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).
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Development of proteomic patterns for detecting lung cancer.
PMID 14757945 · PMC3851077 · Disease markers · 2003 · 8 claims · 3 setups
A decision tree classification algorithm built on three serum protein mass peaks (8122Da, 1452Da, 1610Da) can discriminate lung cancer patients from healthy controls
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Identification of deleterious non-synonymous single nucleotide polymorphisms using sequence-derived information.
PMID 18588693 · PMC2446391 · BMC bioinformatics · 2008 · 8 claims · 5 setups
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.
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Peptide bioinformatics: peptide classification using peptide machines.
PMID 19065810 · PMC7122642 · Methods in molecular biology (Clifton, N.J.) · 2008 · 8 claims · 4 setups
The bio-basis function, which converts peptides into numerical vectors using nongapped pairwise homology alignment scores against indicator peptides, can statistically quantify peptide similarity for classification.
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Has reproduction · 48
Comparative analysis of molecular signatures reveals a hybrid approach in breast cancer: Combining the Nottingham Prognostic Index with gene expressions into a hybrid signature.
PMID 35143511 · PMC8830616 · PloS one · 2022 · 8 claims · 6 setups
A hybrid signature combining the Nottingham Prognostic Index with SIS-selected gene expressions can be built in a data-driven fashion (NPI treated as a gene expression during feature selection).
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Speeding disease gene discovery by sequence based candidate prioritization.
PMID 15766383 · PMC1274252 · BMC bioinformatics · 2005 · 7 claims · 8 setups
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.
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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
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
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Boosting accuracy of automated classification of fluorescence microscope images for location proteomics.
PMID 15207009 · PMC449699 · BMC bioinformatics · 2004 · 8 claims · 8 setups
New classifiers (SVMs, ensembles) and new wavelet-derived (Gabor, Daubechies) features improve recognition of protein subcellular location patterns over the previous neural network approach
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Application of machine learning in SNP discovery.
PMID 16398931 · PMC1955739 · BMC bioinformatics · 2006 · 8 claims · 6 setups
PolyBayes produces high false-positive SNP predictions even with stringent parameters
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Constructing support vector machine ensembles for cancer classification based on proteomic profiling.
PMID 16689692 · PMC5173238 · Genomics, proteomics & bioinformatics · 2005 · 7 claims · 4 setups
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.
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Has reproduction · 53
Combining evidence of preferential gene-tissue relationships from multiple sources.
PMID 23950964 · PMC3741196 · PloS one · 2013 · 8 claims · 8 setups
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.