Li Xin , PhD
Email: xin18@uw.edu
Phone: (206) 543-6551
Dr. Xin's lab is interested in using the prostate as a tissue model to study the molecular and cellular mechanisms that regulate development, tissue homeostasis and carcinogenesis. Currently, there are two major research focuses in the lab. The first is to characterize the prostate epithelial lineage hierarchy and investigate how individual prostate epithelial lineages are maintained in adults by prostate stem cells and to identify master regulators that control adult prostate homeostasis. The is to investigate the molecular and cellular basis of aggressive prostate cancer. The lab is interested in determining the function of disease-associated genes in prostate cancer initiation and progression, and characterizing the identity of the cells of origin for prostate cancer. The major approaches utilized are cell culture-based prostate stem cell assays, genetically engineered mouse models, and a prostate regeneration method.
Ka Yee Yeung , PhD
Email: kayee@uw.edu
Phone: (253) 692-4924
Dr. Yeung has extensive experience in the design of algorithms for the mining and integration of big data. She also has research expertise/interest spanning multiple disciplines, including computer science, statistics, computational biology, cancer biology and systems biology.
Lue-Ping Zhao , PhD
Email: lzhao@fredhutch.org
Phone: (206) 667-6927
Being trained in biostatistics/bioinformatics, epidemiology and genetics, Dr. Zhao's current interest in STTR includes how to use omics methodology to dissect solid tumor etiology and mechanism with either expression arrays, SNP arrays, or short-read sequencing methods. Further, he is interested in utilizing large and complex electronic medical records with modern genomic technologies for translational bioinformatics studies.
Yingye Zheng , PhD
Email: yzheng@fredhutch.org
Phone: (206) 667-7580
Dr. Zheng's research interests have been in the statistical methods for longitudinal data with time-to-event outcome, with a focus on using semi-parametric methods for estimating time-dependent ROC curves which are useful for evaluating the ability of longitudinal biomarkers or algorithms to identify cancer early, or signal disease prognosis. Her work is also focused on statistical methods for family-based genetic association studies. She is presently providing support to a number of research proposals and applications in the cancer prevention research program, including serving as an investigator in the DMCC of the Early Detection Research Network.