北京雁栖湖应用数学研究院

This course introduces the fundamental concepts, theoretical frameworks, and modern applications of Computational Biology and Statistical Genetics. We begin with the biological foundations of genetics and evolution, and then develop core computational methods including sequence alignment, genome annotation, phylogenetic tree construction, genome-wide association studies (GWAS), heritability estimation, genomic prediction, and multi-omics integration. Emphasis is placed on the mathematical and statistical principles underlying these approaches, such as likelihood theory, mixed models, matrix decomposition, and high-dimensional inference.

The course concludes with emerging AI-driven paradigms in biology, including deep learning for genomic prediction, genome annotation, regulatory network inference, and medical image analysis. By integrating classical statistical genetics with modern machine learning, this course prepares students to address complex biological questions in the era of big data and artificial intelligence.