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    Area of Science:

    • Computational Biology
    • Causal Inference
    • Machine Learning

    Background:

    • Causal discovery from observational data is vital across many fields.
    • The PC algorithm is a leading method but struggles with high-dimensional data due to its exponential runtime.
    • Modern multi-core processors offer opportunities for parallelization.

    Purpose of the Study:

    • To develop a parallelized PC algorithm (parallel-PC) that is efficient and memory-conscious.
    • To enable causal discovery on high-dimensional datasets using readily available multi-core computers.
    • To improve the speed and accuracy of causal inference, particularly in biological applications.

    Main Methods:

    • Developed parallel-PC, a parallelized implementation of the PC algorithm.
    • Utilized parallel computing techniques suitable for standard multi-core CPUs.
    • Applied parallel-PC to synthetic and real-world high-dimensional datasets, including gene expression data.

    Main Results:

    • parallel-PC significantly reduces runtime compared to the original PC algorithm on high-dimensional data.
    • On a DREAM 5 challenge dataset, parallel-PC finished in under 12 hours (4-core) and 6 hours (8-core), while the original PC algorithm exceeded 24 hours.
    • Integration of parallel-PC into a miRNA-mRNA regulatory relationship inference method improved both efficiency and accuracy.

    Conclusions:

    • parallel-PC offers a practical and efficient solution for causal discovery in high-dimensional settings.
    • The parallelized algorithm democratizes causal discovery by leveraging accessible multi-core hardware.
    • parallel-PC enhances the performance of downstream causal inference tasks, demonstrating its utility in biological research.