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MGT-SM: A Method for Constructing Cellular Signal Transduction Networks.

Min Li, Ruiqing Zheng, Yaohang Li

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |May 26, 2017
    PubMed
    Summary

    We developed MGT-SM, a novel multivariate Granger test framework to build cellular signal transduction networks. This method effectively infers biological pathways even with limited time-series data, outperforming existing approaches.

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    Area of Science:

    • Systems Biology
    • Computational Biology
    • Network Inference

    Background:

    • Cellular signal transduction networks are crucial for understanding biological responses and disease mechanisms.
    • Network inference methods, particularly Granger causality tests, are vital for constructing these networks.
    • Existing multivariate Granger tests require more time points than network nodes, limiting their application to many real-world datasets.

    Purpose of the Study:

    • To propose a new multivariate Granger test-based framework (MGT-SM) for constructing cellular signal transduction networks.
    • To address the challenge of inferring networks from time-series data with fewer time points than nodes.
    • To evaluate the performance of MGT-SM against established methods.

    Main Methods:

    • Developed the MGT-SM framework utilizing Singular Value Decomposition (SVD) for coefficient matrix computation.
    • Employed Monte Carlo simulation to assess the statistical significance of directed edges in inferred networks.
    • Applied MGT-SM to the Yeast Synthetic Network and MDA-MB-468 datasets.

    Main Results:

    • MGT-SM demonstrated superior performance in cellular signal transduction network construction.
    • Evaluations based on recall and Area Under the Curve (AUC) showed MGT-SM outperforming CGC2SPR, PGC, and DBN.
    • The framework successfully inferred networks despite limitations in time-series data length.

    Conclusions:

    • MGT-SM provides a robust and effective approach for cellular signal transduction network inference, especially with limited time-series data.
    • The proposed method advances the field of computational biology by overcoming common data limitations.
    • MGT-SM offers a valuable tool for studying biological activities and disease mechanisms.