As part of a prestigious program to encourage research collaboration, 23Strands has secured funding in partnership with the University of Technology Sydney (UTS). The Australian Research Council’s Linkage Program (ARC LP) aims to encourage and extend co-operative approaches to research and encourages the transfer of skills, knowledge and ideas as a basis for securing commercial and other benefits of research. The 23Strands/UTS project is developing novel approaches with artificial intelligence (AI) and machine-learning technologies to revolutionise our understanding of patterns and correlations within data from whole human genome sequencing and from deep phenotyping, which clinically characterises traits that signify health or disease, such as fever, rash or irregular heartbeat. The understanding of correlated patterns will be embedded into our person-centered medical platform to support clinicians and health professionals to make decisions effectively, deliver high-quality personalised care for patients, and ultimately provide significant impact to the health system as a whole.High-level objectives of the ARC LP project are to:
- Develop a novel AI-empowered approach for categorising risk and for predicting infertility and cardiovascular disease based on whole human genome sequencing:
- Develop a personalised recommendation framework via genotype and phenotype knowledge that correlates and quantifies patterns for clinical application: and
- Develop a novel methodology for patient healthcare pathway recommendation with limited health information. The chief research investigators for the project are Professor Jie Lu, AO, Associate Dean (research), Faculty of Engineering and IT at UTS; Dr Yi Zhang, Senior Lecturer at UTS Australian Artificial Intelligence Institute; and Dr Hua Lin, Chief Data Officer at 23Strands. The project would not be possible without the expertise of the following 23Strands team members recruited after an intensive process to work specifically on the project.
Yue (Eric) Yang received his Bachelor of Engineering Honours (Biomedical) from UTS in 2022. He is currently a PhD student at the Australian Artificial Intelligence Institute, Faculty of Engineering and Information Technology, UTS. His research interests include machine learning and transfer learning applications in genomics.
Dr Kairui Guo received his Bachelor of Science degree from the University of Melbourne in 2014 and his Master of Engineering from the UTS in 2016. Kairui completed his PhD at UTS in Biomedical Engineering. He is now a postdoctoral research associate at the Australian Artificial Intelligence Institute, UTS. His research interests are artificial intelligence in genetics and genomics, biosignal analysis, and machine learning applications in neuroscience research.
Mengjia Wu received B.Sc. and MA degrees in information science from HuazhongUniversity of Science and Technology, Wuhan, China. Currently, he is in the final stage of his Ph.D. at the Australian Artificial Intelligence Institute, Faculty of Engineering and Information Technology, UTS, with his doctoral dissertation under examination. Mengjia has authored/co-authored more than 20 conference/journal papers in bibliometric and cross-disciplinary venues, including Technological Forecasting and Social Change, Advanced Engineering Informatics, Scientometrics, and Journal of Informetrics. His research interests include leveraging bibliometrics, text analytics, network analytics and graph neural networks to develop and optimise knowledge extraction and discovery models. In 2021, Mr.Wu was granted the ISSI student travel prize from the International Society for Informetrics and Scientometrics.
Zelia Soo received her Bachelor of Engineering Honours (Biomedical)/Bachelor of Science (Medical Science) from the University of Sydney in 2022. She is currently a PhD student at the Australian Artificial Intelligence Institute, Faculty of Engineering and Information Technology, UTS. Her research interests include machine learning applications in genomics and bioinformatics.