Edward R. Dougherty* Pages 1 - 8 ( 8 )
The most basic aspect of modern engineering is the design of operators to act on physical systems in an optimal manner relative to the desired objective – for instance, designing a control policy to autonomously direct a system or designing a classifier to make decisions regarding the system. These kinds of problems appear in biomedical science, where physical models are created with the intention of using them to design tools for diagnosis, prognosis, and therapy. In the classical paradigm, our knowledge regarding the model is certain; however, in practice, especially with complex systems, our knowledge is uncertain and operators must be designed while taking this uncertainty into account. The related concepts of intrinsically Bayesian robust operators and optimal Bayesian operators have been developed to treat operator design under uncertainty, and they have been applied in numerous areas. An objective-based experimental design procedure is naturally related to optimal operator design because, if we are to select among a collection of experiments, then we would like to perform the experiment that maximally reduces our uncertainty as it pertains to our objective. The purpose of this paper is to provide a nonmathematical review directed at biomedical scientists and to do it in the context of two genomic applications, structural intervention in gene regulatory networks and classification.
Nonmathematical Review, Uncertain Scientific Models, Applications, Genomics
Texas A&M University - Department of Electrical and Computer Engineering College Station, Texas