Microbiome Symposium 2018
Illustration by Kim Carney
Friday, March 16, 2018
Arnold Building, M1 A305/307
Directions | Parking | UW Shuttle
Welcome to the First Fred Hutch Microbiome Symposium! The purpose of this symposium is to bring together leading scientists, bioinformaticians and statisticians to answer important questions in microbiome research such as
- how are changes in the microbiome associated with diseases in the population?
- how are raw sequencing reads processed for downstream analysis?
- what are the statistical and bioinformatic challenges in microbiome data analysis?
As more and more microbiome data are collected, rigorous bioinformatic and novel statistical methods are critically needed to help address outstanding scientific problems. As such, the theme of this year’s symposium is on `microbiome data to knowledge’ with a special focus on the informatics and analysis of microbiome data.
Assistant Member, Public Health Sciences Division
Dr. Jing Ma's research concerns estimation and inference from high-dimensional 'omics' data, in particular to develop statistical machine learning methods to solve problems in genomics, metabolomics and microbiome.
Associate Member, Public Health Sciences Division
Affiliate Associate Professor, Department of Biostatistics, UW
Adjunct Associate Professor, Department of Biostatistics, UNC Chapel Hill
Dr. Wu's research focuses on the development and application of statistical methods for translational science and particularly for analysis of high-dimensional genomic data within the broader context of clinical trials as well as population-based genetic, gene-environment interaction, epigenetic, microbiome, and metabolomic studies. Additional disorders and disease areas of interest include reproductive and birth outcomes, autoimmune disorders, cardiovascular disease, and environmental health related traits.
Member, Public Health Sciences Division
Chair, Strategic Advisors Committee Hutch Data Commonwealth
Dr. Etzioni’s work focuses on statistical and computer modeling for policy development, with a focus on prostate cancer research. Dr. Etzioni’s models of disease have been used to estimate the lifetime probabilities of prostate cancer and its outcomes, study the extent of overdiagnosis associated with prostate cancer screening, and quantify the roles of screening and changes in receipt of initial therapies in explaining population mortality declines.