Our research is focused on the interface of computer science, statistics, and biological sciences, more specifically (but not limited to) the identification and analysis of connectivity; the development of formal statistical methods in graphs, and the application of them in molecular (omics) and brain imaging (fMRI/sMRI) data to better understand several human diseases/disorders.
To determine the behavior of a biological system, it is important to understand the way each component of the system interacts with the others (connectivity). The increasing amounts of data generated by high-throughput quantification methods aid the determination of those interactions. One of our research topics consists in the identification of connectivity based on concepts of statistical dependence and Granger causality (information flow). By using these approaches, we model gene regulatory and functional brain networks.
The study of the structure of those networks provides important information about how biological systems evolve in time and the potential causes of malfunctioning (the abnormal regulations observed in unhealthy networks when compared to controls).