Forward-Backward Binarization

2025-10-17

AI Summary

Gene expression binarization is essential for constructing Boolean gene regulatory networks (GRNs), yet conventional thresholding methods neglect regulatory logic, compromising biological plausibility and robustness. To address this, we propose a regulatory-graph-guided forward-backward binarization method: first, adaptive thresholding performs initial screening; then, known regulatory interactions (regulator → target gene) guide forward propagation of functional states via Boolean rules, followed by backward calibration of regulator expression levels—enabling dynamic state inference from static single-cell data without temporal information. Our approach significantly improves binarization accuracy and biological consistency. Evaluations on synthetic and real-world GRNs demonstrate superior binarization precision and enhanced downstream GRN reconstruction performance compared to state-of-the-art thresholding methods. Moreover, the resulting binarized profiles effectively support controllability analysis and intervention strategy design.

Technology Category

• Reasoning under Uncertainty: Graphical Models
• Machine Learning: Probabilistic Circuits and Graphical Models
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Binarization of gene expression data is a extbf{critical 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 extbf{regulation-based binarization 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.

Problem

• Binarization oversimplifies gene expression data biology
• Thresholding methods are insufficient for regulatory network modeling
• Snapshot data requires regulation-based binarization for accuracy

Innovation

• Combines thresholding with regulation-based value completion
• Uses regulatory graph to propagate binary values
• Ensures binarization consistency with Boolean modeling principles