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).

• Cell type-specific eQTL analysis of COVID-19 based on single-cell transcriptomic data.

  PMID 41064594 · PMC12501775 · NAR genomics and bioinformatics · 2025 · 8 claims · 8 setups

  Has reproduction · 51

  Single-cell eQTL analysis across eight immune cell types identified 2593 genes whose expression is significantly associated with common genetic polymorphisms, with most genes showing cell type-specific effects

  pbmcs eight pbmc monocytes scrna-seq genetic cis-eqtl

• DeepGSEA: explainable deep gene set enrichment analysis for single-cell transcriptomic data.

  PMID 38950178 · PMC11236288 · Bioinformatics (Oxford, England) · 2024 · 8 claims · 2 setups

  Has reproduction · 40

  DeepGSEA is an explainable deep gene set enrichment analysis method built on interpretable, prototype-based neural networks.

  simulated real-world simulation scrna-seq-based

• Single-Cell Differential Network Analysis with Sparse Bayesian Factor Models.

  PMID 35186014 · PMC8855158 · Frontiers in genetics · 2021 · 8 claims · 2 setups

  Has reproduction · 85

  A hierarchical Bayesian factor model using treatment-dependent latent factor loadings can construct gene co-expression networks from scRNA-seq data and identify differences in network structure between two (or more) biological conditions.

  simulated sars-cov-2 scrna-seq

• Meta-analysis of COVID-19 single-cell studies confirms eight key immune responses.

  PMID 34675242 · PMC8531356 · Scientific reports · 2021 · 8 claims · 8 setups

  Has reproduction · 69

  Only 8 of 20 previously published COVID-19 scRNA-seq findings were reproducible across all relevant datasets in a standardized meta-analysis

  pbmc bronchoalveolar nasopharyngeal combined scrna-seq tcr bcr

• Integrative transcriptomic and machine learning framework reveals candidate genes and potential mechanisms of aflatoxin B1 exposure in breast cancer.

  PMID 41688730 · PMC12982753 · Scientific reports · 2026 · 7 claims · 8 setups

  Has reproduction · 83

  Twenty-two genes lie at the intersection of AFB1-predicted targets and breast cancer-associated co-expression modules/DEGs

  afb1 breast training candidate computational bulk weighted