Investigators
Brad Zhang, Peter Le Souef, Ingrid Laing
Project description
The study will address the main deficits in asthma research that have delayed progress in treating and preventing asthma over the past 20 or 30 years: lack of understanding of why only selected children develop asthma and the lack of early biomarkers to detect these children. This project has the potential to open the way for the development of entirely new therapies to counter epigenetic changes and achieve the large-scale prevention of the development of asthma.
Aims
- To use machine learning to integrate and decipher all epigenomic, genomic and clinical data to identify the epigenetic changes that predict repeated hospital presentation of acute wheezing in children and the later development of asthma.
- To use this approach to define endotypes of childhood asthma.
External collaborators
Ryan Mead-Hunter (Curtin University), Adnan Custovic (Imperial College London).