Section IV
Diagnostic Accuracy
The class discriminating genes were identified as described above, and then used in an ANN-based supervised learning algorithm. As previously discussed (section II, Supplemental Information), class assignment was based on a differential diagnostic tree format and required that the node value for assignment exceeded a statistically defined confidence level. The results of this analysis are shown in Table 4 of the paper and are included below for the reader’s convenience.

Table S16. Training and test set permutation results – errors per group using 50 probe sets

Comparison of supervised learning algorithms
The performance of other supervised learning algorithms was compared to ANN. Using the original training and test sets, chi-squared was used to select the desired number of probes sets, and then the selected probes were used to build a model using ANN, SVM, and k-NN. ANN was performed with one hidden layer consisting of 4 nodes and the backpropagation epoch number was 5000. For the other algorithms, the linear SVM kernel was used and the k-NN parameter was 3. The comparison of the results is shown in Table S17 below. The comparison was performed using the top 20 and 50 probe sets, as well as the top 20 and 50 genes. The numbers correspond to the number of errors made in either the training or test set by class for each metric. Overall, ANN and SVM performed fairly comparably while k-NN gave slightly poorer results.

Table S17. Comparison of supervised learning algorithms

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