Alzheimer’s Disease (AD) is the most common form of dementia, affecting up to 70% of all people with dementia. In Australia there are more than 350,000 living with dementia, and total direct health expenditure on people with dementia was at least $4.9 billion in 2009-10. Through the combination of rich data sources including an individual’s genomic signature, medical scans (MRI, PET and retinal scans), blood- and spinal fluid-based biomarker signatures, and cognitive tests, early detection of AD is becoming increasingly reliable, but the cost of diagnosis is high and methods for predicting the future onset and rate of cognitive decline of a patient are largely undeveloped. Through the use of the rich Australian Imaging Biomarker and Lifestyle (AIBL) dataset on ageing, this project will develop novel machine learning models to predict the age a patient is likely to develop AD, and the rate of their cognitive decline. Particular machine learning challenges in this project include: handling large block-wise missing data, common in large medically-focused studies; modelling temporal multimodal data; deep learning over large-scale sequential data, in the form of genomic data; and patient stratification, i.e. stratifying patients based on their expected prognosis, and using this to inform intervention and management strategies. A heavy focus will be placed on encoding and correcting for confounding factors, with an aim to derive interpretable models that can assist clinical outcomes.

Professor Colin Masters AO

Laboratory Head – Neuropathology and Neurodegeneration Laboratory
The Florey Institute of Neuroscience and Mental Health

In 1984, Beyreuther and Masters purified and sequenced the amyloid constituent of the plaque in Alzheimer’s disease, and three years later, their group used this sequence to clone the gene encoding the Aβ amyloid peptide located on chromosome 21. These studies of a demonstrated that the Aβ amyloid was derived by proteolytic cleavage neuronal transmembrane receptor. Subsequent studies by many groups has shown that a variety of Aβ-amyloid oligomers lie at the centre of AD pathogenesis, and these are now the validated primary targets for both diagnostic and therapeutic strategies. Masters and Beyreuther therefore defined the principal molecular and genetic pathways leading to the current Aβ amyloid theory of causation of Alzheimer’s disease.

More recent studies from Masters and colleagues have also demonstrated the time-course over which the Aβ accumulates in the evolution of Alzheimer’s disease, using molecular PET- Aβ imaging, allowing the preclinical and prodromal stages to be identified during life. They have also identified some of the genetic determinants which affect the rates of cognitive decline. These insights into the natural history of Alzheimer’s disease will have a major impact on clinical trial design and provide prognostic information for subjects at risk.

Dr Noel Faux

Research Staff Member
IBM Research – Australia

Dr Noel Faux is a researcher at IBM Research - Australia and an Honorary Research Fellow at The Florey Institute for Neuroscience and Mental Health. He has a strong research interest in precision medicine, with application in oncology and mental health/neuroscience, as well as the application of technology in mental health.

Dr Faux is a trained molecular biologist, specialised in genomic engineering, having worked in a small gene knockout company (IngenKO), before completing a Grad Dip in Computer Science followed by a PhD in Bioinformatics, received in 2009. His body of research has spanned from understanding the functional role of amino acid repeats and the genetic architecture that encodes them, protein structural informatics, to applying machine learning and biostatistics for the identification of blood based biomarkers for Alzheimer’s Disease. Dr Faux’s work in Alzheimer’s Disease was performed at Australia’s leading neuroscience institute, The Florey Institute for Neuroscience and Mental Health. Further, these work was listed in the top ten projects funded by the National Health and Medical Research Council (NHMRC) for 2014. Dr Faux has a deep technical expertise that crosses the boundaries of genomics, proteomics, mental health, neurodegenerative diseases, biostatistics, and informatics. All areas in which he has communicated to a varied audience, from the general public to technical experts.

Dr Victor Fedyashov

Research Fellow
The University of Melbourne

Dr Victor Fedyashov joined the Alzheimer's stream of the ARC Training Centre as a Research Fellow in April 2019. He is hoping that combining his background in mathematical modelling and computational statistics with other team members' vast domain expertise will lead to some unexplored avenues and result in a multitude of fresh insights.

Dr Fedyashov obtained his PhD in Mathematics (with specialisation in Probability Theory and Stochastic Analysis) from the University of Oxford in 2016 and has subsequently worked as a quantitative researcher at a hedge fund in London, utilising data mining and machine learning techniques in order to find new signals and build algorithms for predictive statistical modelling. His prior education includes an MSc in Maths and Computer Science from St.Petersburg State University (2010) and an MSc in Quantitative and Computational Finance from ETH Zurich (2012).