Dr. Dougherty’s research is directed towards tissue classification and the discovery of signaling pathways, both based on the expressed macromolecule phenotype of the cell. The essential issue with expression-based classification is the large number of genes (features) and small number of microarrays (data points). Dr. Dougherty has focused on the twin problems of small-sample error estimation and gene selection.

The kind of correlation-based analysis commonly used for understanding pair-wise relations between genes or cellular effects cannot capture the complex network of nonlinear information processing based upon multivariate inputs from inside and outside the genome. Regulatory models require the kind of nonlinear dynamics studied in signal processing and control. Dr. Dougherty has been involved in the development of multivariate measurements of gene interaction and the mathematical modeling of genetic regulatory networks. Genomics requires its own model systems, not simply straightforward adaptations of currently formulated models. New systems must capture the specific biological mechanisms of operation and distributed regulation at work within the genome. Dr. Dougherty’s work concerns the development of appropriate mathematical models and the application of control theory to find optimal strategies for therapeutic intervention.

Dr. Dougherty received his BS and MS degrees in mathematics from Fairleigh Dickinson University in 1967 and 1969, and earned his PhD in mathematics from Rutgers University in 1974. Following that, he obtained an MS degree in computer science from Stevens Institute of Technology in 1986.