Room 004, School of Arts and Sciences
Central Campus
Over the past decade, statistical genomics has been a driving force in the advancement of precision medicine, utilising genomic data to tailor medical treatments to individual patients. This field harnesses advanced sequencing technologies, such as Next-Generation Sequencing, to analyse large-scale genomic datasets, facilitating the identification of molecular signatures linked to complex diseases like cancer, diabetes, and cardiovascular disorders. A key and fundamental tool in statistical genomics is differential analysis, which involves developing and applying sophisticated statistical methods to uncover molecular mechanisms associated with disease susceptibility, progression, and treatment response. These approaches enable the discovery of different genomic alterations underlying disease processes, leading to more precise patient stratification, improved prognostic assessments, and personalised therapeutic strategies. This seminar will highlight the critical role of statistical genomics in identifying and elucidating biological mechanisms, contributing significantly to the advancement of precision medicine. This seminar will cover recent advances in statistical models for differential analysis of two genomic data types, demonstrating their application in understanding disease mechanisms and identifying therapeutic targets.
Suvo Chatterjee is an Assistant Professor of Biostatistics in the Department of Epidemiology and Biostatistics at Indiana University, Bloomington. His methodological research focuses on developing scalable and robust statistical models to analyze high-dimensional biomedical data, with a particular focus on complex genomics data. His applied research interests include adverse pregnancy outcomes, cardiometabolic disorders, and neurodegenerative diseases.
