Marina is an Associate Professor at the Bakar Computational Health Sciences Institute at UCSF. Prior to that she was a Senior Research Scientist at Pfizer after getting her PhD in Biomedical Informatics at Stanford. Dr. Sirota’s research experience spans over 10 years during which she has co-authored over 100 scientific publications. Her research interests lie in developing computational integrative methods and applying these approaches in the context of disease diagnostics and therapeutics. The Sirota laboratory is funded by NIA, NLM, NIAMS, March of Dimes and the Burroughs Wellcome Fund. As a young leader in the field, she has been awarded the AMIA Young Investigator Award in 2017. Dr. Sirota also is the director of the AI4ALL program at UCSF, with the goal of introducing high school girls to applications of AI in biomedicine and serves as the director of outreach and advocacy at the Bakar Computational Health Sciences Institute.
Leveraging Molecular and Clinical Data to Improve Pregnancy Outcomes
Each year, 15 million babies are born preterm (before 37 weeks of gestation) but the exact mechanism of spontaneous preterm birth is unknown. We are leveraging different types of molecular and clinical data combined with integrative computational approaches to define new diagnostic and therapeutic strategies to prevent preterm birth.
Linda Giudice, UCSF
Yoel Sadovsky, UPMC
Researchers have long been recognizing the uniqueness of women’s health and its substantial effect on clinical practice, acknowledging the increasing appreciation of the importance of multidisciplinary approaches to health and disease. In every organ system, there are diseases that are unique to women, more common in women than in men, or characterized by differences in disease course in women compared to men. This Track will focus on the following topics related to Women’s Health: