Incorporation of biological knowledge into distance for clustering genes.
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.


BACKGROUND
Supplement is available at www.somnathdatta.org/Supp/Bioinformation/appendix.pdf.
Boratyn
Grzegorz M
GM
Clinical Proteomics Center, University of Louisville, Louisville, KY 40202, USA. greg.boratyn@louisville.edu
Datta
Susmita
S
Datta
Somnath
S