Description
Tumour formation and progression are driven by widespread changes in gene regulation. We have combined machine learning with gene co-expression analysis to identify transcriptional signatures predictive of time to progression from Grade II/III to Grade IV tumours in glioblastoma, a common adult brain cancer. This project aims to improve the predictive power of our machine learning models and explore gene regulatory changes driving glioblastoma progression using single-cell and spatial transcriptomics data.
Knowledge of R, Python or another programming language required.
Essential criteria:
Minimum entry requirements can be found here: https://www.monash.edu/admissions/entry-requirements/minimum
Keywords
cancer, machine learning, transcriptomics, genomics, bioinformatics, tumour evolution, single-cell, glioblastoma
School
Biomedicine Discovery Institute (School of Biomedical Sciences) » Biochemistry and Molecular Biology
Available options
PhD/Doctorate
Masters by research
Honours
Time commitment
Full-time
Part-time
Top-up scholarship funding available
No
Physical location
15 Innovation Walk
Research webpage