Analysis of complex subjects, such as human psychiatric disorders, is currently fragmented into multiple scientific communities, which often have little or none interactions. It is quite possible, even likely, that powerful cues to finding remedies to numerous human maladies can be found right now provided that disparate pieces of the knowledge puzzle are combined in one head. It would be even more desirable to have the unordered collection of facts substituted with a quantitative probabilistic model allowing for formal evaluations of model predictions, analysis of discrepancies between data points, and hypothesis testing. The long-term research plan would address many issues, such as compiling the data, converting interactions into beliefs and amplifying the data associated with each node.
The test of the feasibility of this vision begins with a well-known system, cell-cycle network in the baker.s yeast to reproduce with belief network formalism the known phenotypic effects for yeast. The goals in this analysis would be (1) define applicability boundaries of the belief network methodology as applied to pathway data, (2) demonstrate feasibility of the approach, and (3) use the resulting model as a proof-of-principle for a larger study. The next step would include modeling with belief networks the knowledge that has been compiled over a few years on autism in humans, with a focus on automated conversion of molecular interaction data into belief networks, computation of probabilities for individual interactions, incorporation of expert inputs and experiments with reasoning over the network. The PI and a graduate student will be engaged in this project.
