Research article
Prediction potential of candidate biomarker sets identified and validated on gene expression data from multiple datasets
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* Corresponding author: Aydin Tozeren aydin.tozeren@drexel.edu
School of Biomedical Engineering, Drexel University, Philadelphia, PA, USA
BMC Bioinformatics 2007, 8:415 doi:10.1186/1471-2105-8-415
Published: 26 October 2007Additional files
Additional file 1:
Prediction error of DLDA classifiers on lymphoma (Broad) and renal carcinoma (Zhao) datasets. Classifiers trained to predict relapse-free status. E is the mean 1-AUC of the corresponding set of ROC curves, calculated as described in the Methods section. Error bars show empirical 95% CIs.
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Additional file 2:
Significance of prediction error (P values) of DLDA classifiers trained to predict molecular phenotype. Bold entries indicate significant P-values < = 0.01.
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Additional file 3:
Kaplan-Meier plots of survival rates for tumor classes with different classification/cross-validation methods. Classifiers trained on the basis of relapse-free status on diffuse large B-cell lymphoma dataset GSE4475. Column 1: Weighted-voting algorithm. Column 2: DLDA. Row 1: Leave-one out cross-validation. All data used for training and testing. Row 2: Training and test sets selected randomly from the dataset. Training based on leave-one out cross-validation.
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