Title

Incorporation of biological knowledge into distance for clustering genes

Authors

Grzegorz M Boratyn^1*, Susmita Datta² and Somnath Datta² 

Affiliation

¹Clinical Proteomics Center, University of Louisville, Louisville, KY 40202; ^2,3Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY - 40202

Email

greg.boratyn@louisville.edu; * Corresponding author

Article Type

Prediction Model

Date

      received December 09, 2006; accepted January 20, 2007; published online April 10, 2007

Abstract

In this paper we propose a data based algorithm to marry existing biological knowledge (e.g., functional annotations of genes) with experimental data (gene expression profiles) in creating an overall dissimilarity that can be used with any clustering algorithm that uses a general dissimilarity matrix. We explore this idea with two publicly available gene expression data sets and functional annotations where the results are compared with the clustering results that uses only the experimental data. Although more elaborate evaluations might be called for, the present paper makes a strong case for utilizing existing biological information in the clustering process.   

Availability

Supplement is available at http://www.somnathdatta.org/Supp/Bioinformation/appendix.pdf

Keywords

knowledge; distance; clustering; genes; expression

Citation

Boratyn et al., Bioinformation 1(10): 396-405 (2007)

Edited by

Susmita Datta

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.