Marina Sirota

Feb 24, 2025 | cell.com | Grace Ramey |Alice S. Tang |Thanaphong Phongpreecha |Monica Yang |Sarah R. Woldemariam |Tomiko Oskotsky | +6 more

Keywords autoimmunity Alzheimer’s bioinformatics case-control cohort electronic health records risk analysis sex differences statistical epidemiology Introduction Alzheimer’s disease (AD) is a debilitating neurodegenerative disease that is accompanied by enormous social and economic burdens, and its prevalence is increasing due to the growing aging population worldwide.1,2 AD is characterized biologically by amyloid plaques and tau deposition in the brain, while clinical syndromic diagnoses,...

May 14, 2024 | nature.com | Tomiko Oskotsky |Ophelia Yin |Umair Khan |Marina Sirota |Leen Arnaout

This perspective explores the transformative potential of data-driven insights to understand and address women’s reproductive health conditions. Historically, clinical studies often excluded women, hindering comprehensive research into conditions such as adverse pregnancy outcomes and endometriosis. Recent advances in technology (e.g., next-generation sequencing techniques, electronic medical records (EMRs), computational power) provide unprecedented opportunities for research in women’s reproductive health. Studies of molecular data, including large-scale meta-analyses, provide valuable insights into conditions like preterm birth and preeclampsia. Moreover, EMRs and other clinical data sources enable researchers to study populations of individuals, uncovering trends and associations in women’s reproductive health conditions. Despite these advancements, challenges such as data completeness, accuracy, and representation persist. We emphasize the importance of holistic approaches, greater inclusion, and refining and expanding on how we leverage data and computational integrative approaches for discoveries so that we can benefit not only women’s reproductive health but overall human health.

Feb 29, 2024 | universityofcalifornia.edu | Marina Sirota |Victoria Colliver

The conditions that most influenced the prediction were high cholesterol and, for women, the bone-weakening disease osteoporosis. The work demonstrates the promise of using artificial intelligence (AI) to spot patterns in clinical data that can then be used to scour large genetic databases to determine what is driving that risk. The researchers hope that one day it will hasten the diagnosis and treatment of Alzheimer’s and other complex diseases.