A genetic algorithms framework for estimating individual gene contributions in signaling pathways

With the rapid advancements in our data acquisition capabilities and the increased availability of gene interaction databases a variety of pathway analysis tools have been proposed. However, all these methods are dependent on the quality of the available pathways. These pathways were designed to describe the general mechanism of a particular disease or biological process. The known pathways encompass the results of many biological experiments and even though they represent our current understanding of those particular biological processes, they are still generally considered sketchy and incomplete. One piece of information that is generally missing regards the role or importance of a gene in a given pathway which we refer to as the gene contribution. We propose here a method, based on genetic algorithms, to objectively quantify the contribution of each gene. Using a pool of 24 data sets from 12 different conditions divided in train and test groups, we show how an impact pathway analysis method achieves significantly better results with the newly estimated gene contributions when compared with both the initial default contributions, as well as randomly selected gene contributions.