Statistical Methods in Genomics and Bioinformatics

(3 Credits)

Genomics and other omics data have become cornerstones of research in biology, biomedicine, and public health, yet their high dimensionality, heterogeneity, and complex structures challenge traditional computational and statistical methods. This course introduces fundamental and advanced statistical and computational approaches to address these challenges, emphasizing core statistical concepts and their applications in bioinformatics and biomedical problems. Topics include gene expression, regulatory sequence analysis, RNA-seq, spatial transcriptomics, and gene regulation studies. Methods covered range from multivariate analysis and sequence modeling to machine learning, illustrated with real-world examples from public health.