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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Identifying disease-associated biomarker network features through conditional graphical model.

Shanghong Xie1, Xiang Li2, Peter McColgan3

  • 1Department of Biostatistics, Mailman School of Public Health, Columbia University, New York.

Biometrics
|December 19, 2019
PubMed
Summary
This summary is machine-generated.

Biomarker networks vary between individuals and can predict disease outcomes. This study developed a method to analyze these networks, finding cortical connections and subcortical volumes predict motor function decline in Huntington's disease.

Keywords:
Huntington's diseasegraphical modelgray matter networkmediation analysisregularized regression

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Area of Science:

  • Neuroscience
  • Biostatistics
  • Genetics

Background:

  • Biomarkers form networks with connection strengths varying by individual covariates like genetic variants.
  • Subject-specific network variations can predict clinical disease outcomes.
  • Understanding these network dynamics is crucial for disease research.

Purpose of the Study:

  • To develop a two-stage method for estimating biomarker networks that account for subject heterogeneity.
  • To evaluate the association between network measures and clinical disease outcomes.
  • To investigate the impact of the Huntington's disease (HD) causal gene on brain gray matter atrophy connections.

Main Methods:

  • A conditional Gaussian graphical model was used in the first stage to estimate covariate-dependent networks.
  • The second stage evaluated the clinical utility of network measures, assessing their predictive power for clinical impairment.
  • The method was validated through simulations and applied to Huntington's disease (HD) data.

Main Results:

  • Cortical network connections and subcortical volumes, not subcortical connections, were identified as predictive of clinical motor function deterioration in HD.
  • These findings were validated in an independent HD study.
  • Similar patterns were observed in gray matter and white matter connectivity studies, suggesting shared biological mechanisms in HD.

Conclusions:

  • The proposed method effectively estimates subject-specific biomarker networks and their clinical relevance.
  • Cortical network alterations and subcortical volume changes are key indicators of motor decline in HD.
  • Findings support a shared biological basis for gray and white matter changes in HD, potentially linked to neuronal loss.