Project Summary/Abstract
The candidate currently serves as an Assistant Professor of Engineering Management and
Systems Engineering (EMSE) with a joint appointment in Biological Sciences at Missouri
University of Science and Technology (Missouri S&T), a member institution of the University of
Missouri (UM) System. Before joining Missouri S&T, the candidate obtained an MS degree in
Biomedical Informatics (BMI) and completed a National Library of Medicine (NLM) Postdoctoral
Fellowship in BMI at Department of Biomedical Informatics (DBMI) at University of Pittsburgh
(Pitt). The candidate’s long-time research goal is to become an independent researcher with an
extramurally supported research program concentrating on inferring the activation states of
signaling pathways from multi-omics data and utilizing it in precision medicine for cardiovascular
diseases. In this K01 application, the candidate has assembled a strong mentoring committee
from both Pitt and UM System. The training, mentorship, and research opportunities provided by
this K01 award will significantly strengthen her expertise in multi-omics analytics, causal
inference, deep learning, and more importantly will help build her expertise in complex
cardiovascular diseases and their risk factors. This K01 award is critical in transitioning the
candidate into an independent investigator in multi-omics analytics for precision medicine in
cardiovascular disease. In this proposal, the candidate proposes to pursue the following aims:
develop and evaluate an instance-specific causal inference (ICI) framework to identify causative
genomic variants for blood pressure regulation (Aim 1); harmonize a large mixed-ethnic cohort
from The Trans-Omics for Precision Medicine program and apply ICI and GWAS to better
understand the role of genomic variants in racial disparity in hypertension prevalence(Aim 2);
apply and evaluate both population-based and instance-specific predictive machine learning
models for hypertension prediction by integrating genomics and other omics data (Aim 3). If
successful, this project will develop and evaluate a novel, instance-specific method for
discovering individualized genomic variants of hypertension, for better understanding the
genomic basis of racial differences in hypertension, and for more accurately and timely
predicting the development of hypertension for intervention and prevention. Moreover, the
developed methods will be applicable to other cardiovascular diseases and risk factor as well.