Dr. Baranzini directs his lab at UCSF, which is composed by experimental and computational researchers. His current work involves the genetics of MS susceptibility and progression, and molecular studies to identify different stages of the disease and differential response to treatment. His lab also performs in-vitro and in-vivo immunological studies to understand the pathogenesis of MS.Dr. Baranzini leads the iMSMS, an international consortium to study the effect of bacterial populations (microbiota) on MS susceptibility and progression. In addition, he is the principal investigator of the SPOKE project, a large multi-disciplinary bioinformatics approach to gather, integrate and analyze all biomedical data, currently supported by NIH and NSF.Dr. Baranzini earned his degrees in clinical biochemistry and PhD in human molecular genetics from the University of Buenos Aires, Argentina. Dr. Baranzini then moved to UCSF to specialize in the analysis of complex hereditary diseases, and focused his efforts on multiple sclerosis (MS).
Knowledge graphs and AI for genomics in clinical practice
Successful integration of individual patient information, including genomics, will be critical to developing computational predictive models with value in the clinic. Here I will introduce how the SPOKE biomedical knowledge graph can be leveraged with AI to enable integration of multiple streams of information from a single patient in the context of an entire population.
Russ Cucina, UCSF