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Table 2 Classification results

From: A critical assessment of SELDI-TOF-MS for biomarker discovery in serum and tissue of patients with an ovarian mass

Serum training cohort
  CM10 Q10
  accuracy sens spec accuracy sens spec
DLDA 81 75 86 73 71 75
SVM 79 75 83 71 69 73
Serum validation cohort
DLDA     73 88 62
SVM     81 81 81
Tissue predicted (%)
  Benign LMP Cancer
Benign (true) 82.1 17.6 0.3
LMP (true) 10.6 64.5 24.9
Cancer (true) 3.4 10.6 86.0
  1. A. Classification of serum samples on the training and independent validation cohort. Training cohort: average classification accuracy, sensitivity (sens), and specificity (spec) (in percentage) of discriminating ovarian cancer versus benign tumor for CM10 and Q10 arrays on 500 test sets (repeated random sampling; size training sets: 80, size test sets: 47 for CM10, 48 for Q10). Validation cohort: classification accuracy, sensitivity, and specificity (in percentage) of the classifiers trained on all serum training data for Q10 validation data only. Classification models: SVM (support vector machine) and DLDA (diagonal linear discriminant analysis) with feature selection.
  2. B. Confusion matrix giving the percentage of cases (average over 500 test sets) from one class classified into each of the three classes. Rows correspond to the correct class, columns to the predicted class. Results are for DLDA with three different classes: benign tumors, tumors of low malignant potential (LMP) and cancer tissue (Q10 data).