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    Area of Science:

    • Machine Learning
    • Biostatistics
    • Computational Biology

    Background:

    • Multitask learning shows promise in survival analysis.
    • A key challenge is varying task and instance learnability.
    • Improving survival analysis models requires addressing these complexities.

    Purpose of the Study:

    • To propose an asymmetric graph-guided multitask learning approach with self-paced learning for survival analysis.
    • To enhance learning performance by identifying task structures and instance/task complexities.
    • To enable progressive learning from easy to hard instances and tasks.

    Main Methods:

    • Asymmetric graph-guided regularization to facilitate knowledge transfer between tasks.
    • Self-paced learning strategy for progressive model training.
    • Integration of these methods to handle complex survival analysis tasks.

    Main Results:

    • The proposed model identifies complex task structures and instance complexities.
    • Asymmetric regularization enables effective knowledge transfer across tasks.
    • Progressive learning from easy to hard items improves model training.

    Conclusions:

    • The proposed method significantly improves survival analysis performance.
    • Experimental results demonstrate higher prediction accuracies than state-of-the-art methods.
    • The approach is effective on both synthetic and real-world TCGA data.