Rapid detection of rare-event cells by strong UP-conversion
encoded nano-radiators (SUPER Dots): finding a needle in a haystack. Current diagnostic tests are not sensitive enough to detect cancer in its very early stages or early recurrence following treatment. The new technologies developed by this project will be able to find single cancer cells in blood and urine samples heralding a new era in medical diagnostics.
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE120100006
Funder
Australian Research Council
Funding Amount
$600,000.00
Summary
An adaptable and dedicated linear accelerator for medical radiation research. Leading radiation scientists developing innovative methods and devices for treating cancer patients will collaborate in future research using this highly adaptable linear accelerator for medical radiation research. Innovations in tumour targeting, better patient safety, new medical devices and improved cancer outcomes are expected.
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE110100078
Funder
Australian Research Council
Funding Amount
$500,000.00
Summary
Establishment of a comprehensive regional biophysical analysis facility. Interactions between molecules are needed for cells to function correctly. This facility will permit comprehensive molecular characterisation as well as research into the fundamentals of how molecules interact.
Prognosis based network-type feature extraction for complex biological data. This project aims to develop statistical tools that integrate high-throughput molecular data with biological knowledge to make discoveries in complex diseases. This project uses machine learning methods, statistical models and proteomic platforms to identify relationships among clinico-pathologic and molecular measurements. It will produce tools and insights that are intended to accelerate the process of biologically an ....Prognosis based network-type feature extraction for complex biological data. This project aims to develop statistical tools that integrate high-throughput molecular data with biological knowledge to make discoveries in complex diseases. This project uses machine learning methods, statistical models and proteomic platforms to identify relationships among clinico-pathologic and molecular measurements. It will produce tools and insights that are intended to accelerate the process of biologically and clinically significant discoveries in biomedical research. This project will help Australian researchers in statistics and users of statistics (from fields as diverse as biology, ecology, medicine, finance, agriculture and the social sciences) to make better predictions that are easier to understand.Read moreRead less