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).

• 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

• 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

• 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

• Combining evidence of preferential gene-tissue relationships from multiple sources.

  PMID 23950964 · PMC3741196 · PloS one · 2013 · 8 claims · 8 setups

  Has reproduction · 53

  A high-level integration approach combining three methods across four human microarray datasets, merged by consensus voting and a rule-based inner/total score, predicts preferentially expressed genes while reducing method- and study-specific bias.

  normal four microarray genes microarray computational bayes

• 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

• Boosting accuracy of automated classification of fluorescence microscope images for location proteomics.

  PMID 15207009 · PMC449699 · BMC bioinformatics · 2004 · 8 claims · 8 setups

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  New classifiers (SVMs, ensembles) and new wavelet-derived (Gabor, Daubechies) features improve recognition of protein subcellular location patterns over the previous neural network approach

  hela automated stepwise training-set-size

• GAUGE-Annotated Microbial Transcriptomic Data Facilitate Parallel Mining and High-Throughput Reanalysis To Form Data-Driven Hypotheses.

  PMID 33758032 · PMC8547006 · mSystems · 2021 · 8 claims · 7 setups

  Has reproduction · 70

  GAUGE automatically annotates GEO microbial transcriptomic data sets (microarray and RNA-seq), increasing the proportion of annotatable studies from 4% to 33%

  microarray rna-seq aeruginosa mantel manual anova

• 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

• Policy implications of genetic information on regulation under the Clean Air Act: the case of particulate matter and asthmatics.

  PMID 16507451 · PMC1392222 · Environmental health perspectives · 2006 · 8 claims · 4 setups

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  The Clean Air Act mandates protection of sensitive subpopulations, including asthmatics, from air pollution health effects, creating an opening for genetic susceptibility data in regulation.

  u.s epa asthma asthmatic statutory regulatory literature