Advanced Multi-Omic Modeling and Systems Analysis

3 units

This advanced course addresses the computational and statistical challenges of integrating diverse high-throughput biological data—including genomics, transcriptomics, proteomics, metabolomics, and epigenomics. The curriculum covers data harmonization, preprocessing techniques, and advanced statistical methods such as matrix factorization, kernel-based techniques, and Bayesian inference, alongside application of network and systems biology approaches for predictive modeling, biomarker discovery, and clinical translation in personalized medicine. Through computational labs, literature reviews, and a semester-long project, MS and PhD students with strong backgrounds in statistics and programming (R or Python) will develop the ability to critically evaluate, design, and implement robust multi-omic data analysis pipelines, equipping them with essential skills for cutting-edge biological research and clinical discovery. This advanced, doctoral-level course provides a deep dive into the computational and statistical challenges inherent in integrating diverse high-throughput biological data (genomics, transcriptomics, proteomics, metabolomics, and epigenomics). The course begins by reviewing state-of-the-art data harmonization and preprocessing techniques necessary for combining datasets of varying dimensionality and noise profiles. Key themes include advanced statistical modeling for integration, such as matrix factorization methods, kernel-based techniques, and Bayesian inference, alongside the application of network and systems biology approaches to contextualize findings. Students will explore predictive modeling, biomarker discovery, and clinical translation within the framework of personalized medicine. The target audience is MS and PhD students in Biomedical Informatics, Computational Biology, and related quantitative disciplines who possess a strong foundation in statistics and programming (R or Python). Through computational labs, critical literature reviews, and a semester-long, project-based assessment, students will transition from theoretical knowledge to practical application. Upon completion, students will be able to critically evaluate, design, and implement robust multi-omic data analysis pipelines, equipping them with essential skills for cutting-edge biological research and clinical discovery.