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• For each feature, 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, we compute the median feature expression and the samples are separated into two groups, depending on whether the expression in the sample is below or above the median , as seen below(Figure 1). The levels of thus created categorical attribute are automatically denoted by P_1, P_2, …, P_B. Here P stands for “percentile” and the higher the bin number the higher the feature expression of the samples in the bin.

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Each of the defined groups produces a survival curve.

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