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).

• Software JimenaE allows efficient dynamic simulations of Boolean networks, centrality and system state analysis.

  PMID 36725967 · PMC9892028 · Scientific reports · 2023 · 8 claims · 4 setups

  Has reproduction · 74

  JimenaE performs systematic, exhaustive calculation of all network system states rather than the heuristic approach used by SQUAD.

  mesenchymal arabidopsis dendritic comparison boolean dynamical network

• A reference profile-free deconvolution method to infer cancer cell-intrinsic subtypes and tumor-type-specific stromal profiles.

  PMID 32111252 · PMC7049190 · Genome medicine · 2020 · 8 claims · 8 setups

  Has reproduction · 91

  DeClust is a reference-profile-free deconvolution method that incorporates molecular subtyping directly into the deconvolution process, outputting cohort-level cancer subtype and stromal reference profiles rather than per-individual profiles

  tcga simulated muscle-invasive ccrcc computational simulation-based deconvolution

• SiDCoN: a tool to aid scoring of DNA copy number changes in SNP chip data.

  PMID 17971856 · PMC2034603 · PloS one · 2007 · 8 claims · 3 setups

  Full-text index only

  SiDCoN is a spreadsheet-based application that simulates Ballele and logR plots for all known types of DNA copy number change, with or without stromal contamination, for up to 5000 SNP data points and up to 3 combined aberrations

  sidcon real mixtures snp re-analysis

• Community assessment of methods to deconvolve cellular composition from bulk gene expression.

  PMID 39191725 · PMC11350143 · Nature communications · 2024 · 8 claims · 4 setups

  Has reproduction · 63

  Most deconvolution methods accurately predict coarse-grained immune/stromal cell populations from bulk expression.

  admixtures cancer tumor bulk bulk admixture crowdsourced

• sCellST predicts single-cell gene expression from H& E images.

  PMID 41513659 · PMC12858858 · Nature communications · 2026 · 7 claims · 6 setups

  Full-text index only

  sCellST is a weakly supervised (Multiple Instance Learning) deep learning framework that predicts single-cell gene expression from H&E images alone, trained using paired spatial transcriptomics (Visium) and H&E slides

  ovarian kidney prostate breast simulation spot-level nucleus