Associate Professor Brett Lidbury
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About
Undergraduate and Honours degrees in Biological Sciences were completed at the University of Newcastle (NSW), followed by the award of a Ph.D. from the ANU (John Curtin School of Medical Research).
Post-doctoral experience was gained in molecular virology and mucosal vaccine development, followed by a lecturing position (molecular biology, genetics, medical science) at the University of Canberra. Research during this period involved investigations of immuno-pathogenesis associated with Ross River virus (RRV) infection, with key findings published on the molecular basis of antibody-dependent enhancement (ADE - associated with several viruses, including dengue), models of muscle and bone pathology post-infection, and a model of long-term viral persistence in host cells.
Further research on virus-host interaction and pathogenesis was conducted while attached to the Department of Microbiology and Immunology at the University of North Carolina-Chapel Hill in the United States, supported by an NIH-R01 grant.
From 2010 the method of research investigation switched from a laboratory - experimental focus to machine learning (ML) data driven methods, and platforms. This change in method was inspired by the quest to develop animal-free alternatives for biomedical research.
In the context of computational methods, recent fruitful international collaboration has been conducted with colleagues in the Department of Health Evidence, Radboudumc, Nijmegen (The Netherlands), particularly research concerning the development of machine learning supported systematic review to encourage non-animal methods for experimentation and testing. ML also has been successfully applied to a number of problems in laboratory medicine, including the development of a decision support algorithm for the early detection of Hepatitis B virus (HBV) in Australia and Nigeria.
In addition to the above, I have experience in diagnostic pathology (Science Fellowship with the RCPA), and for a period served as a senior toxicologist with the Therapeutic Goods Administration.
Affiliations
- , Researcher
Research interests
Previous laboratory-based interests in virology and pathogenesis have moved in silico, with the application of machine-learning / pattern-recognition techniques to support the study of human susceptibility or resistance to disease post viral infection (HBV; Post-viral Syndromes - see below). Techniques include recursive partitioning (trees) and support vector machines (SVMs), as both classification and regression applications to biomedical data. This research theme has diversified into other aspects of quality in diagnostic pathology, supported by the Quality Use of Pathology Programme (QUPP - Commonwealth Department of Health), and in collaboration with the Royal College of Pathologists of Australasia Quality Assurance Programme (RCPAQAP), as well as public and private pathology laboratories.
Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) studies are ongoing in collaboration with La Trobe University, Bio21 Institute (University of Melbourne), Macquarie University, and our community partner Emerge Australia. As a human-centred programme, we rely upon research participant volunteers and interaction with clinical collaborators, with additional support provided by Emerge. The ME/CFS focus has recently extended into long COVID and a deeper emphasis on post-viral (fatigue) syndromes.
ME/CFS projects were funded by the Judith Jane Mason Foundation, Alison Hunter Memorial Foundation and ME Research UK. With Emerge Australia and collaborators listed above, a programme is underway to develop Australia's first ME/CFS Biobank, with funding again generously provided by the Mason Foundation.
Projects
Teaching information
ME Pathology in silico - There are data available from past and current projects that will benefit from student involvement. A background in medical science and experience in statistics and/or data mining will be required. Other ME projects are available using systematic review methods and meta-analyses. With the advent of COVID-19, research projects will aim to explore the pathogenesis of post-viral fatigue more broadly.
Infection and Immunity in silico: Hepatitis B virus - Biological validation of machine learning models for HBV infection and disease, suitable for a medical science graduate with experience in laboratory diagnosis or pathology testing - involvement in this project will require specialised training and vaccination prior to commencement, due to contact with potentially infected human samples. Some experience in statistics and/or machine learning will be advantageous.
Genetics and Machine Learning - Through research collaborator Professor Mauricio Arcos-Burgos, future projects for students with an interest in human genetics will be available, primarily as in silico investigations on complex diseases like ME.
Supervisors (for students)
- Brett Lidbury
- Alice Richardson
- Tony Badrick (Hon. Associate Professor)
- Katrina Roper (Hon. Senior Lecturer)
- Mauricio Arcos-Burgos (Colombia)
Location
Room 2.43, Building 62