Rebecca Howard, PhD student
Distinguishing asthma and allergy endotypes using longitudinal probabilistic modelling
- Aiming to find out whether the diagnostics for asthma and allergy in children and young adults can be improved using a new test that relies on immunoglobulin response levels, and whether the development trajectories and severity of these conditions can be predicted from a young age
Start: September 2014
End: September 2017
Funded by: Medical Research Council
Asthma and allergy
- Manchester Asthma and Allergy Study (MAAS) mainly immunoglobulin levels. Results shall be related to the corresponding patient, and clinician reported data and other laboratory measurements
- Bayesian focus, have performed Markov Chain Monte Carlo (MCMC) to derive the groupings of the study participants and of the allergen components that the immunoglobulin levels are tested against
Benefits and Outcomes
This research could benefit asthma and allergy sufferers. In the UK it’s estimated that 1.1 million children have asthma, and that up to 8% of children have an allergy, so the scope for impact is huge.
The aim is to find out whether testing immunoglobulin levels can be an effective tool in predicting the likelihood of a person developing asthma and/or allergies, and the severity of them. Knowing this information could influence treatment from a young age.
Prof. Magnus Rattray
Prof. Adnan Custovic
Distinguishing Asthma Phenotypes Using Machine Learning Approaches (Howard et al., 2015) in Current Allergy and Asthma Reports (doi: 10.1007/s11882-‐015-‐0542-‐0).