About Us
Interrogating genome and transcriptome data to identify novel cancer biomarkers and create new diagnostic tools.
Timely diagnosis and treatment of cancer can significantly impact a patient’s chances of recovery. Advances in genomics is enabling more rapid approaches, but swiftly managing the vast amounts of data generated can be challenging.
The Cancer Bioinformatics group led by Professor Lachlan Coin is developing and applying tools that analyse genomic and transcriptomic data to quickly identify significant cancer biomarkers, characterise disease state, and predict how well a patient might respond to treatment.
The group is working on streaming algorithms, utilising approaches from high-dimensional statistics, information theory and machine learning, including deep neural networks. They investigate collaborative learning from diverse, linked datasets.
These algorithms process data as soon as it is generated, enabling real-time analysis and visualisation of the most likely disease state and clinical outcomes, along with an understanding of any uncertainties that may decrease as more information is gathered.