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Individually, miR-200a, miR-200b and miR-200c normalized to serum volume and miR-103 were significantly higher in serum of the SEOC cohort (P < 0.05; 0.05; 0.0005 respectively) and in combination, miR-200b + miR-200c normalized to serum volume and miR-103 was the best predictive classifier of SEOC (ROC-AUC = 0.784).

The current study extends this work, showing that miR-200a, b and c, are positive predictive classifiers of SEOC.

These algorithms mainly aim to select a minimum set of voxels necessary for constructing a classifier with the best predictive accuracy [19].

Table 4: Prediction accuracy of the best predictive models listed in Supplementary Table 3.

Nevertheless, the classifier constructed using all the descriptors of evolutionary information (PSSM,, and ) appeared to give the best predictive performance with 75.2% prediction strength (71.6% sensitivity and 78.7% specificity), MCC = 0.393 and ROC AUC = 0.825 (Table 2).

KF algorithm is one of the best predictive algorithms, especially for noise processing and motion prediction.

Finally, it cascades every best weak classifier of the rounds to a strong classifier.

The aim of this algorithm is to select the more informative gene in order to improve the SVM classifier accuracy performance by preselecting the relative and informative genes using the mRMR method and then estimating the best predictive genes by applying the ABC algorithm as a wrapper gene selection technique with the SVM classifier.

We consider the problem of designing a study to develop a predictive classifier from high dimensional data.

A perfectly predictive classifier (or PWM in this case) would give an aROC value of 1 while a random classifier would give an aROC of ~0.5.

Of the rules we investigated, offers marginally best predictive performance.