support vector machines.

  PMID 18005451 · PMC2216041 · BMC bioinformatics · 2007 · 8 claims · 5 setups

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  Parepro, an SVM-based method integrating three attribute sets (RD, MI, IE) derived from evolutionary and residue-property information, predicts whether an nsSNP is deleterious or neutral.

  sequences target svm benchmark homology

• Genome bioinformatic analysis of nonsynonymous SNPs.

  PMID 17708757 · PMC1978506 · BMC bioinformatics · 2007 · 8 claims · 8 setups

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  Structure- and sequence-based prediction tools can generally distinguish disease-causing mutations from neutral ones

  gene modelled nssnps automated fold relative

• Predicting deleterious nsSNPs: an analysis of sequence and structural attributes.

  PMID 16630345 · PMC1489951 · BMC bioinformatics · 2006 · 8 claims · 7 setups

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  Sequence conservation (PSIC score difference) at the nsSNP position is the single most useful attribute for predicting deleterious vs neutral status.

  swissprot nssnp-containing nssnps machine single-attribute sequence

• 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

• Cancer-specific high-throughput annotation of somatic mutations: computational prediction of driver missense mutations.

  PMID 19654296 · PMC2763410 · Cancer research · 2009 · 7 claims · 7 setups

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  CHASM, a Random Forest-based computational method, was developed to identify and prioritize missense mutations likely to be functional drivers of tumor cell proliferation.

  missense same synthetic tp53 random support principal