Forward-Backward Binarization

Binarization of gene expression data is a $\mathbf{\text{critical}}\mathbf{\text{prerequisite}}$ for the synthesis of Boolean gene regulatory network (GRN) models from omics datasets. Because Boolean networks encode gene activity as binary variables, the accuracy of binarization directly conditions whether the inferred models can faithfully reproduce biological experiments, capture regulatory dynamics, and support downstream analyses such as controllability and therapeutic strategy design. In practice, binarization is most often performed using thresholding methods that partition expression values into two discrete levels, representing the absence or presence of gene expression. However, such approaches oversimplify the underlying biology: gene-specific functional roles, measurement uncertainty, and the scarcity of time-resolved experimental data render thresholding alone insufficient. To overcome these limitations, we propose a novel $\mathbf{\text{regulation-based}}\mathbf{\text{binarization}}\mathbf{\text{method}}$ tailored to snapshot data. Our approach combines thresholding with functional binary value completion guided by the regulatory graph, propagating values between regulators and targets according to Boolean regulation rules. This strategy enables the inference of missing or uncertain values and ensures that binarization remains biologically consistent with both regulatory interactions and Boolean modeling principles of the gene regulation. Validation against ODE simulations of artificial and established Boolean GRNs demonstrates that the method achieves accurate and robust binarization, thereby strengthening the reliability of Boolean network synthesis.