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Related Concept Videos

Protein Networks02:26

Protein Networks

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.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

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Related Experiment Video

Updated: Jul 5, 2026

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data
07:11

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Published on: November 10, 2023

Correlation network analysis for data integration and biomarker selection.

Aram Adourian1, Ezra Jennings, Raji Balasubramanian

  • 1BG Medicine Inc., 610N Lincoln Street, Waltham, MA, USA. aadourian@bg-medicine.com

Molecular Biosystems
|April 26, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a novel network approach to identify circulating biomarkers for drug-induced liver injury. By correlating molecular changes in blood and liver, it pinpoints specific biomarkers for lipid metabolism and urea cycle disruptions.

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

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Last Updated: Jul 5, 2026

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data
07:11

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Published on: November 10, 2023

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Biomolecular profiling
  • Toxicology
  • Systems biology

Background:

  • High-throughput omics technologies (transcriptomics, proteomics, metabolomics) are vital for studying xenobiotic effects in vivo.
  • Identifying specific biomarkers in accessible fluids like blood is crucial but challenging due to numerous molecular changes.
  • Current methods struggle to isolate biomarkers linked to specific biological processes.

Purpose of the Study:

  • To develop and present a novel cross-compartment correlation network approach for integrating multi-omics data.
  • To identify circulating biomarkers indicative of drug-induced hepatic toxicity.
  • To select biomarkers specifically related to alterations in lipid metabolism and urea cycle processes.

Main Methods:

  • Integrated proteomic, metabolomic, and transcriptomic data from blood plasma and liver tissue.
  • Employed a correlation network analysis without a priori supervision.
  • Utilized a rodent model (Wistar Hannover rats) administered a toxic compound.

Main Results:

  • Identified numerous statistically significant molecular changes in both plasma and liver.
  • Successfully exploited drug-induced correlations between plasma analytes and liver molecules.
  • Nominated specific plasma molecules as biomarkers for drug-induced hepatic alterations.

Conclusions:

  • The cross-compartment correlation network approach effectively integrates multi-omics data for biomarker discovery.
  • This method enables the identification of circulating biomarkers for specific drug-induced hepatic processes.
  • The findings highlight potential biomarkers for monitoring drug-induced liver toxicity.