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 · 73
Expression of neurofibromin 1 in colorectal cancer and cetuximab resistance.
PMID 34779495 · PMC8611403 · Oncology reports · 2022 · 8 claims · 8 setups
NF1 is highly expressed in cetuximab-sensitive CRC cell lines and minimally expressed in cetuximab-resistant ones
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Full-text index only
Oncogene mutations, copy number gains and mutant allele specific imbalance (MASI) frequently occur together in tumor cells.
PMID 19826477 · PMC2757721 · PloS one · 2009 · 8 claims · 8 setups
Homozygous mutations of oncogenes are frequent (20%) across 833 cancer cell lines of 12 tumor types in the Sanger database
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Has reproduction · 61
Multi-omics analyses identify mannose phosphate isomerase-centered hypoxia-induced angiogenesis signature in colorectal cancer.
PMID 41204349 · PMC12595641 · Journal of translational medicine · 2025 · 8 claims · 8 setups
Twelve HIA-related genes were identified that are transcriptionally activated by HIFs and functionally implicated in angiogenesis in CRC.
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Has reproduction · 45
Single-Cell Analysis Reveals Characterization of Infiltrating T Cells in Moderately Differentiated Colorectal Cancer.
PMID 33584715 · PMC7873865 · Frontiers in immunology · 2020 · 8 claims · 7 setups
Eight distinct T cell populations are identifiable in CRC tumor tissue and seven in peripheral blood by unsupervised clustering of scRNA-seq data.
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Has reproduction · 71
Parsimonious Gene Correlation Network Analysis (PGCNA): a tool to define modular gene co-expression for refined molecular stratification in cancer.
PMID 30993001 · PMC6459838 · NPJ systems biology and applications · 2019 · 8 claims · 7 setups
Retaining only the top ~3 most correlated edges per gene (EPG3) combined with FastUnfold clustering (termed PGCNA) produces gene co-expression modules with significantly better separation and enrichment of known biology than using all edges or other clustering methods.