Md Asad Rahman, a doctoral candidate in systems engineering, will defend their dissertation titled “A Bayesian Inference Based Complex Systems Approach in Precision Medicine.” Asad’s advisor, Dr. Jinling Liu, is an advisor in the engineering management and systems engineering department. The dissertation abstract is provided below.

Precision medicine, tailoring healthcare based on individual genetic profiles, represents a fundamental shift from traditional medical approaches. This dissertation introduces a novel and personalized framework, including advanced genomic tools, the Individualized Bayesian Inference (IBI), and the Individualized Bayesian Inference - Decision Tree (IBI-DT), that complement conventional Genome-wide Association Studies (GWAS) in detecting genomic variants of diseases for each individual. These tools improve the detection of genetic variants influencing complex diseases such as hypertension, Parkinson's disease, cancer, and congenital heart disease (CHD) and facilitate the discovery of genetic interactions associated with these corresponding complex diseases at the subgroup and individual level. By applying the IBI method to hypertension, we demonstrated its capability to identify unique and low-frequency variants influencing hypertension, providing insights into personalized disease mechanisms that GWAS often misses. In Parkinson’s disease research, our approach not only pinpointed both established and unknown risk factors but also developed predictive models that utilize these genetic markers to forecast disease risk effectively. For cancer, the IBI-DT method identified key cancer drivers and their interactions, showing superior performance over traditional expression quantitative trait loci (eQTL) analysis. In the study of CHD, IBI-DT proved more effective than GWAS by uncovering critical genetic influences, revealing more novel and well-known genes, gene interactions, and essential biological pathways. This research integrates systems thinking with genomics to significantly advance our understanding of diseases by identifying novel variants, uncovering genetic network interactions, and improving disease prediction and comprehension. The dissertation represents systems engineering principles, offering scalable, reproducible, and efficient solutions for complex genomic data, paving the way for future advancements in precision medicine.