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Functional identification in correlation networks using gene ontology edge annotation.

Kathryn Dempsey¹, Ishwor Thapa, Dhundy Bastola

¹College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE 68182-0016, USA.

International Journal of Computational Biology and Drug Design

|September 28, 2012

Summary

This study introduces a novel method to improve correlation networks using Gene Ontology (GO) annotations. This approach enhances the identification of causative biological relationships and essential genes within complex datasets.

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Area of Science:

Bioinformatics
Systems Biology
Computational Biology

Background:

Correlation networks are used to infer biological mechanisms from temporal data.
Distinguishing causative relationships from coincidental associations in these networks remains a challenge.

Purpose of the Study:

To develop a method for enhancing causative relationships in correlation networks using Gene Ontology (GO) annotations.
To reduce network size while conserving biologically relevant signals.

Main Methods:

Annotating network edges based on the shortest path between elements.
Utilizing the position of the deepest common parent in the GO tree for annotation.
Enriching correlation networks with GO functional information.

Main Results:

The proposed method generates network structures enriched in GO functions.
Network size is reduced while preserving relevant biological signals.
Functional relationships are highlighted, enabling identification of biologically relevant clusters and essential genes.

Conclusions:

The enhanced correlation networks provide functional insights beyond traditional statistical analysis.
This method aids in uncovering true biological functions and identifying key genes within biological networks.