Table 3

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


Size of data




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.

Chu et al. Genome Biology 2008 9:R118   doi:10.1186/gb-2008-9-7-r118

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