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).

• Deep-learning prediction of gene expression from personal genomes.

  PMID 41495833 · PMC12869966 · Genome biology · 2026 · 8 claims · 8 setups

  Full-text index only

  Fine-tuning Enformer on paired personal WGS and RNA-seq data (Variformer) corrects Enformer's failure to predict inter-individual gene expression differences across held-out people.

  gtex rosmap same monocyte paired mutagenesis chromhmm

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

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

  Full-text index only

  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

• Constructing support vector machine ensembles for cancer classification based on proteomic profiling.

  PMID 16689692 · PMC5173238 · Genomics, proteomics & bioinformatics · 2005 · 7 claims · 4 setups

  Full-text index only

  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

• JIGSAW, GeneZilla, and GlimmerHMM: puzzling out the features of human genes in the ENCODE regions.

  PMID 16925843 · PMC1810558 · Genome biology · 2006 · 8 claims · 4 setups

  Full-text index only

  Adding model states for specific biological features (signal peptides, CpG islands, etc.) to non-comparative GHMM gene finders did little or nothing to enhance predictive accuracy, sometimes reducing it.

  encode genes invertebrate initio comparative training

• Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics.

  PMID 18053132 · PMC2254445 · BMC bioinformatics · 2007 · 6 claims · 5 setups

  Full-text index only

  The paired oligo-border kernel (POBK) combined with SVMs predicts peptide adsorption/elution in SAX-SPE and retention time in IP-RP-HPLC more accurately than existing methods.

  peptides peptide sax-spe ip-rp-hplc cross-run