Satoru Miyano, University of Toyko
"Gene Networks for Drug Target Gene Discovery"

Uses knockout/knockdowns and microarray data and bayesian network and nonparametric regression to establish gene links. Optimize parameters to an approximating function using both microarray and the DAG of the bayesian network.
Data set of 521 genes from 120 micrarrays each with about 1800 genes each. To compute the gene links uses a heck of alot of computing power! looks like ~800 processors at around $10 million.
Mentions a lack of papers on the layout of biological networks (for interpretation of results, etc)
Also has developed a generic method for incorporating other biological knowledge into network estimation. This is done by assigning a prior to links according to the Gibbs distribution. Also developed a method for incorporating p-p interactions into this framework.

When evaluating the optimal networks a single network is not biologically relevant. By compiling many optimal networks together you get biological links reinforced. I actually like this because ive heard one of the big problems in network construction is that there are many network configurations that explain the phenotypes equally well.

Did huge experiment knockdown 270 human genes by siRNA. To look at hypertension drug fenofibrate. Many of the targets recovered were known drug targets. More are being investigated with GNI.