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Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

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Hybrid coexpression link similarity graph clustering for mining biological modules from multiple gene expression

Saeed Salem¹, Cagri Ozcaglar²

¹Department of Computer Science, North Dakota State University, Fargo, ND 58102, USA.

|September 16, 2014

Summary

Integrating multiple gene expression datasets aids protein functional annotation. Our joint mining algorithm identifies functionally homogeneous biological modules by clustering coexpression links in a hybrid similarity graph.

Keywords:

Biological networks Coexpression Frequent subnetworks Hybrid similarity Mining

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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:

Genomics
Bioinformatics
Computational Biology

Background:

Genomic technologies generate vast gene expression datasets across species and conditions.
Integrating these datasets overcomes limitations of single-dataset analysis for protein annotation and module discovery.
Single-dataset approaches often yield spurious coexpression, hindering accurate biological insights.

Purpose of the Study:

To develop a novel joint mining algorithm for integrating multiple gene expression datasets.
To improve protein functional annotation and biological module discovery.
To address the challenge of spurious coexpression in individual datasets.

Main Methods:

A joint mining algorithm constructs a weighted hybrid similarity graph where nodes represent coexpression links.
Edge weights in the graph combine topological and co-appearance similarities between coexpression links.
Clustering of the weighted hybrid similarity graph identifies recurrent coexpression link clusters (modules).

Main Results:

The algorithm successfully identifies recurrent coexpression link clusters (modules).
Experimental results on human gene expression datasets demonstrate functional homogeneity of the identified modules.
Enrichment analysis confirms modules are associated with Gene Ontology (GO) terms and KEGG pathways.

Conclusions:

The proposed joint mining algorithm effectively integrates multi-species gene expression data.
This approach enhances the accuracy of protein functional annotation and biological module discovery.
The identified modules exhibit significant functional coherence, validated by GO and KEGG pathway enrichment.