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Protein Networks

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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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Related Experiment Video

Updated: May 5, 2026

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

Protocol for constructing correlation-based molecular networks from large-scale untargeted metabolomics data.

Huang Lin^1,2, Lijun Zhang³, Ali Lotfi⁴

¹Department of Epidemiology and Biostatistics, University of Maryland, College Park, MD 20742, USA.

Biorxiv : the Preprint Server for Biology

|May 4, 2026

Summary

This summary is machine-generated.

This study presents MetVAE, a computational method for building molecular networks from metabolomics data. It reveals functional links between metabolites, uncovering a potential "auto-brewery" pathway in liver cancer.

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Last Updated: May 5, 2026

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Published on: October 19, 2021

Area of Science:

Metabolomics
Network Biology
Computational Biology

Background:

Untargeted metabolomics generates complex datasets.
Understanding metabolite functional relationships is crucial.
Existing methods like spectral similarity networks have limitations.

Purpose of the Study:

To introduce MetVAE, a computational framework for constructing correlation-based molecular networks.
To capture functional relationships in metabolomics data beyond spectral similarity.
To provide a workflow for data preprocessing, correlation estimation, and network visualization.

Main Methods:

Utilized MetVAE, a variational autoencoder-based framework.
Imported metabolomics features and sample metadata.
Applied data adjustment for compositionality, missingness, confounding, and high-dimensionality.
Estimated sparse metabolite correlations.
Exported data in GraphML format for network visualization.

Main Results:

Successfully constructed correlation-based molecular networks from untargeted metabolomics data.
Identified functional relationships reflected in cross-sample metabolite correlations.
In a hepatocellular carcinoma mouse model, linked lipid classes in high-fat-diet animals.
Suggested an endogenous "auto-brewery" route contributing to lipotoxic metabolites.

Conclusions:

MetVAE offers a complementary approach to spectral similarity for molecular network construction.
The framework effectively processes and analyzes complex metabolomics data.
The findings in the mouse model highlight a novel potential metabolic pathway in liver cancer development.