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Research WorkClustering, Linear models, Gene Expression Microarrays. We have studied extensions of clustering of regression models which have both time and species factors. These models isolate signal attributable to each factor and allow the estimation of the mean signal as well as the dependence structure. Clustering puts genes with similar mean and variance patterns into distinct groups whose biological characteristics can be further investigated.
Phylogenetic Estimation with Gene Expression. High resolution arrays potentially offer a large amount of information on the dependence structure across distantly or closely related species. We are studying the estimation of the tree structured correlation from continuous gene expression traits in order to characterize the observed dependence and compare it to the predicted dependence under the usual sequence based models. Under a relative mutation rate framework, we produce comparative models for neutral evolution which allow the estimation of deviations attributable to selection forces.
Functional Estimates with Political Boundaries. With Marc Ratkovic, I am beginning to study the incorporation of political boundaries (discontinuities) in smooth functional estimates.
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