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个人简介
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.
研究兴趣
论文共 160 篇作者统计合作学者相似作者
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crossref(2023)
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
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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
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Journal of Clinical Oncologyno. 6_suppl (2023): TPS400-TPS400
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Medha S. Darshan,Matthew S. Loftus,Maria Thadani-Mulero, Benjamin P. Levy,Daniel Escuin,Xi Kathy Zhou,Ada Gjyrezi,Chantal Chanel-Vos, Ruoqian Shen,Scott T. Tagawa,Neil H. Bander,David M. Nanus,
crossref(2023)
Daniel W. Fitzgerald, Karl Bezak,Oksana Ocheretina,Cynthia Riviere,Thomas C. Wright,Ginger L. Milne,Xi Kathy Zhou,Baoheng Du,Kotha Subbaramaiah, Erin Byrt,Matthew L. Goodwin, Arash Rafii,
crossref(2023)
Jay O. Boyle,Zeynep H. Gümüş,Ashutosh Kacker,Vishal L. Choksi, Jennifer M. Bocker,Xi Kathy Zhou,Rhonda K. Yantiss, Duncan B. Hughes,Baoheng Du,Benjamin L. Judson,Kotha Subbaramaiah,Andrew J. Dannenberg
crossref(2023)
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