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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
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Part mutual information for quantifying direct associations in networks.

Juan Zhao1, Yiwei Zhou2, Xiujun Zhang1

  • 1Key Laboratory of Systems Biology, Innovation Center for Cell Signaling Network, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of the Chinese Academy of Sciences, Shanghai 200031, China;

Proceedings of the National Academy of Sciences of the United States of America
|April 20, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces Part Mutual Information (PMI) to accurately identify direct, nonlinear variable dependencies in complex networks. PMI overcomes limitations of existing methods, enabling better network reconstruction in biology and beyond.

Keywords:
conditional independenceconditional mutual informationnetwork inferencesystems biology

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Area of Science:

  • Network analysis and reconstruction
  • Computational biology
  • Data science

Background:

  • Identifying direct variable dependencies is crucial for network and causal relation reconstruction in science.
  • Partial correlation captures only linear associations, missing nonlinear relationships.
  • Conditional Mutual Information (CMI) detects nonlinear dependencies but suffers from underestimation, limiting its application.

Purpose of the Study:

  • To introduce a novel concept, partial independence, and its measure, Part Mutual Information (PMI).
  • To address the underestimation problem of CMI in quantifying nonlinear direct associations.
  • To retain the quantification properties of both Mutual Information (MI) and CMI.

Main Methods:

  • Definition of Part Mutual Information (PMI) to measure nonlinear direct dependencies.
  • Derivation of the relationships between PMI, MI, and CMI.
  • Validation using simulated benchmark datasets and real gene expression data from E. coli and yeast.

Main Results:

  • PMI successfully quantifies nonlinear direct associations, overcoming CMI's underestimation issues.
  • PMI demonstrates superior performance in accurately identifying direct dependencies in simulated data.
  • Application to gene expression data enabled effective reconstruction of gene regulatory networks.

Conclusions:

  • Part Mutual Information (PMI) offers a robust method for quantifying nonlinear direct dependencies.
  • PMI enhances the accuracy of network reconstruction, particularly in biological systems.
  • The proposed method provides a valuable tool for analyzing complex relationships in various scientific domains.