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The Computational Systems Oncology and Bioinformatics program will create a centre of research excellence in advanced computational systems biology, taking advantage of novel technologies and high-density data from the clinic and laboratory, to develop new methods and research programs that will form an integral part of all of the basic and translational research excellence at SAiGENCI.

The first flagship project led from the Computational Systems Oncology program will involve the integration of 20 years’ worth of prostate cancer clinical trials and associated molecular data into a single integrated environment to support computational discovery using bioinformatics, biostatistics and machine learning.  This project will involve research from across SAiGENCI’s programs and will produce new knowledge with respect to where and why cancer appears and progresses, generate advanced data mapping and classification methods for targeting patient treatment, and reveal new insights into the molecular mechanisms of treatment response and resistance. This is an important emerging field in which SAiGENCI and the broader ×îÐÂÌÇÐÄVlog of Adelaide are well placed to develop an internationally recognised centre of excellence given the ×îÐÂÌÇÐÄVlog’s existing strengths in Artificial Intelligence, machine learning, data science and cancer research.

Artificial Intelligence for Biological Innovation (ABI Lab)

Group Leader -

Our group specialises in the intersection of Artificial Intelligence (AI)and Big Data in Bioinformatics.  Our research revolves around harnessing the power of AI and leveraging Big Data analytics to address various bioinformatics challenges in cancer studies.  By developing AI-driven bioinformatics tools, platforms, software, pipelines, and resources, our research aims to unravel the complexities of cancer and contribute to advancements in the field.

One key aspect of our research involves exploring gene regulation mechanisms in cancer.  We aim to uncover the intricate interplay between genetic factors and their impact on cancer development and progression by utilising AI algorithms and analysing large-scale genomic and epigenomic data.  Understanding these mechanisms can provide crucial insights into identifying biomarkers, potential therapeutic targets, and novel treatment strategies.

Additionally, our research focuses on developing innovative approaches for multi-omics data processing in cancer research.  With the integration of diverse omics datasets, such as genomics, transcriptomics, proteomics, and metabolomics, we aim to unravel the complex molecular networks underlying cancer.

By developing advanced machine learning approaches, our research aims to extract meaningful patterns and correlations from multi-omics data, enabling a comprehensive understanding of cancer biology.  Through our research endeavours, we strive to contribute to the field of Bioinformatics by providing novel insights and tools that facilitate precision medicine, personalised therapies, and improved patient outcomes in cancer research and treatment.

Data Sciences Unit

The Data Sciences Unit in the Computational Systems Oncology program is responsible for the integrity and storage of ‘omics data, biostatistics support for research staff, as well as the development of training modules for PhD and postdoctoral students.

People

Researchers