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Algorithm

binary signal based two-tier system

1) What is Binary signals?
It is the method of determining the threshold value in accordance with the gene expression of the normal and cancer tissues by using adaptive regression algorithm and distinguishing the low expression and high expression of the corresponding gene on the basis of the threshold value determined.
2) two-tier system based on Binary signals
First tier genetic axis is referred to as the Immune Axis and is categorized by the 2 target genes that represent the axis.
Second tier genetic axis is referred to as the Prognostic Axis (cancerous stem cell-like axis; Stem-like Axis) and Predictive Axis (Epithelial Axis), and is categorized by the 2 target genes that represent the axis.
The system of categorization into 2 tiers as described above is referred to as 2-tier categorization.
3) Cluster categorization system: Immune vs. Prognostic Axis
Immune Axis categorizes the Group 1 with the best prognosis by the 2 target genes that represent the immune axis and categorizes Prognostic Cluster I in accordance with the system of categorization of the prognosis groups.
The Second Tier is executed for the remaining stomach cancer patient groups with the exclusion of the Group 1 categorized in the First tier, and categorizes the patients into 4 groups (Groups 2, 3, 4 and 5) in accordance with the quartile with the genes that represent the epithelial axis and the cancerous stem cell-like axis.
Group 2 and Group 4, which are the groups with low expression of the representative genes by the Prognostic Axis (cancerous stem cell-like axis; Stem-like Axis) are categorized into the group with intermediate level of prognosis and referred to as Prognostic Cluster II, while the Group 3 and the Group 5, which are the groups with high gene expression are categorized into the group with bad prognosis and referred to as Prognostic Cluster III.
4) Categorization of the prognosis group on the basis of the Immune Axis and the Prognostic Axis
Immune Axis and Prognostic Axis are determined by the threshold value computed through adaptive regression algorithm in accordance with the expression of each of the target genes (G1, G2, G3 and G4).
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