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Table 4 Comparative performance results in colon cancer prediction. Sensitivities and specificities were estimated by 100 two-fold cross-validations (standard deviations are in brackets).

From: A mixture model with a reference-based automatic selection of components for disease classification from protein and/or gene expression levels

Methods Sensitivity/Specificity/Accuracy
Proposed method M = 2, Δθ = 10
RBF SVM (σ2 = 1200, C = 1)
Sensitivity: 90.8 (5.5)%, specificity: 79.4 (9.8)%; accuracy: 85.1%
Control specific component extracted with respect to a cancer reference sample.
Proposed method M = 4, Δθ = 50 λ = 10-2λmax
RBF SVM (σ2 = 1000, C = 1)
Sensitivity: 89.8 (6.2)%, specificity: 78.6 (12.8)%; accuracy: 84.2%.
Control specific component extracted with respect to a control reference sample.
[1] Sensitivity: 89.7 (6.4)%, specificity: 84.3 (8.4)%; accuracy = 87%. 100 two-fold cross-validations.
[2] Sensitivity: 92.1 (4.7)%, specificity: 85 (10.1)%; accuracy: 88.55%. 100 two-fold cross-validations. c u = 2.0.
[48] Sensitivity: 92-95% calculated from Figure 5. Specificity not reported.
[15] Accuracy 85%. Cross-validation details not reported.
[50] Accuracy 82.5%, ten-fold cross-validation (RFE with linear SVM).
[51] Accuracy 88.84%, two-fold cross-validation (RFE with linear SVM and optimized penalty parameter C).