Models and Computational Strategies in Statistical Bioinformatics

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The investigator and his colleagues will develop methods for high-throughput biological data that provide innovative extensions of modern statistical building blocks, including the use of random effects for regularization, shrinkage estimation, Bayesian statistics, and mixtures for posterior classification and prediction. Novel modifications of the expectation-maximization algorithm are proposed for scalable and efficient model fitting and inference in several important biological applications.The goal of this project is to develop new statistical models, computational algorithms, and decision theoretic analysis of estimates for high-throughput biological data.  The new methods can be applied in the analysis of microarrays, RNA sequencing counts, label-free shotgun proteomics, metabolomics, the identification of quantitative trait loci and association mapping.