ABSTRACT
This text has focused so far on the numerical analysis of the microarray data and the nitty-gritty of the number crunching. However, the ultimate purpose of the gene expression experiments is to produce biological knowledge, not numbers.1 Independently of the methods used, the result of a microarray experiment is, in most cases, a set of genes found to be differentially expressed between two or more conditions under study. The challenge faced by the researcher is to translate this list of differentially regulated genes into a better understanding of the biological phenomena that generated such changes. Although techniques aimed at this goal have started to appear (e.g., inferring
etc.) such approaches are very difficult and challenged by the amount of noise in the data. However, a good first step in this direction is the translation of the list of differentially expressed genes into a functional profile able to offer insight into the cellular mechanisms acting in the given condition. Even if our information about the genes were complete and accurate, the mapping of lists of tens or hundreds of differentially regulated genes to biological functions, molecular functions, and cellular components is not a trivial matter.
