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Table 3 Comparative performance results in prostate 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 = 5, Δθ = 10
λ = 10-4λmax Linear SVM
Sensitivity: 97.6 (2.8)%; specificity: 99 (2.2)%; accuracy: 98.3%
Control specific component extracted with respect to a cancer reference sample.
Proposed method M = 4, Δθ = 10
λ = 10-4λmax Linear SVM
Sensitivity: 97.7 (2.3)%; specificity: 98 (2.4)%; accuracy: 97.9%
Control specific component extracted with respect to a cancer reference sample.
[1] Sensitivity: 86 (6.6)%; specificity: 67.8(12.9)%; accuracy: 76.9%.
[46] Sensitivity: 94.7%; specificity: 75.9%; accuracy: 85.3%. 253 benign and 69 cancers. Results were obtained on independent test set comprised of 38 cancers and 228 benign samples.
[47] Sensitivity: 97.1%; specificity: 96.8%; accuracy: 97%. 253 benign and 69 cancers. Cross-validation details not reported.
[45] Average error rate of 28.97 on four class problem with three-fold cross-validation.