David Bickel

Our lab uses mathematics to interpret the results of biological studies. In particular, we collaborate with scientists and clinicians to use probability and statistics to help them understand the conclusions that can be drawn from the observations they make in conducting scientific studies. For example, in large-scale genetic association studies, thousands of letters of DNA code are measured for hundreds of people with a widespread disease and for hundreds of people without the disease. The goal of this research is to create mathematical methods of accurately determining which DNA letters make someone more susceptible to contracting the disease.

The Bickel lab accepts students from graduate programs in Mathematics and Statistics.

Research Program

The members of the Statomics Lab focus our research on improving statistical methods of weighing evidence in order to enable more reliable interpretations of -omics data. This has involved research on confidence distributions, empirical Bayes approaches, likelihood, information theory and the foundations of statistics.

In our biostatistics program, we discover ways to assess complex information relevant to health care, renewable energy and other applications in the post-genomic era. We are improving statistical methods of weighing evidence to enable more reliable interpretations of both experimental measurements of transcript, protein and metabolite levels in the cell, and case-control measurements of genomes.

In the first component of the research program, our lab is developing statistical methods for the analysis of gene expression microarray data and other functional genomics data. The methods include the creation and testing of new ways to estimate levels of microarray gene expression. For the second component of this research program in high-dimensional statistics, the lab is extending similar methods developed for gene expression data to genome-wide association (GWA) studies. In particular, we are creating methods of reliably approximating probabilities of association in order to obtain better estimates of the effect sizes.

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