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 ofgenes) with experimental data (gene expression profiles) in creating an overall dissimilarity that can be used with anyclustering algorithm that uses a general dissimilarity matrix. We explore this idea with two publicly available geneexpression data sets and functional annotations where the results are compared with the clustering results that uses only theexperimental data. Although more elaborate evaluations might be called for, the present paper makes a strong case forutilizing existing biological information in the clustering process.

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