Abstract
Pablo Moscato speaks to Francesca Lake, Managing Editor Australian Research Council Future Fellow Prof. Pablo Moscato was born in 1964 in La Plata, Argentina. Obtaining his B.Sc. in Physics at University of La Plata, his PhD was defended at UNICAMP, Brazil. While at the California Institute of Technology Concurrent Computation Program he developed, in collaboration with Michael Norman, the first application of a methodology later called ‘memetic algorithms’, which is now widely used internationally. He is the founding co-director of the Priority Research Centre for Bioinformatics, Biomarker Discovery and Information-based Medicine (CIBM) (2006–present) and the funding director of the Newcastle Bioinformatics Initiative (2002–2006) of The University of Newcastle (Australia). He is also Chief Investigator of the Australian Research Council Centre in Bioinformatics. He is one of Australia's most cited computer scientists. Over the past 7 years, he has introduced a unifying hallmark of cancer progression based on the changes of information theory quantifiers, and developed a novel mathematical model and an associated solution procedure based on combinatorial optimization techniques to identify drug combinations for cancer therapeutics. In addition, he has identified proteomic signatures to predict the clinical symptoms of Alzheimer's disease, among other ‘firsts’. He is a member of the Editorial Board of Future Science OA.
Highlights
It was only when I moved to Auscareer to-date, & how you came into tralia that I had the right moment and condibioinformatics?
I worked from 2007 to 2010 on the theme of detecting biomarkers of use based on covariations of gene expression with information theory-quantifiers in cancer and Alzheimer’s Disease
In the area of cancer, we have shown that the use of Information Theory can guide the selection of biomarkers which allow us to track the progression of the disease
Summary
It was only when I moved to Auscareer to-date, & how you came into tralia that I had the right moment and condibioinformatics?. I worked from 2007 to 2010 on the theme of detecting biomarkers of use based on covariations of gene expression with information theory-quantifiers in cancer and Alzheimer’s Disease. We basically propose a new paradigm here for biomarker discovery, understanding gene expression as ‘a message’ and tracking progressions of disease by monitoring the changes in its information content.
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