| Title: | Integrative Approaches to Precision Medicine |
| Lead Institution: | NYU Grossman School of Medicine |
| Principal investigator: |
Application 220731
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
About
We will use data from the UK Biobank to enhance our understanding of precision medicine, or how we can tailor therapies to individual patients based on their own genetic, clinical, and lifestyle traits for better outcomes. Over 36 months, we will work toward accomplishing several aims: 1) develop models that predict risk, specifically with respect to cardiovascular-related disease, with and without the incorporation of wearable technologies and novel omic layers, 2) conduct large scale meta-analysis of data from UK Biobank and other research cohorts around the world, 3) study the benefits and risks of medication prescription and use in different populations, and 4) study underlying genetic and cellular structures to uncover insights into human biology. Recent advancements in techniques to study underlying human biology have expanded our ability to study chronic diseases with complex etiologies, including technical advancement in proteomic profiling, whole genome sequencing, and detection of changes in DNA methylation. Furthermore, the expansion of the use of artificial intelligence and machine learning based models in healthcare holds great promise for researchers and medical professionals to more accurately diagnose disease, predict patient outcomes, and implement prevention or treatment strategies. Our researchers plan to analyze UK Biobank data using the most cutting edge methods in data science and computational modeling. The data will allow us to identify biomarkers and drug targets at the cellular level, optimize treatment outcomes based on unique patient characteristics and activity patterns and enhance our understanding of chronic diseases like kidney and cardiovascular diseases, aiding in their management and treatment. We believe our project can aid in revolutionizing our ability to study and implement targeted therapies tailored to individual patients. This project has significant implications for public health. By tailoring medical screenings and treatments to individual patients' characteristics, genetics, and lifestyle factors, we can improve patient care and outcomes. Moreover, by addressing disparities through the study of genetic, environmental, and socioeconomic factors, we can work towards reducing inequalities in healthcare access and outcomes. The findings from this project will not only benefit researchers and scientists but also contribute to the development of personalized interventions, ultimately promoting more equitable and effective healthcare for all.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 18281 | Development and Validation of a Parsimonious Risk Stratification Model for Pancreatic Cancer | Lucas A. Mavromatis (+9) | 2026 | JAMA Oncology |
Enabling scientific discoveries that improve human health