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The Kaplan-Meier task begins similar to the Cox regression task, then differs when selecting categorical attributes to define the compared groups. 

For each feature (e.g. gene), the expression values are sorted in ascending order and placed into B bins of (roughly) equal size. As a result, a feature-specific categorical attribute with B levels is constructed which can be used by itself or in combination with other categorical attributes. For instance, for B = 2 which is shown in the example below, we compute the median feature expression and the (Figure 1), we take a given feature and compute its median expression. The samples are separated into two groupsbins, depending on whether the expression in the sample is below or above the median (Figure 1). The levels of the created categorical attribute bins are automatically denoted by labeled P_1, P_2 , …, P_B. Here P where P stands for “percentile” and the . The higher the bin number the higher the feature expression of in the samples in the bin. The bins are feature-specific since this procedure is repeated for each feature separately.

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