Our lab’s research focuses on generating and integrating omic data, functional annotations, and patient information through data science approaches to discover novel mechanisms in disease etiology and progression, therapeutic targets, and circulating biomarkers. Current research includes developing and applying cell-free nucleic acid sequencing platforms in disease studies to enable improved circulating diagnostic and therapeutic biomarker discovery, investigating the role of genomic instability in disease progression and treatment, the impact of context dependency on disease manifestations, and developing statistical frameworks for improving integrative analysis disease prediction models. We have a particular emphasis on developing and applying methods for identifying novel drug targets and prioritizing drug repurposing candidates for rare diseases and precision medicine approaches by integrating multiple omic data types through machine learning techniques.
