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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Computational approaches for predicting the biological effect of p53 missense mutations: a comparison of three sequence analysis based methods.
PMID 16522644 · PMC1390679 · Nucleic acids research · 2006 · 7 claims · 6 setups
Align-GVGD predicts loss of transactivation activity with high specificity (~88%) but lower sensitivity (67.9-71.2%) for neutral mutants
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p53 expression and its relationship to DNA alterations in bone and soft tissue sarcomas.
PMID 8260365 · PMC1968651 · British journal of cancer · 1993 · 8 claims · 6 setups
25.7% (29/113) of bone and soft tissue sarcomas show positive p53 immunostaining
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Mechanisms of relapse in acute leukaemia: involvement of p53 mutated subclones in disease progression in acute lymphoblastic leukaemia.
PMID 10098750 · PMC2362216 · British journal of cancer · 1999 · 6 claims · 4 setups
p53 mutations are detected at relapse far more frequently in ALL (28.6%) than in AML (7.3%)
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Correlation of mutation and immunohistochemistry of p53 in hepatocellular carcinomas in Korean people.
PMID 12483005 · PMC3054961 · Journal of Korean medical science · 2002 · 6 claims · 3 setups
5% immunoreactive tumor cells is a reliable IHC threshold to detect p53 mutations in HCC
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Alteration of the p53 tumor suppressor gene occurs independently of K-ras activation and more frequently in serous adenocarcinomas than in other common epithelial tumors of the human ovary.
PMID 7852189 · PMC5919385 · Japanese journal of cancer research : Gann · 1994 · 7 claims · 4 setups
p53 gene mutations occur more frequently in serous adenocarcinomas than in other common epithelial ovarian tumors combined
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Has reproduction · 85
Predicting the pathogenicity of missense variants using features derived from AlphaFold2.
PMID 37084271 · PMC10203375 · Bioinformatics (Oxford, England) · 2023 · 6 claims · 8 setups
AlphaFold2-derived structural features (solvent accessibility, amino acid network features, physicochemical environment, pLDDT) can be used to train a random forest classifier (AlphScore) that distinguishes proxy-benign from proxy-pathogenic missense variants.