Laying the groundwork for precision medicine by harnessing the power of genomics.
We craft multifaceted models that offer a more comprehensive understanding of health conditions. Our approach is hypothesis-driven, tailored to each disease, and leverages multi-modal datasets.
Themes in our research include:
Genomic
Discoveries
In our lab, we uncover the genetic underpinnings of various diseases by examining the main effects of genes. This method allows us to delve deep into the genetic architecture of diseases, identifying key genetic variants and understanding their roles in disease pathogenesis.
Risk
Prediction
We employ machine learning techniques to enhance risk prediction. Our strategy integrates both genetic and non-genetic factors, offering a more holistic view of disease susceptibility. By combining multi-modal data, we develop more accurate and personalized disease prediction models.
Integrative
Omics
Our research extends into the realm of integrative omics, where we apply machine learning methods to understand complex interactions such as gene-gene and gene-environment interplays. This approach helps us untangle the multifaceted nature of diseases.
Featured Project
Funded by the National Institute of Child Health and Human Development (NICHD), this project aims to develop an advanced predictive model for endometriosis by integrating both genetic and non-genetic risk factors.
Utilizing machine learning techniques, the model will analyze a wide range of data, including genetic markers, environmental factors, and clinical indicators, to improve the accuracy of endometriosis risk prediction and aid in early diagnosis.
Comprehensive Predictive Modeling for Endometriosis Using Genetic and Non-Genetic Risk Factors
Featured Project
This project represents a groundbreaking initiative that fuses state-of-the-art genomics with advanced cardiac imaging techniques to unravel the complexities of Coronary Microvascular Disease (CMVD) specifically in women. CMVD, a condition affecting the small blood vessels of the heart, poses a significant health risk, yet its genetic underpinnings remain poorly understood, particularly in the context of female patients.This multidisciplinary research endeavor will employ cutting-edge genomic technologies to conduct a comprehensive analysis of the genetic factors contributing to CMVD susceptibility in women.
By integrating genomic data with high-resolution cardiac imaging, this study aim to elucidate the mechanistic links between genetic variants and microvascular dysfunction, providing unprecedented insights into the pathogenesis of CMVD.
Genetic Determinants of Coronary Microvascular Disease in Women
Featured Project
Advanced Deep Phenotyping for Women's Health Using Multi-Modal Datasets
This project is dedicated to the deep phenotyping of women's health phenotypes, incorporating a comprehensive analysis of multi-modal datasets including imaging, disease codes, surgery codes, and lab measures. The goal is to accurately identify true cases and controls for various women's health issues, enhancing our understanding of these conditions and paving the way for more targeted and effective treatments.
Our Mission
We harness the power of Electronic Health Records (EHR) and omics data to develop comprehensive models that advance diagnosis, prevention, and treatment strategies for a variety of health conditions.
