An Evolving Neural Network for the Interpretation of Gene Expression Patterns

Abstract

It is of central interest in biology to understand how gene activity networks are coordinated and integrated in the cell. Within the field of genomics, microarray technologies have become a powerful technique for monitoring simultaneously the expression patterns of thousands of genes under different sets of conditions. A main task now is to propose analytical methods that can suggest which groups of genes are activated by similar conditions. We review several techniques based on self-organizing map and clustering algorithms but implemented through a network of units controlled by biologically inspired functions (see Table 1). The computer tool, named NBIA, permits a categorization that generates a set of gene groups with coordinated expression patterns.