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Finding Correlated Patterns via High-Order Matching for Multiple Sourced Biological Data.

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    This study introduces a novel model for discovering correlated patterns in complex genomic data, aiding in personalized disease diagnosis and treatment strategies.

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    Area of Science:

    • Genomics
    • Bioinformatics
    • Computational Biology

    Background:

    • Multidimensional genomic data present analysis challenges due to varying scales and units.
    • Integrating multiple data sources is crucial for understanding biological interactions.

    Purpose of the Study:

    • To develop a model for discovering correlated patterns in multidimensional genomic data.
    • To incorporate prior biological knowledge into the analysis framework.

    Main Methods:

    • A correlated pattern discovery model using tensor similarity to measure pattern correlations.
    • Integration of prior knowledge as constraints within the model.
    • Development of efficient numerical solutions for analysis.

    Main Results:

    • The model demonstrates robust and effective performance on both simulated and real biological datasets.
    • Experiments on five cancer datasets successfully identified distinct cancer subtypes.
    • Survival analysis validated the clinical relevance of the identified subtypes.

    Conclusions:

    • The proposed model effectively integrates prior knowledge for correlated pattern discovery in genomic data.
    • This approach holds significant potential for advancing personalized diagnostics in diseases like cancer.