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 · 91
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
DeClust is a reference profile-free deconvolution method that simultaneously deconvolves bulk tumor expression into cancer, immune, and stromal compartments and clusters samples into cancer cell-intrinsic molecular subtypes, outputting subtype-specific reference profiles for the cohort rather than for individuals.
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Has reproduction · 95
Increased prevalence of hybrid epithelial/mesenchymal state and enhanced phenotypic heterogeneity in basal breast cancer.
PMID 38974967 · PMC11225361 · iScience · 2024 · 7 claims · 7 setups
Luminal breast cancer gene expression signature is closely/positively associated with an epithelial signature
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Full-text index only
A statistical approach for array CGH data analysis.
PMID 15705208 · PMC549559 · BMC bioinformatics · 2005 · 8 claims · 4 setups
Existing model-selection criteria (AIC, BIC, and prior ad hoc penalties) are not well adapted to estimating the number of segments in array CGH data
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Full-text index only
Exploiting noise in array CGH data to improve detection of DNA copy number change.
PMID 17272296 · PMC1994778 · Nucleic acids research · 2007 · 7 claims · 4 setups
When aberrations are present, noise in BAC, 19k oligo, and 385k oligo array-CGH data is highly non-Gaussian and shows long-range spatial correlations.
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Has reproduction · 85
Digital sorting of complex tissues for cell type-specific gene expression profiles.
PMID 23497278 · PMC3626856 · BMC bioinformatics · 2013 · 8 claims · 8 setups
The Digital Sorting Algorithm (DSA) deconvolves mixed tissue expression into cell type-specific profiles using only marker genes, without requiring prior knowledge of cell type frequencies or in vitro pure-cell profiles.