Woo Jun Shim

Since 2023, Dr Shim has been a postdoctoral researcher at UQ’s Institute for Molecular Bioscience. From 2020–2023, he was an Industry Scientist at HAYA Therapeutics in Lausanne, Switzerland, contributing to therapeutic discovery programs in genomic medicine. His doctoral research produced computational methods for identifying genetic determinants of cell identity and disease, leading to invited national presentations, international industry engagement with Merck, Mogrify, ConcR and HAYA Therapeutics, and more than A$700k in combined industry contributions. His work has been recognised by the AEpiA Emma Whitelaw Early Career Researcher Publication Award (2022) and the UQ Early to Mid-Career Researcher Industry Engagement Award (2022).

Dr Shim’s research focuses on developing computational approaches to advance personalised medicine by identifying the regulatory mechanisms that shape disease susceptibility, heterogeneity and clinical outcomes. His previous work includes TRIAGE (Transcriptional Regulatory Inference Analysis of Gene Expression), a computational framework that leverages conserved epigenetic signals across diverse cell types and species to prioritise regulatory genes governing cell identity and disease. The original TRIAGE framework was published in Cell Systems, with subsequent methodological extensions published in Nucleic Acids Research and Briefings in Bioinformatics.

Dr Shim also developed EpiCops (Epigenetically Co-Modulated Programs), a framework that captures recurrent epigenomic regulatory programs and applies them to genetic variants associated with complex diseases, including type 2 diabetes and asthma. By organising disease-associated variants according to shared regulatory programs, EpiCops enables biologically informed disease subgrouping and provides a framework for investigating heterogeneity in disease risk and outcomes.

More broadly, Dr Shim is interested in understanding how inherited genetic variation contributes to disease trajectories and responses to pharmacological interventions beyond what can be captured by conventional phenotype-based definitions of disease. His long-term goal is to develop clinically adaptable frameworks that estimate an individual’s predisposition to specific disease mechanisms and outcomes directly from their genetic architecture, complementing clinical diagnoses that may be influenced by variation in clinical decision-making, subjective self-reporting and inter-individual heterogeneity in phenotypic presentation.