Yu Shyr, Ph.D.
Harold L. Moses Chair in Cancer Research
Director, Vanderbilt Center for Quantitative Sciences; Director, Vanderbilt Technologies for Advanced Genomics Analysis and Research Design (VANGARD)
Associate Director for Quantitative Sciences Integration, Vanderbilt-Ingram Cancer Center
Professor of Biostatistics, Biomedical Informatics, and Cancer Biology
Vanderbilt University Medical Center
571 Preston Building
Nashville, TN 37232-6848
Yu Shyr received his PhD in biostatistics from the University of Michigan (Ann Arbor) in 1994 and subsequently joined the faculty at Vanderbilt University School of Medicine. At Vanderbilt, he has collaborated on numerous research projects; assisted investigators in developing clinical research protocols; collaborated on multiple grants funded through external peer-reviewed mechanisms; and developed biostatistical methodologies for clinical trial design, high-dimensional data preprocessing, and other statistical approaches, published in journals such as Statistics in Medicine, Bioinformatics, and Clinical Trials in the last three years.
Dr. Shyr is a Fellow of the American Statistical Association and an FDA advisory committee voting member. He has delivered more than 190 abstracts at professional meetings and has published more than 300 peer-reviewed papers. Dr. Shyr has served on numerous NIH/NCI SPORE, P01, and CCSG review panels/committees and has been a member of the invited faculty at the AACR/ASCO Methods in Clinical Cancer Research Vail Workshop since 2004. He currently serves on the external advisory board for eight national cancer centers, and directs the biostatistics and bioinformatics cores for the NCI-funded Vanderbilt University Breast Cancer SPORE, GI Cancer SPORE, and other program projects. In addition, Dr. Shyr is the Principle Investigator of a UO1 grant for the Barrett's esophagus translational research network coordinating center (BETRNetCC). Dr. Shyr's current research interests lie in developing and analyzing predictive models of the statistical relationships between multiple-variable protein and next-generation sequencing data and clinical endpoints using both supervised and unsupervised classification and pattern recognition approaches.