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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: Oct 16, 2025

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

Extended graphical lasso for multiple interaction networks for high dimensional omics data.

Yang Xu^1,2, Hongmei Jiang², Wenxin Jiang²

¹Zhongtai Securities Institute for Financial Studies, Shandong University, Jinan, Shandong, China.

Plos Computational Biology

|October 20, 2021

Summary

This summary is machine-generated.

We introduce the extended joint hub graphical lasso (EDOHA), a new method for analyzing multiple high-dimensional omics data. EDOHA excels at identifying class-specific hubs in biological networks.

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Last Updated: Oct 16, 2025

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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

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Published on: August 21, 2019

Area of Science:

Bioinformatics
Computational Biology
Network Science

Background:

Association networks, such as genetic interaction networks, are crucial in biological and medical research.
High-dimensional omics data across multiple classes require advanced methods for network estimation.

Purpose of the Study:

To propose a novel method, the extended joint hub graphical lasso (EDOHA), for estimating multiple related interaction networks.
To address the challenge of analyzing high-dimensional omics data and compositional data.

Main Methods:

Developed a convex penalized log-likelihood optimization problem.
Utilized an alternating direction method of multipliers (ADMM) algorithm for solving the optimization problem.
Adapted the method for high-dimensional compositional data, including microbial interaction networks.

Main Results:

Simulated studies demonstrated EDOHA's superior performance in recognizing class-specific hubs compared to existing methods.
Applications to three real datasets provided biological interpretations consistent with previous findings.

Conclusions:

EDOHA offers a powerful and adaptable approach for estimating multiple interaction networks from high-dimensional data.
The method enhances understanding of underlying mechanisms in disease by revealing class-specific network structures.