报告人： 荣耀华（北京工业大学）

报告时间： 6月21日  19:00-20:00

腾讯会议： 600-968-080

报告摘要：Multiple myeloma is a form of lethal cancer, and wide heterogeneity exists in multiple myeloma patients’ survival, ranging from a few months to several decades. To accurately predict clinical outcomes, it is vital to build an accurate predictive model that relates patients’ molecular profiles, such as gene expressions, with patients’ survival. With complex relationships between survival and high-dimensional molecular predictors, it is challenging to conduct non-parametric modeling and irrelevant predictors removing simultaneously. In this paper, we build a kernel Cox proportional hazards semi-parametric model and propose a novel regularized garrotized kernel machine (RegGKM) method to fit the model. We use the kernel machine method to describe the complex relationship between survival and predictors, while automatically removing irrelevant parametric and non-parametric predictors through a LASSO penalty. An efficient high-dimensional algorithm is developed for the proposed method. Comparison with other competing methods in simulation shows that the proposed method always has better predictive accuracy. We apply this method to analyze a multiple myeloma dataset and predict patients’ death burden based on their gene expressions. Our results can help classify patients into groups with different death risks, facilitating treatment for better clinical outcomes.

 报告人介绍：荣耀华博士，现为北京工业大学理学部副教授，硕士生导师，兼全国工业统计教学研究会理事、北京大数据协会理事，曾获北京市委组织部优秀人才青年骨干荣誉称号。荣耀华一直从事非参数与半参数统计建模、高维数据分析的理论方法及应用研究。部分成果发表在《Statistics and Its Interface》，《统计研究》，《Journal of Applied Statistics》，《Communications in Mathematics and Statistics》，《中国高教研究》等。先后主持承担国家自然科学基金青年项目、全国统计科学研究重点项目等十余项国家级和省部级课题。曾获北京高校第十二届青年教师教学基本功比赛论文优秀奖，北京工业大学第十三届青年教师教学基本功比赛一等奖。

邀请人：马学俊