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Spearman rank marker-order correlations were then estimated and a heuristic 'fit value' for each comparison was calculated as; Fit value = correlation × log (number of common markers); where the second term rewards for the number of common markers with a diminishing returns function.

The usual random forest (RF) variable importance (VI) index used to rank markers for variable selection is not optimal when applied to linkage data because of correlation between markers.

We trained support vector classifiers using 1, 10, 50, 100, 200, 500 and 1000 highest ranked marker(s).

We tested the effect of using 20, 50, and 200 ranked marker genes from a list of differentially expressed genes, as they represent the number of markers commonly used in classification and prediction studies for microarray data, as well as the level at which we might expect to begin to see noise.

Top ranked marker genes predicted using both data sets #1 and #2 were investigated.

Furthermore, top ranked marker genes were experimentally validated by reverse transcriptase-polymerase chain reaction (RT-PCR).

Moreover, top ranked marker genes were further validated by RT-PCR.

Figure 4A shows the expression profiles of the top two ranked marker peaks for classifying disease outcome.

Figure 4B shows the expression profiles of the top two ranked marker peaks for classifying the characteristic of lymph node involvement.

Therefore, the receiver operating characteristics (ROC) were plotted for different thresholds of ranked marker pairs, and for different models of heritability and MAF.

The test statistic is thus: where LogLik(m(i)) is the log-likelihood of the model including the i-th ranking marker.