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Table 2 Comparative performance results in ovarian 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

Method Sensitivity/Specificity/Accuracy
Proposed method M = 3, Δθ = 50
λ = 10-4λmax
Linear SVM
Sensitivity: 96.2 (2.7)%; specificity: 93.6 (4.1)%; accuracy: 94.9%
Control specific component extracted with respect to a cancer reference sample.
Proposed method M = 4, Δθ = 30
λ = 10-6λmax
Linear SVM
Sensitivity: 95.4 (3)%; specificity: 94 (3.7)%; accuracy:94.7%
Control specific component extracted with respect to a cancer reference sample.
[1] Sensitivity: 81.4 (7.1)%; specificity: 71.7 (6.6)%
[42] Sensitivity: 100%; specificity: 95% (one partition only: 50/50 training; 66/50 test).
[44] Accuracy averaged over 10 ten-fold partitions: 98-99% (sd: 0.3-0.8)
[13] Sensitivity: 98%, specificity: 95%, two-fold CV with 100 partitions.
[45] Average error rate of 4.1% with three-fold CV.