9.4 Summary and Conclusions

In this study, we proposed a network-based information synergy approach to identify candidate genes involved in AD. Results obtained from simulation data and AD microarray data suggested that, information synergy, particularly positive information synergy, could identify gene pairs with specific joint expression patterns that would otherwise be overlooked by differential and correlation analyses. Meanwhile, some of the hub genes in the PPI subnetworks, consisting of positive information synergy interactions, show biological relevance to the pathogenesis of AD, suggesting that the networks obtained through information synergy could potentially identify genes involved in diseases. Moreover, the information theory used in synergy analysis allows one to capture gene pairs with different types of relationship (either linear or nonlinear), as long as the gene pairs provide additional information on the phenotype. This advantage renders information synergy particularly attractive for capturing gene pairs in complex scenarios, where the interactions between genes are not linear. Taken together, information synergy is a promising complementary approach to network-based studies.

The concept of information synergy has been applied to identify gene pairs predictive of phenotypes based upon microarray data. The synergistic gene pairs inferred from microarray data may not necessarily interact with each other physically. This makes it difficult to interpret the biological roles of individual gene pairs, especially when the structural or functional information is not available for many genes. This issue is of less concern when PPI data is incorporated with the gene expression data as physical interactions in the network, which can help facilitate the interpretation of the functions of the genes of interest, that is, by inferring the function of the genes through their neighbors in the network.

We have demonstrated that information synergy provides results complementary to existing approaches in network-based studies. Previous network-based studies have shed much light onto the biochemical features of the genes and proteins that show changes in correlation across phenotypes, that is, smaller binding domain size or enriched in signaling domains, and so on [53]. Exploring such structural characteristics of the genes/proteins with positive information synergy could provide insight on their general mechanisms and further help elucidate the molecular mechanisms underlying diseases. In this chapter, we exemplified the potential role of the synergy network in identifying genes of interest for AD by investigating the hub genes in a positive synergy subnetwork. In future, further exploration of the synergy network, that is, enrichment analysis to identify canonical pathways over-represented in the networks, pathway–gene association analysis to reveal the genes associated with specific pathways, or modular analysis to characterize the network modules with specific patterns in terms of information synergy (i.e., do the connections within a module have exclusively positive or negative synergy), and so on would likely provide more information on potential disease mechanisms.