Kerby Shedden is an Associate Professor of Biostatistics and an Associate Professor of Statistics in the College of Literature, Science and the Arts (LSA). He received a Ph.D. in Statistics from UCLA in 1999. His research focuses on developing and evaluating methods for analyzing high dimensional and complex data including dimension reduction, feature extraction, modeling, and inference. In addition to basic research he also collaborates on a number of life science projects in which complex data sets arise. This includes collaborations with members of the UM Cancer Center Cancer Genetics Program and Biostatistics Core, the UM College of Pharmacy, and the UM Addiction Research and Depression centers.
Ph.D., Statistics, UCLA, 1999
B.S., Mathematics, University of Michigan, 1994
Research Interests & Projects
One of my major interests is in statistical analysis of molecular assay data for cancer studies, such as gene expression microarray data and proteomics data from mass mapping and UV mapping. I have worked on low-level data processing such as how to convert raw fluorescence intensities into quantitative expression measurements. I have also worked on high level modeling and inference problems such as how to account for intergene correlations when doing statistical inference with microarray data, and how to detect complex interactions between gene expression levels that are associated with clinical outcomes.
I am also very interested in data analysis and modeling in pharmaceutical science. This includes predictive modeling of drug activity in cells based on structures and properties of the drugs and biological properties of the target cells. This collaborative work also involves statistical analysis of fluorescent cell images to assess subcellular transport and compartmentalization of small molecules.
Some of my other work includes mathematical modeling and analysis of gene and protein expression during the cell cycle, and longitudinal analysis of behavioral and psychiatric measures.
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Shedden, K., Chen, W., Kuick, R., Ghosh, D., Macdonald, J., Cho, K.R., Giordano, T.J., (2005 Feb 10). Comparison of seven methods for producing Affymetrix expression scores based on False Discovery Rates in disease profiling data. BMC Bioinformatics, 6(1), 26.
Shedden, K. (2004). Confidence levels for the comparison of microarray experiments. Statistical Applications in Genetics and Molecular Biology., 3(1)
Shedden, K. and Rosania, G.R. (2004). Exploratory chemoinformatic analysis of cell-type selective anticancer drug targeting. Molecular Pharmaceutics, 1(4), 267-280.
Shedden, K. and Taylor, J. (2004). Differential correlation detects complex associations between gene expression and clinical outcomes in lung adenocarcinomas. In Jennifer Shoemaker (Ed.) Methods of Microarray Data Analysis IV. Kluwer.
Shedden, K. and Cooper, S. (2002 Jul 1). Analysis of cell-cycle gene expression in Saccharomyces cerevisiae using microarrays and multiple synchronization methods. Nucleic Acids Res., 30(13), 2920-9.