Exploring interaction measures to identify informative pairs of genes.

Microarray experiments can be used for simultaneous expression of thousands of genes in various conditions. Data from these experiments are used to identify the gene involved in a particular biological phenomenon. Most current methods for such analysis assume that genes are independent. We explored the interaction between genes to identify informative gene pairs. This was based on measuring the interaction information using the information theory. We show that there are two kinds of gene interaction, redundancy and synergy. We analysed these interactions to construct a network of redundancy and conducted a functional analysis of synergic components on two public datasets.