I am a PhD student working in the Computational Biology Group at the Department of Computer Science, University of Warwick. My supervisor is Professor Feng. I finished my undergraduate study in Warwick at 2006 with First class reward.
I primarily work in the field of reverse-engineer approach base upon simultaneously multi-site physiological recording data (spiking, firing rates, local feild potentials, EEG and MEG etc.) There are several well-established reverse-engineer approaches, such as Bayesian network inference, ordinary differencial equations (ODEs), information theory and Granger causality. From our previous study, we found that Granger causality approach, although simple, can be successfully applied to recover the network structure from temporal data and it could play a significant role in systems biology approach.
There is a problem for Granger causality approach applied in large network, since the number of parameters to estimate is polynomially increased with the number of variables. To solve this problem, we proposed a novel approach, called global Granger causality which reconstructing network by using iterative steps. This approach has been successfully applied for real experimental data which has more than 800 proteins recorded.
Recently, we focused on the some experimental data. After doing some data analysis, we found some very interesting phonomenon which could help us to understand more about biology mechanisim.
Zou CL, Feng J.F. (2009) Granger causality vs. Dynamic Bayesian network inference: A Comparative Study BMC Bioinformatics vol. 10:122 doi:10.1186/1471-2105-10-122. ( most viewed paper in past 30 days in the journal, flagged as 'highly accessed paper', IF=3.8)