统计与数据科学学院学术报告：癌症关联研究的高效数据自适应检验

【主讲人简介】：Dr. Zhongyuan Chen (陈中媛) is an Associate Professor and PhD advisor in the School of Mathematics at Nanjing University, and a Principal Investigator at the National Institute of Health Data Science at Nanjing University. Previously, she was a Tenure-Track Assistant Professor in the Division of Biostatistics of the Data Science Institute at the Medical College of Wisconsin in the United States. She obtained her Ph.D. in Statistics from Rice University, USA, during which she also served as a Research Assistant at the MD Anderson Cancer Center. Subsequently, she was a Visiting Assistant Professor in the Department of Statistics at Purdue University, USA. Her research is characterized by deep multidisciplinary integration spanning statistics, biostatistics, bioinformatics, data science, and related fields. Her primary research interests include statistical genetics, precision medicine, causal inference, big-data analytics in cancer genomics, statistical analysis of randomized algorithms, and the applications of statistics to biomedical research and clinical trials.

【内容简介】：Despite the availability of large amounts of genomic/genetic/clinical data, cancer association studies have yet to successfully take good advantage of them. In this talk, I will first discuss data‐adaptive and pathway‐based powerful tests for cancer association studies between somatic mutations and germline variations, which is important for cancer risk prediction. Second, I will present a series of low-rank approximation methods that have been developed to improve the efficiency of such association tests and to further extract other valuable information. Finally, I will show our proposed data-adaptive gene- and pathway-based tests for rare-variants and survival association studies in patients with non-metastatic prostate cancer. Together, these methodologies provide powerful and efficient tools for cancer association studies.

【讲座时间】：2026年10月21日（星期三）下午15:00

【讲座地点】：人文社科科研楼1801会议室