Description：

This course provides an introduction to modern statistical and computational methods for analyzing high-dimensional and complex data arising from omics and health studies. Topics include statistical foundations and computational strategies for genomics, transcriptomics, epigenomics, proteomics, and other high-throughput molecular data, as well as their integration with clinical, epidemiological, and electronic health record (EHR) data. The course covers methods for data preprocessing and quality control, dimensionality reduction, clustering and classification, differential analysis, association studies, network and pathway analysis, prediction, machine learning, and integrative multi-omics analysis. Particular emphasis is placed on understanding the statistical assumptions underlying these methods, addressing multiple testing and dependence, accounting for heterogeneity and correlation, and developing reproducible computational workflows. Through lectures, students will learn how to select, implement, evaluate, and interpret statistical and computational approaches for addressing contemporary questions in biomedical and population health research.

Prerequisite：Basic knowledge on entry-level statistics course

Target Audience：Graduate students

Teaching Language：中文

Bio:

Professor, Department of Biostatistics, Yale University School of Public Health

Visiting Scholar, Tsinghua University

Fields of Research: 统计学，生物信息学

Registration: https://v.wjx.cn/vm/r4LEhlk.aspx

Note: This is only for course registration. Please make the campus entry application by yourself. 校外人员，请自行申请进校。