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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Reliable Bayesian Network Structure Learning in Biomedical Applications: Model Uncertainty Criterion and Its

Grigoriy Gogoshin¹, Andrei S Rodin^1,2

¹Department of Computational and Quantitative Medicine, Beckman Research Institute, and Diabetes and Metabolism Research Institute, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, 91010, CA, USA.

Biorxiv : the Preprint Server for Biology

|November 24, 2025

Summary

This study refines the Minimum model Uncertainty (MU) criterion for Bayesian network (BN) structure learning in systems biology. The enhanced MU criterion improves consistency, generalizability, and interpretability of biomedical data analysis.

Keywords:

AIC APOE BD BDeu BIC Bayesian network K2 MDL MU conditional independence interpretable machine learning misclassification error model selection criteria probabilistic graphical model probabilistic network sampling error statistical uncertainty

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

Computational Systems Biology
Biomedical Data Analysis
Network Inference

Background:

Bayesian network (BN) modeling is crucial in computational systems biology for learning from multiscale biomedical data.
Conventional model selection criteria for BN structure learning suffer from limitations in feature quantification, consistency, and interpretability.
The Minimum model Uncertainty (MU) principle and scoring criterion offer potential solutions to these limitations.

Purpose of the Study:

To develop and refine the MU scoring criterion for enhanced Bayesian network structure learning.
To comprehensively assess the operating characteristics of the refined MU criterion against conventional scoring methods.
To ensure the wide applicability of the MU criterion across diverse biomedical research scenarios.

Main Methods:

The MU criterion was derived considering broad network neighborhood properties.
Misclassification error estimates were used to assess the quality of the learned network structures.
A dedicated statistical relationship model was constructed to support the MU criterion.
Numerical validation and performance assessment on real biomedical data were conducted.

Main Results:

The refined MU criterion demonstrates robustness across various parameters.
It consistently outperforms conventional scoring criteria in terms of accuracy and consistency.
The refined criterion mitigates sensitivity degradation and improves the generalizability of results.
The accompanying statistical model enhances interpretability by providing accuracy/power estimates for dependencies.

Conclusions:

The refined MU criterion offers significant improvements for Bayesian network structure learning in biomedical research.
It enhances the reliability, consistency, and interpretability of data-driven models.
The MU criterion provides a more robust and generalizable approach for analyzing complex biological systems.