Figure 5. Tree-based Structures Can Represent Complex Relationships in Sets of Variables.

Here each derived variable Z is computed from its inputs (genetic variants, clinical factors, or other derived variables) and a pair of edge parameters θ. The regression coefficient β₁ represents the net effect of the entire combination of variables on the outcome of interest Y. These structures were explored using Bayesian algorithms to learn biosignatures of nicotine metabolism.