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

• 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

• 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

• Serum diagnosis of diffuse large B-cell lymphomas and further identification of response to therapy using SELDI-TOF-MS and tree analysis patterning.

  PMID 18163913 · PMC2242801 · BMC cancer · 2007 · 8 claims · 8 setups

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  SELDI-TOF-MS serum proteomic patterns analyzed by decision tree classification (Biomarker Pattern Software) can discriminate DLBCL patients from healthy controls with high sensitivity and specificity.

  serum seldi dlbcl pooled seldi-tof-ms proteinchip decision

• 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

• 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

• 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

• 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

• Limitations in SELDI-TOF MS whole serum proteomic profiling with IMAC surface to specifically detect colorectal cancer.

  PMID 19689818 · PMC2743709 · BMC cancer · 2009 · 7 claims · 3 setups

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  The previously reported classifier (m/z 8,132 and 4,002) failed to discriminate CRC patients from healthy volunteers in this independent validation cohort

  serum pooled seldi-tof comparative decision

• Towards precise classification of cancers based on robust gene functional expression profiles.

  PMID 15774002 · PMC1274255 · BMC bioinformatics · 2005 · 6 claims · 7 setups

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  Functional expression profiles (FEPs) achieve comparable or better classification performance than conventional gene expression profiles (GEPs) across four public microarray datasets

  nci60 lymphoma fep go-annotated cdna recursive hypergeometric

• Crunching the bio-numbers.

  PMID 14664241 · PMC1316909 · Environmental health perspectives · 2003 · 8 claims · 6 setups

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  The eTag Assay System rapidly identifies genes and related proteins without complex sample preparation or follow-up bioinformatics, unlike microarrays

  blood model brain lung etag sirna addl

• MALDI profiling of human lung cancer subtypes.

  PMID 19890392 · PMC2767501 · PloS one · 2009 · 8 claims · 8 setups

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  PIMAC/MALDI-TOF peptide profiles combined with classification models can distinguish normal lung from tumor and differentiate NSCLC histological subtypes

  lung tryptic peak ffpe maldi-tof parallel stepwise