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Multiscale part mutual information for quantifying nonlinear direct associations in networks.

Junliang Shang1, Jing Wang1, Yan Sun1

  • 1School of Computer Science, Qufu Normal University, Rizhao 276826, China.

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A new method, multiscale part mutual information (MPMI), effectively quantifies nonlinear direct associations in complex networks. This advancement aids network construction in data mining, particularly for systems with multiscale associations.

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Area of Science:

  • Computational biology
  • Network analysis
  • Data mining

Background:

  • Network construction is vital for network-assisted data mining.
  • Accurate quantification of direct variable associations is essential for network construction.
  • Multiscale and nonlinear associations pose challenges to existing quantification methods.

Purpose of the Study:

  • To develop a novel method for quantifying nonlinear direct associations in networks with multiscale associations.
  • Introduce multiscale part mutual information (MPMI) as a robust measure.

Main Methods:

  • Defined multiscale part mutual information (MPMI) theoretically and derived its properties.
  • Conducted experiments on simulated and real-world datasets (glioblastoma, lung adenocarcinoma).
  • Compared MPMI with existing methods like part mutual information (PMI) and nonlinear partial association (NPA).

Main Results:

  • MPMI effectively quantifies nonlinear direct associations, especially in networks with multiscale associations.
  • Validated through theoretical analysis, simulations, and application to biological datasets.
  • Demonstrated superior performance compared to PMI, NPA, and conditional mutual information in specific contexts.

Conclusions:

  • MPMI offers an effective alternative for quantifying nonlinear direct associations in biological networks.
  • The method is particularly valuable for analyzing complex systems with multiscale interactions.
  • Source code is publicly available for broader adoption and research.