Advancing Methodological Foundations in Precision Medicine Through Novel Causal Inference, Machine Learning, and Artificial Intelligence Frameworks

Aditya Sriram - Final Dissertation Defense - HUGEN PhD Candidate Department of Human Genetics Doctoral Candidate, Aditya Sriram, will defend the following dissertation on “Advancing Methodological Foundations in Precision Medicine Through Novel Causal Inference, Machine Learning, and Artificial Intelligence Frameworks” COMMITTEE CHAIR: Hyun-Jung (HJ) Park, PhD Committee Members: Joseph A. Carcillo, MDJenna C. Carlson, PhDJohn R. Shaffer, PhD ABSTRACT: Complex diseases arise from interactions across multiple layers of biological and clinical information, including inherited genetic components, environmental exposures, clinical contexts, and molecular regulation. This multilayered heterogeneity is a well-established concern for precision medicine, which seeks to tailor disease prevention, diagnosis, and treatment to the biological and clinical characteristics of individual patients and, within larger heterogeneous populations, more precisely defined subgroups. Machine learning models, particularly deep learning models, have become increasingly powerful for prediction in this setting. Additionally, with the advent of artificial intelligence and its growing role in healthcare and biomedical research, there is growing demand for models that extend beyond prediction toward causal inference and offer greater interpretability for researchers, clinicians, and patients. Motivated by these challenges and directions, this dissertation develops three novel deep learning-based frameworks for biologically meaningful and reliability-aware biomedical discovery in complex disease and precision medicine. Aim 1 introduces DeepEXPOKE, a paired-reference deep-learning framework for decomposing exposure-associated disease risk into genetically driven, non-genetic, and confounding-driven components. By evaluating observed exposures against both model-X statistical knockoffs and polygenic risk score-derived matched controls, DeepEXPOKE provides a conserva