Exploring Biomarker Relationships in Both Type 1 and Type 2 Diabetes Mellitus Through a Bayesian Network Analysis Approach
<|reference_start|>Exploring Biomarker Relationships in Both Type 1 and Type 2 Diabetes Mellitus Through a Bayesian Network Analysis Approach: Understanding the complex relationships of biomarkers in diabetes is pivotal for advancing treatment strategies, a pressing need in diabetes research. This study applies Bayesian network structure learning to analyze the Shanghai Type 1 and Type 2 diabetes mellitus datasets, revealing complex relationships among key diabetes-related biomarkers. The constructed Bayesian network presented notable predictive accuracy, particularly for Type 2 diabetes mellitus, with root mean squared error (RMSE) of 18.23 mg/dL, as validated through leave-one-domain experiments and Clarke error grid analysis. This study not only elucidates the intricate dynamics of diabetes through a deeper understanding of biomarker interplay but also underscores the significant potential of integrating data-driven and knowledge-driven methodologies in the realm of personalized diabetes management. Such an approach paves the way for more custom and effective treatment strategies, marking a notable advancement in the field.<|reference_end|>
@article{sun2024exploring,
    title={Exploring Biomarker Relationships in Both Type 1 and Type 2 Diabetes
  Mellitus Through a Bayesian Network Analysis Approach},
    author={Yuyang Sun, Jingyu Lei, Panagiotis Kosmas},
    journal={arXiv preprint arXiv:2406.17090},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.17090},
    primaryClass={q-bio.QM cs.AI cs.CE cs.LG}
}