Predictive Models To Design And Develop New Antibiotics Derived From The Community For Open Antimicrobial Drug Discovery
Funder
National Health and Medical Research Council
Funding Amount
$977,427.00
Summary
With the rise of infections from multidrug-resistant bacteria, and limited antibiotics in the development pipeline, new strategies are required to generate novel antibiotics. This project will apply artificial intelligence methods to study a unique dataset generated over five years with the help of over 300 academic groups around the world. It will produce predictive models that will then be applied to design new antibiotics, which will be synthesized and tested for antimicrobial activity.
DEEP LEARNING AND PHYSIOLOGY BASED APPROACH TO DERIVE AND LINK OBSTRUCTIVE SLEEP APNOEA PHENOTYPES AND SYMPTOMATOLOGY
Funder
National Health and Medical Research Council
Funding Amount
$402,978.00
Summary
Obstructive sleep apnoea (OSA) is a highly prevalent nocturnal breathing disorder strongly related to daytime sleepiness, accident risk and reduced quality of life. However, the current severity index, the apnoea-hypopnoea index, poorly predicts daytime sleepiness and vigilance. In this project we elegantly combine physiological insight and artificial intelligence to develop and evaluate novel clinically applicable computational tools for detailed quantification of OSA severity and its symptoms.