Speaker: Jian Peng, University of Illinois at Urbana-Champaign
Abstract: High-throughput techniques in proteomics and biotechnology have been creating ever-expanding repositories of experimental interaction data. However, the large-scale, heterogeneous, noisy and high-dimensional nature of such data presents computational and statistical challenges for integration, modeling and predictive analytics. Moreover, high-throughput experiments have been mostly performed in model organisms rather than in human. Approaches that translate discoveries from model organisms to human will significantly accelerate the process of drug discovery and development. In this talk, I will present two new computational approaches for large-scale network integration and cross-species comparison, and show that they can be applied to predict gene function and reveal hidden biological processes connecting diverse neurodegenerative genes. We envision multi-faceted, cross-species approaches will continue to yield important insights into many complex diseases with evolutionarily conserved biological mechanisms, and perhaps help fulfill therapeutic promises in the post-genomics era.