Comparison of the Data-based and Gene Ontology-Based Approaches to Cluster Validation Methods for Gene Microarrays

Authors:
Nadia Bolshakova;Anton Zamolotskikh;Padraig Cunningham
Affiliations:
Trinity College Dublin, Ireland;Trinity College Dublin, Ireland;Trinity College Dublin, Ireland
Venue:
CBMS '06 Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems
Year:
2006

Citing 0
Cited 2

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Proximity Measures for Clustering Gene Expression Microarray Data: A Validation Methodology and a Comparative Analysis

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)

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Abstract

The paper presents a comparison of the data-based and Gene Ontology (GO)-based approaches to cluster validation methods for gene microarray analysis. We apply a homogeneous approach to obtaining metrics from different GO-based similarity measures and a normalization of validation index values, that allows us to compare them to each other as well as to databased validation indices. The results show strong correlation between both GO-based and data-based validation indices. The results suggest that this may represent an effective tool to support biomedical knowledge discovery tasks based on gene expression data.