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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Has reproduction · 78
Emergent dynamics of underlying regulatory network links EMT and androgen receptor-dependent resistance in prostate cancer.
PMID 36851919 · PMC9957767 · Computational and structural biotechnology journal · 2023 · 8 claims · 7 setups
Simulations of the EMT-AR crosstalk network reveal four possible phenotypes: epithelial-sensitive (ES), epithelial-resistant (ER), mesenchymal-resistant (MR), and mesenchymal-sensitive (MS), with MS occurring rarely
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Has reproduction · 67
Optimal scaling of digital transcriptomes.
PMID 24223126 · PMC3819321 · PloS one · 2013 · 8 claims · 8 setups
Fifteen existing and novel transcript-count normalization algorithms can be compared with two novel, mutually independent metrics: the number of "uniform" genes (sufficiently low coefficient of variation after normalization) and low average Spearman correlation between normalized expression profiles of gene pairs.
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Loss of long-range co-expression is a common feature in cancer.
PMID 41935098 · PMC13247121 · NPJ systems biology and applications · 2026 · 8 claims · 7 setups
In cancer, the strongest co-expressed gene pairs (top MI values) are predominantly intra-chromosomal, unlike in normal tissue.
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sCellST predicts single-cell gene expression from H& E images.
PMID 41513659 · PMC12858858 · Nature communications · 2026 · 7 claims · 6 setups
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