What can we learn from metabolic networks?

Mario Guarrcino, High Performance Computing and Networking Institute of the Italian National Research Council. Part of the Leeds Applied Nonlinear Dynamics seminar series.

Networks represent a convenient model for many scientific and technological problems. From power grids to biological processes and functions, from financial networks to chemical compounds, the representation of case studies with graphs enables the possibility to highlight both topological and qualitative characteristics.

In this talk, we report recent developments in supervised classification of data in form of networks. Given two or more classes whose members are networks, we build a mathematical model to classify them. We focus on networks with labeled nodes and weighted edges, defining distances between networks and building a supervised classification model. We detail the graphical model selection process and provide empirical results on datasets of biological interest.