MeV+R: using MeV as a graphical user interface for Bioconductor applications in microarray analysis

Table 3 Comparing the results of iterativeBMA to KNN and USC on the leukemia data and the breast cancer prognosis data

Data	Size of data	iterativeBMA	KNN	USC
Leukemia data [32]	38 training samples	11 genes	3,051 genes	51 genes
	34 test samples	2 errors	2 errors	2 errors
Breast cancer prognosis data [33]	76 training samples	4 genes	4,919 genes	662 genes
	19 test samples	3 errors	5 errors	4 errors

The number of selected genes and the number of classification errors are shown for each method. For each dataset, the smallest number of genes and the smallest number of classification errors across all three methods are shown in bold. On the leukemia data, iterativeBMA produced the same number of classification errors using much fewer genes. On the breast cancer prognosis data, iterativeBMA produced fewer errors using much fewer genes.