Functional Distances for Genes Based on GO Feature Maps and their Application to Clustering

Abstract

With the invention of high throughput methods, researchers are capable of producing large amounts of biological data. During the analysis of such data, the need for a functional grouping of genes arises. In this paper, we propose a new functional distance measure for genes and its application to clustering. The proposed distance is based on the concept of empirical feature maps that are built using the Gene Ontology. Besides, our distance function can be calculated much faster than a previous approach. Finally, we show that using this distance function for clustering produces clusters of genes that are of the same quality as in our previous publication. Therefore, it promises to speed up biological data analysis.