The Utility of Sequence, Function and Transcriptional Information in Gene Expression Profiling *

As the determination of the DNA sequences comprising the genome of various organisms came to completion or, nears completion, a shift from static structural genomics to dynamic functional genomics is taken place. In this paper we focus on functional genomics and in particular on the analysis of microarray data. Microarray or, gene-expression data analysis is heavily depended on Gene Expression Data Mining (GEDM) technology, and in the very-last years a lot of research efforts are in progress. GEDM is used to identify intrinsic patterns and relationships in gene expression data. The identification of patterns in complex gene expression datasets provides two benefits: (i) generation of insight into gene regulation, and (ii) characterization of multiple gene expression profiles in complex biological processes, e.g. pathological states [8]. GEDM activities are based on two approaches: (a) hypothesis testingto investigate the induction or perturbation of a biological process that leads to predicted results, and (b) knowledge discoveryto detect internal structure in biological data [1, 13]. In this paper we present an integrated methodology that combines both. It is based on a hybrid clustering approach able to compute and utilize different distances (or, similarities) between the objects to be clustered. In this respect the whole exploratory data analysis process becomes more knowledgeable in the sense that pre-established domain-knowledge is used to guide the clustering.