A Paradigm for Class Prediction Using Gene Expression Profiles

By Michael D. Radmacher, Lisa M. McShane, Richard M. Simon. J. Comput. Biol., 2002

317 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/2d8e77473790589761d1345902dc35c581447ffc

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In Molecular Biology

Citations in the corpus

Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma (2017, unknown): “When the Bayesian compound covariate predictor (BCCP) algorithm (Radmacher et al., 2002) was applied to the mRNA expression data, 11 samples with gene expression patterns similar to the IDH1/2-mutated samples were identified; however, these samples did not have IDH1/2mutations (Figure 4A).”
The proliferation gene expression signature is a quantitative integrator of oncogenic events that predicts survival in mantle cell lymphoma. (2003, methodology): “…and might turn MCL into a clinically Bayesian formulation of a discrimator between two manageable disease. lymphoma subtypes The method we used to formulate our predictor of MCL was based on a Experimental procedures variation of the compound covariate predictor (Radmacher et al., 2002).”
Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. (2003, unknown): “Predictors based on linear combinations of the weighted intensity measurements of the informative genes have been proposed (1, 5 ).”
Analysis of Gene Expression Data Using BRB-Array Tools (2007, methodology): “The models provided include diagonal linear discriminant analysis (Dudoit and Fridlyand, 2003), compound covariate predictor (Radmacher et al. 2002), nearest neighbor classifi cation (Ripley, 1996), nearest centroid classifi cation, shrunken centroid classifi cation (Tibshirani et al. 2002),…”
Predicting hepatitis B virus–positive metastatic hepatocellular carcinomas using gene expression profiling and supervised machine learning (2003, methodology): “This algorithm includes a 'leave-one-out' cross-validation test to avoid the statistical problem of over-estimating prediction accuracy that occurs when a model is trained and evaluated with the same sample”