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Constrained Multi-Objective Optimization-Based Temporal Network Observability for Biomarker Identification of

Kangjia Qiao, Jing Liang, Wei-Feng Guo

    IEEE Journal of Biomedical and Health Informatics
    |July 30, 2024
    PubMed
    Summary

    This study introduces a novel model for identifying disease biomarkers using personalized gene networks. The approach enhances biomarker discovery by considering network observability and prior knowledge, improving diagnostic accuracy.

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    Area of Science:

    • Computational Biology
    • Systems Biology
    • Bioinformatics

    Background:

    • Personalized gene interaction networks are crucial for disease diagnosis.
    • Existing biomarker identification methods often overlook prior biomarkers and system observability.

    Purpose of the Study:

    • To propose a new constrained multi-objective optimization-based temporal network observability model (CMTNO) for identifying biomarkers.
    • To address limitations of existing methods by incorporating prior biomarkers and ensuring network observability.

    Main Methods:

    • Developed a constrained multi-objective optimization model (CMTNO) with two objectives: minimizing selected nodes and maximizing prior nodes, while ensuring network observability.
    • Designed an experience learning-based constrained multi-objective evolutionary algorithm to solve CMTNO problems, incorporating experience from previous tasks.
    • Utilized a two-step neighbor-based connectivity method to enhance archive effectiveness.

    Main Results:

    • The proposed CMTNO model and algorithm were evaluated on three types of cancer patient data.
    • Results demonstrated the effectiveness of the model and algorithm in identifying relevant biomarkers.
    • The experience learning component improved optimization efficiency for new patients.

    Conclusions:

    • The CMTNO model offers an effective approach for biomarker discovery from personalized gene networks.
    • The integration of network observability and prior biomarkers enhances diagnostic potential.
    • The developed evolutionary algorithm shows promise for complex biological network optimization problems.