All Research Projects
Cancer-specific and shared pre-diagnostic DNA methylation signatures across multiple cancers
Postdoc Recipient: Dr Zhoufeng Ye, Melbourne School of Population and Global Health, The University of Melbourne
Identifying the recessively inherited component of breast cancer susceptibility
Postdoc Recipient: Dr Philip Harraka, Precision Medicine, Department of Medicine, Monash University
Genetic predisposition to lifetime asthma trajectories in the general population
Postdoc Recipient: Dr Yaoyao Qian, Allergy and Lung Health Unit, Centre for Epidemiology and Biostatistics, The University of Melbourne
Ambient air pollution and asthma discordance in Australian twins
Postdoc Recipient: Dr Diego Jose Lopez Peralta, Allergy and Lung Health Unit, Centre for Epidemiology and Biostatistics, The University of Melbourne
Leveraging multi-generational linkage data to enhance the prediction of early-onset lung cancer
Lead: Dr Jennifer Perret, Allergy and Lung Function Unit, The University of Melbourne
Biomarkers for prostate cancer risk stratification and outcomes
Lead: A/Professor Pierre-Antoine Dugué, Precision Medicine, School of Clinical Sciences, Monash University
Exploring causal pathways between asthma and COPD and their shared risk factors
Lead: Dr Jingwen Zhang, Allergy and Lung Function Unit, Melbourne School of Population and Global Health
Software for the analysis of family data
Lead: Dr James Dowty, Centre of Epidemiology and Biostatistics, Melbourne School of Population and Global Health
Uncovering genetic components of prostate cancer risk independent of PSA levels
From aetiology to improved risk prediction for clinically significant disease Postdoc Recipient: Dr Hamzeh M Tanha, The Daffodil Centre, The University of Sydney, and Cancer Council NSW, Sydney, Australia
Epigenetic signatures of menopause: an integrative analysis
Postdoc Recipient: Dr. Zhoufeng Ye, Centre for Epidemiology and Biostatistics (CEB), University of Melbourne
Enhancing individual-level consistency of breast cancer Polygenic Risk Score (PRS) through machine learning approaches
PhD Recipient: Di Mu, Melbourne School of Population and Global Health, University of Melbourne
Using machine learning to improve polygenic risk scores (PRSs) prediction of colorectal cancer
PhD Recipient: Max Schuran, Melbourne School of Population and Global Health, University of Melbourne