基本信息
views: 3
![](https://originalfileserver.aminer.cn/sys/aminer/icon/show-trajectory.png)
Bio
Xi Kathy Zhou, Ph.D., M.S., joined the faculty at Weill Cornell Medical College from the Genomic Institute of Novartis Research Foundation in San Diego, where she served as a biostatistician and worked on projects related to microarray and high-throughput screening data analysis.
Dr. Zhou’s research interest is to develop and apply novel statistical methods to better design biological and clinical studies related to cancer prevention, diagnosis and treatment and properly analyze data generated from such studies. Specifically, her interest in statistical methodology covers hierarchical model development, variable selection, model averaging, predictive modeling and the analysis of large complex datasets. She developed a Bayesian hierarchical model to classify missense mutations on disease susceptibility genes (Journal of the American Statistical Association, 100: 51-60), made significant contributions to the development of a Bayesian method to accurately estimate minimum inhibitory concentration based on high throughput microbial growth curves generated from automated microbial susceptibility systems (Annals of Applied Statistics, 3[2]: 710-730), and developed a novel Bayesian model averaging (BMA) approach for analyzing observational gene-expression data (Annals of Applied Statistics, 6[2]: 497-520). She is currently applying the BMA approach to the analysis of metabolomic data derived from mouse and human samples. Her methodology research has been funded by NIH/NCI and the CTSC.
Dr. Zhou’s research interest is to develop and apply novel statistical methods to better design biological and clinical studies related to cancer prevention, diagnosis and treatment and properly analyze data generated from such studies. Specifically, her interest in statistical methodology covers hierarchical model development, variable selection, model averaging, predictive modeling and the analysis of large complex datasets. She developed a Bayesian hierarchical model to classify missense mutations on disease susceptibility genes (Journal of the American Statistical Association, 100: 51-60), made significant contributions to the development of a Bayesian method to accurately estimate minimum inhibitory concentration based on high throughput microbial growth curves generated from automated microbial susceptibility systems (Annals of Applied Statistics, 3[2]: 710-730), and developed a novel Bayesian model averaging (BMA) approach for analyzing observational gene-expression data (Annals of Applied Statistics, 6[2]: 497-520). She is currently applying the BMA approach to the analysis of metabolomic data derived from mouse and human samples. Her methodology research has been funded by NIH/NCI and the CTSC.
Research Interests
Papers共 141 篇Author StatisticsCo-AuthorSimilar Experts
By YearBy Citation主题筛选期刊级别筛选合作者筛选合作机构筛选
时间
引用量
主题
期刊级别
合作者
合作机构
Annals of Surgical Oncologyno. 3 (2024): 1497-1497
Verena Isak,Shayan Azizi,Xi K Zhou,Devina Mehta,Wanhong Ding,Zakir Bulmer, Daniella S Aivazi,Ryan W Dellinger,Richard D Granstein
crossref(2023)
Sydney Wolfe, Marshall A. Diven,Ariel E. Marciscano,Xi Kathy Zhou, A. U. Kishan, M. L. Steinberg,Joseph A. Miccio,Philip Camilleri,Himanshu Nagar
BMC cancerno. 1 (2023): 1-11
Pashtoon Murtaza Kasi,Manuel Hidalgo,Mehraneh D. Jafari,Heather Yeo,Lea Lowenfeld,Uqba Khan, Alana T. H. Nguyen, Despina Siolas,Brandon Swed,Jini Hyun, Sahrish Khan, Madeleine Wood,
Oncogeneno. 44 (2023): 3252-3259
crossref(2023)
Journal of Clinical Oncologyno. 6_suppl (2023): TPS400-TPS400
crossref(2023)
Cited0Views0Bibtex
0
0
Load More
Author Statistics
Co-Author
Co-Institution
D-Core
- 合作者
- 学生
- 导师
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn