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Merging Microarray Data, Robust Feature Selection, and Predicting Prognosis in Prostate Cancer
Motivation: Individual microarray studies searching for prognostic biomarkers often have few samples and low statistical power; however, publicly accessible data sets make it possible to combine data across studies. Method: We present a novel approach for combining microarray data across institutions and platforms. We introduce a new algorithm, robust greedy feature selection (RGFS), to select predictive genes. Results: We combined two prostate cancer microarray data sets, confirmed thedoi:10.1177/117693510600200009 fatcat:a4kuzrjbpzailm6j4nm4isn3ii