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).

• A modified T-test feature selection method and its application on the HapMap genotype data.

  PMID 18267305 · PMC5054219 · Genomics, proteomics & bioinformatics · 2007 · 7 claims · 4 setups

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  A modified t-test ranking measure, extended to handle nominal SNP genotype data via vector transformation, can effectively rank SNPs by their discriminative capability for population classification.

  hapmap modified f-statistics informativeness

• Prioritization of candidate cancer genes--an aid to oncogenomic studies.

  PMID 18710882 · PMC2566894 · Nucleic acids research · 2008 · 8 claims · 8 setups

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  Computational classifiers using combinations of protein conservation, gene structure, protein domains, protein interactions, and regulatory data can distinguish known cancer genes (CD/CR) from unlabelled human genes

  genes cancer interpro interactome averaged cross-validation domain

• 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

• MUUMI: an R package for statistical and network-based meta-analysis for multi-omics data integration.

  PMID 41634555 · PMC12955011 · BMC bioinformatics · 2026 · 8 claims · 5 setups

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  MUUMI unifies statistical meta-analysis and network-based omics data integration within a single analytical R package framework

  lung integrated ranked thp-1 sequencing microarray statistical

• Graph Random Forest: A Graph Embedded Algorithm for Identifying Highly Connected Important Features.

  PMID 37509188 · PMC10377046 · Biomolecules · 2023 · 8 claims · 6 setups

  Has reproduction · 89

  GRF identifies effective features that form highly connected sub-graphs on the underlying biological network

  synthetic tcga embryonic simulation sub-graph reproducibility

• Integration of text- and data-mining using ontologies successfully selects disease gene candidates.

  PMID 15767279 · PMC1065256 · Nucleic acids research · 2005 · 7 claims · 6 setups

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  Integrating eVOC anatomical ontology-based text-mining of PubMed abstracts with data-mining of gene expression annotation successfully selects and prioritizes candidate disease genes

  biomedical refseq training independent text-mining data-mining candidate

• Using random walks to identify cancer-associated modules in expression data.

  PMID 24128261 · PMC4015830 · BioData mining · 2013 · 8 claims · 8 setups

  Has reproduction

  Walktrap-GM, a random-walk community detection algorithm adapted with stopping criteria (maximum modularity, maximum size, maximum module score), identifies modules significantly enriched with cancer genes in expression-weighted interaction networks.

  hepatocellular breast sporadic geo gene differential interaction

• Identifying and classifying trait linked polymorphisms in non-reference species by walking coloured de bruijn graphs.

  PMID 23536903 · PMC3607606 · PloS one · 2013 · 8 claims · 9 setups

  Has reproduction · 45

  Bubbleparse detects sequence variants directly from NGS reads without a reference genome, using the coloured de Bruijn graph implementation of Cortex plus a new depth-first bubble-finding module.

  arabidopsis bubbleparse escherichia col-0 reference-free alignment-based sequence

• SePaCS--a web-based application for classification of seroreactivity profiles.

  PMID 17478503 · PMC1933220 · Nucleic acids research · 2007 · 8 claims · 4 setups

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  SePaCS is a freely available web-based tool that trains and applies multiple classification methods (4 Naive Bayes variants, SVM with RBF kernel, LDA, DLDA) to seroreactivity profiles and outputs results as a summary table plus a detailed PDF report

  blood computational seroreactivity supervised mutual