#data science

Prediction of molecular interactions with RNA using AI

This Project will develop deep-learning models to predict interactions of ribonucleic acid (RNA) with other molecules. The expected outcomes are to improve prediction capabilities to decode RNA interactions in disease mechanisms, identify novel therapeutic modalities, and improve existing therapies for targeting RNA. This could result in enhanced capacity to design new therapies and potential to optimise RNA targeting molecules for therapeutic applications. 

A cotton pangenome for enhanced genomic selection 

This Project will identify key representative lines of the Australian cotton breeding population to construct a cotton pangenome. The expected outcome is to construct a cotton pangenome using the lines which are most efficient in terms of labour and financial resources. This may help increase the accuracy of genomic selection and hence increase the rate of genetic gain. 

Communicating in the real world

This project aims to utilise existing sensor technologies to evaluate the impact of hearing loss when communicating in noisy environments. The expected outcome is the discovery of novel analysis methods that utilise multi-sensor fusion techniques. The potential benefit is improved outcomes for individuals with hearing loss.