The application of complex network analysis has generated deep insights into the structure of social, technological, and biological systems. In our group, we develop general methods and algorithms for the study of networks. We apply a wide variety of approaches to primarily study biological systems.
Gene co-expression analyses have been a valuable tool to investigate cells and tissues at the systems level, finding that co-expression patterns are related to biological pathways and protein-interactions, as well as dysfunctional gene-regulation in cell and tissue disease-states. These analyses typically generate networks that reflect the relationship between the biological units (e.g. transcripts or protein abundance) that are measured.
In our group, we have developed multiple methods for generating and analyzing both co-expression networks and differential co-expression networks. We have also conducted analyses to investigate the relationship between data quality / quantity and the fidelity of the resulting networks. In our work, we are not focused on a particular disease or experimental system.