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Related Experiment Video

Updated: Sep 9, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

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Weighted overlapping group lasso for integrating prior network knowledge into gene set analysis.

Dan Huang¹, Geunsu Jo¹, Kipoong Kim²

¹Department of Statistic, Pusan National University, Busan, 46241, Korea.

BMC Bioinformatics

|September 1, 2025

Summary

This study introduces a novel computational method for gene set analysis that leverages network structures to identify subtle gene expression changes. The new approach effectively detects cancer-related pathways missed by existing methods, improving biological discovery.

Keywords:

Gene expression data Gene set analysis Genetic network Network-based regularization Overlapping group lasso

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Last Updated: Sep 9, 2025

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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

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Published on: October 19, 2021

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Gene set analysis identifies differentially expressed genes between experimental conditions, often using gene regulatory networks.
Current statistical methods overlook network structures, failing to detect subtle or sparse differential gene expression signals.
This limitation hinders the identification of complex biological pathways regulated by small numbers of key genes.

Purpose of the Study:

To develop a novel computational method for gene set analysis that integrates prior network knowledge.
To improve the detection of gene sets with sparse differential expression signals by utilizing gene network structures.
To enhance the identification of biologically relevant pathways in complex datasets like The Cancer Genome Atlas.

Main Methods:

A new method combining network-based regularization with overlapping group lasso is proposed.
Network-based regularization enhances association signals among linked genes.
Overlapping group lasso facilitates the selection of relevant gene sets, incorporating network information as weights (weighted overlapping group lasso - wOGL).

Main Results:

Extensive simulations demonstrate the proposed method's superior performance compared to existing approaches.
Application to The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) data successfully identified significant cancer-related pathways.
These pathways were previously undetected by conventional gene set analysis methods.

Conclusions:

The weighted overlapping group lasso (wOGL) method effectively utilizes prior network information for gene set analysis.
wOGL enhances the identification of gene sets containing differentially expressed genes, especially those with sparse signals.
This approach offers a powerful tool for discovering complex regulatory pathways in genomic data.