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Maximal features are intermediate features.

This approach is frequently applied and termed Genotypic Susceptibility Score (GSS; Rhee et al., 2003) In Figure 4 the AUC curves of engines trained with a minimal and maximal set of features are provided.

These features are the maximal difference of the total CN, gene orientation, fusion side, minimal and maximal length of a fusion and the tendency of probe intensities in both genes.

Lumen roundness (GAVG) is the only one ranked high by maximal relevance, yet all three features are consistently chosen by the classifier, since they provide different, complementary information on a tissue: greater circularity of lumens and increase in the number of lumens and the size of nuclei indicate malignancy of a tissue.

These features are up-regulated throughout the time course, but showed maximal expression at 4 h post-P-addition.

Results for the individual classifiers using the minimal and maximal feature set are summarized in Table 2.

Pearson's correlation coefficient (r) indicated that the predicted probability of success for the training instances using the minimal (maximal) feature set were highly correlated (i.e. close to 1): GD-ME 0.812 (0.868); GD-EV 0.797 (0.786); ME-EV 0.774 (0.768).

Evaluating the significance of the proposed features, the maximal accuracy attained using only n-gram features is 91.8% compared to 94.9% using our proposed feature vector.

An optimal combination of compared features is used to construct an algorithm that provides maximal detection efficacy per unit hardware cost.

The high-risk pathologic features were as follows: poor differentiation, deep stromal invasion, and maximal tumor diameter >2 cm.

To extract maximal information from the data, first-order questions pertaining to cells and features were treated separately from second-order questions pertaining to tissues and diagnoses.