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Comparative network stratification analysis for identifying functional interpretable network biomarkers.

Chuanchao Zhang1,2, Juan Liu3, Qianqian Shi2

  • 1State Key Laboratory of Software Engineering, School of Computer, Wuhan University, Wuhan, 430072, China.

BMC Bioinformatics
|April 1, 2017
PubMed
Summary

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This summary is machine-generated.

This study introduces Comparative Network Stratification (CNS), a new bioinformatics tool to identify functional network biomarkers for complex diseases. CNS enhances disease discrimination and provides biological interpretations, aiding precision medicine.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Identifying molecular biomarkers for complex diseases is crucial for precision medicine.
  • Conventional methods like expression-based or network-based approaches often lack biological interpretation.
  • Function-based methods may overlook gene differential information, reducing discriminative ability.

Purpose of the Study:

  • To develop a computational method for identifying functional network biomarkers.
  • To ensure biomarkers possess both strong discriminative power and biological interpretability.
  • To address limitations of existing methods in capturing disease-specific biological functions and network structures.

Main Methods:

  • Developed Comparative Network Stratification (CNS), an integer programming model.
Keywords:
Complex diseaseInteger programmingNetwork biomarkerNetwork stratification

Related Experiment Videos

  • Extracted functional or interpretable network biomarkers.
  • Validated CNS against five state-of-the-art methods on four complex disease datasets.

    • Main Results:

    • CNS enhances the discriminative power of network biomarkers.
    • CNS provides biologically interpretable information and disease pathogenic mechanisms.
    • A case study on type 1 diabetes identified previously disregarded dysfunctional genes and networks.

    Conclusions:

    • CNS is a powerful bioinformatics tool for identifying functional and interpretable network biomarkers.
    • The tool offers both discriminative power for disease states and readable biological function interpretation.
    • CNS is available as a Matlab package.