Extracting the most out of your biomedical data
Charles Perkins Centre, The University of Sydney
With the advancement of many high-throughput biotechnologies, a research interest of many scientists has been to utilize multiple data sources to gain further insights into biology and deeper understanding of complex diseases. Data integration not only enables scientists to address and ask very specific questions, it also allows them to explore and understand the complex relationships among different phenotypes. In this talk, I will discuss how recent statistical visualization and machine learning approaches are used to address different questions in bioinformatics research. For example, the modeling of heterogeneity in multi-omics data together with extracting different types of features can help to improve the prognosis of disease outcome. Finally, I will discuss the impact of such research on our statistics and data science curriculum.
Professor Jean Yang is an applied statistician with expertise in statistical bioinformatics. She was awarded the 2015 Moran Medal in statistics from the Australian Academy of Science in recognition of her work on developing methods for molecular data arising in cutting edge biomedical research. Her research stands at the interface between medicine and methodology development and has centered on the development of methods and the application of statistics to problems in -omics and biomedical research. In particular, her focus is on developing methods for integrating omics and clinical data to answer a variety of scientific questions. As a statistician who works in the bioinformatics area, she enjoys research in a collaborative environment, working closely with scientific investigators from diverse backgrounds.
This plenary address will be delivered in AH1 on Tuesday 27 November at 2:00.