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).

• Swarm intelligence based wavelet coefficient feature selection for mass spectral classification: an application to proteomics data.

  PMID 19733729 · PMC2748225 · Analytica chimica acta · 2009 · 8 claims · 4 setups

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  ACA-based wavelet coefficient feature selection can achieve up to 100% classification accuracy on training, validating, and independent testing sets using only 5 selected features.

  blood baseline-corrected 953-dimensional training seldi-tof wavelet ant

• A scale space approach for unsupervised feature selection in mass spectra classification for ovarian cancer detection.

  PMID 19828085 · PMC2762074 · BMC bioinformatics · 2009 · 7 claims · 1 setups

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  A scale-space based unsupervised feature extraction method combined with SVM classification achieves high accuracy in ovarian cancer detection from serum mass spectra.

  serum high-resolution

• On consensus biomarker selection.

  PMID 17570864 · PMC1892093 · BMC bioinformatics · 2007 · 7 claims · 2 setups

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  Four popular feature ranking criteria (t-statistic, mutual information, peak probability contrasts, random forest variable importance) produce different rankings of the same features on the same dataset.

  serum seldi-tof maldi-tof

• Proteomics as a tool for biomarker discovery.

  PMID 18057524 · PMC3851415 · Disease markers · 2007 · 8 claims · 7 setups

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  A useful clinical biomarker must be easily attainable, have adequate sensitivity, have adequate specificity, and lead to patient benefit through intervention

  serum laser ovarian seldi-tof two-dimensional gene

• 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