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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

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Robust de novo pathway enrichment with KeyPathwayMiner 5.

Nicolas Alcaraz¹, Markus List², Martin Dissing-Hansen³

¹Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark; Department of Cancer and Inflammation Research, Institute of Molecular Medicine, University of Southern Denmark, 5000 Odense, Denmark.

|August 20, 2016

Summary

This study introduces KeyPathwayMiner for de novo pathway enrichment, identifying active biological pathways from interaction networks. It assesses pathway robustness against network changes and evaluates enrichment against gold standards.

Keywords:

Pathway enrichment algorithms data integration network analysis systems biology

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Area of Science:

Computational systems biology
Bioinformatics
Network analysis

Background:

De novo pathway enrichment is a key challenge in computational systems biology.
Identifying functional modules and active pathways from large biological networks is crucial.
Existing methods require robust evaluation against evolving interaction networks.

Purpose of the Study:

To introduce KeyPathwayMiner for de novo pathway enrichment.
To enable the study of pathway extraction robustness under network perturbations.
To provide a method for evaluating pathway enrichment against gold standards.

Main Methods:

Utilizing KeyPathwayMiner to extract connected subnetworks from biological interaction networks and molecular profiles.
Implementing network perturbation techniques to assess the robustness of extracted pathways.
Comparing extracted pathways against gold-standard gene sets and randomized networks.

Main Results:

KeyPathwayMiner effectively extracts subnetworks enriched for differentially active entities.
The tool allows for systematic analysis of pathway robustness across various network modifications.
Evaluation against gold standards demonstrates the biological relevance of extracted pathways.

Conclusions:

KeyPathwayMiner is a valuable tool for de novo pathway enrichment and network analysis.
The robustness analysis provides critical insights into the reliability of identified pathways.
This approach enhances the discovery of functional modules in complex biological systems.