Developing Interpretable Machine Learning Models For Clinical Imaging And Single-cell Genomics
Funder
National Health and Medical Research Council
Funding Amount
$1,312,250.00
Summary
Machine learning methods will be vital to make best use of the deluge of data generated by high-throughput technologies in biomedical science. To get the most out of these models, however, we need to be able to unpack the 'black box'. I will use curated clinical and public research data to benchmark and develop interpretable deep learning models and software tools. These models will be used for breast cancer screening programs and for analysis of complex, large-scale single-cell genomics data.
Investigating Post-transcriptional Gene Regulation In Cancer
Funder
National Health and Medical Research Council
Funding Amount
$645,205.00
Summary
In this program, I will enhance our understanding of cancer gene regulation and provide novel avenues for the treatment of aggressive tumours. Using own data and that from collaborators, I will determine patterns of gene regulation in blood cancers and identify markers that predict disease outcome. I aim to understand how gene regulation can transform healthy cells into tumour cells and whether personalised treatment can kill tumour cells more effectively and prevent relapse and metastasis.