Tomiko Oskotsky

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,...

Jul 4, 2024 | nature.com | Alice S. Tang |Beau Norgeot |Tomiko Oskotsky

AbstractWith the increasing availability of rich, longitudinal, real-world clinical data recorded in electronic health records (EHRs) for millions of patients, there is a growing interest in leveraging these records to improve the understanding of human health and disease and translate these insights into clinical applications. However, there is also a need to consider the limitations of these data due to various biases and to understand the impact of missing information.

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 20, 2024 | nature.com | Alice S. Tang |Jacquelyn Roger |Sarah R. Woldemariam |Riley Bove |Nima Aghaeepour |Tomiko Oskotsky | +1 more

AbstractIdentification of Alzheimer’s disease (AD) onset risk can facilitate interventions before irreversible disease progression. We demonstrate that electronic health records from the University of California, San Francisco, followed by knowledge networks (for example, SPOKE) allow for (1) prediction of AD onset and (2) prioritization of biological hypotheses, and (3) contextualization of sex dimorphism.