Grace Yi

Genetic and clinical information converge

Grace Yi
Grace Yi’s research interests focus on the development of new statistical methodology and its applications in medical studies. She works on missing data and measurement error problems in longitudinal and microarray studies. Microarray technology provides a novel way to understand gene functions. “It promises to be very useful for the diagnosis, treatment, and prevention of complex disease. Due to the fast advance of microarray technology, microarray data analysis has been receiving increasing interest,” describes Grace. “This technology makes it possible to study clinical phenomenon such as tumor type identification and patient’s survival prediction by means of gene profiles.”

However, due to the special features of microarray data, traditional statistical analysis methods can not be applied directly to analyze such data. For example, measurement error, missing observations, and high-dimensionality – in which the number of observations (less than a hundred patients or samples) is small relative to the number of variables (tens of thousands of genes) – present considerable challenges in developing strategies for the analysis of microarray data.

“My recent project involves building models to predict survival probability of cancer patient using gene expression data,” says Grace. “Traditionally, clinical data are used to study patient survival. Converging genetic and clinical information may help to better comprehend cancer patient disease progression and build more powerful prediction models.”

University of Waterloo Mathematics, Annual Report 2006