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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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System-wide peripheral biomarker discovery using information theory.

Gil Alterovitz1, Michael Xiang, Jonathan Liu

  • 1Division of Health Sciences and Technology, Harvard University/Massachusetts Institute of Technology, Cambridge, MA, USA.

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This study introduces a novel framework to discover peripheral biomarkers by integrating tissue and biofluid data, enabling non-invasive biological insights and guiding future biomedical research.

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Area of Science:

  • Biomarker Discovery
  • Systems Biology
  • Bioinformatics

Background:

  • Current methods for accessing biological information often rely on invasive procedures.
  • Traditional biomarker discovery approaches analyze tissue and biofluid markers separately.
  • There is a need for non-invasive methods to obtain diagnostic and prognostic biological information.

Purpose of the Study:

  • To develop an information-theoretic framework for integrated biomarker discovery.
  • To identify tissue-specific information within peripheral biofluids.
  • To quantitatively assess the correspondence between biofluids and tissues.

Main Methods:

  • Utilized an information-theoretic framework to model tissue-biofluid interactions as an information channel.
  • Analyzed 26 proteomes from 45 sources, mapping them onto phenotype, function, and drug space.
  • Employed relative entropy calculation to determine biofluid-tissue correspondence.
  • Identified candidate biomarkers and biofluids based on functional information transfer (p < 0.01).
  • Validated findings using gene expression data.

Main Results:

  • Identified 851 unique candidate biomarker proxies significantly associated with specific tissues (p < 0.001).
  • Demonstrated that biofluid proteins can serve as functional tissue proxies.
  • Enhanced biomarker discovery by filtering for significant tissue-biofluid information channels.
  • Discovered novel candidate biomarkers with potential for future exploration.
  • Validated the framework's predictions using gene expression data.

Conclusions:

  • The developed framework successfully integrates biofluid and tissue data for biomarker discovery.
  • Peripheral biofluids contain significant information about tissue states, enabling non-invasive assessment.
  • This approach provides a roadmap for biomedical investigations, aiding in disease screening, diagnosis, and protein function studies.