NICTA Bioinformatics Group Photo, March 2013 (left to right): Geoff Macintyre, David Rawlinson, Haroon Naeem, Fan Shi, Adam Kowalczyk, Qiao Wang, Justin Bedő, Cheng Soon-Ong, Cristovao Freitas Iglesias Jr. There are several people from the group not included in this photo. For a full list of staff and students, past and present, visit the ‘People’ page in the links above.
Genomics for understanding disease and improving medicine
What problem are we solving?
Advances in molecular profiling technologies have resulted in large quantities of data being generated for a range of human diseases. The key challenge is to systematically analyze these diverse datasets to deepen our understanding of the molecular basis of disease, and to improve diagnosis and prognosis. For complex diseases, it is crucial to be able to simultaneously utilize genomic information and clinical data for accurate diagnosis. We focus on major killers such as cancer, diabetes and heart disease, where we integrate information in a clinical setting to build predictive models.
How are we doing it?
We develop novel statistical methods that lead to efficient computational tools for systematically analyzing large scale biomedical data. In close collaboration with medical specialists and pathologists, we develop software tools that extract timely information from the sea of data.
- Using genome wide association studies (GWAS) from the University of Melbourne, we investigate the role of higher order genetic interactions in breast cancer in partnership with the IBM Collaboratory for Life Sciences.
- In collaboration with Epworth Medical Centre, we are developing methods to integrate heterogenous genomics data from primary tissue samples to discover Â prognostic markers for lethal prostate cancer.
- To discover the genetic basis of diabetes and heart disease, we use lipidomics and GWAS data in collaboration with Baker IDI Heart and Diabetes Institute.
- In a private-public partnership with Circadian Technologies Limited, Healthscope, and Peter MacCallum Cancer Centre, we have developed a predictive tool for metastatic cancers of unknown primary. This commercial test is currently undergoing clinical trials.
What impact will it have?
Efficient large scale tools for analysis of genomic data will improve our understanding of human disease at the molecular level. Technological advances mean that the trend is now towards personalized medicine, where information about a patient’s genetics, as well as their clinical data, is used to select medication, therapy or preventative measures.