Using machine learning to find sub-groups of ALS and FTD based on a range of biological measures.
Principal Investigator: Dr Ahmad Al Khleifat
Lead Institution: King's College London
MND Association Funding: £161,999 - Junior Non-Clinical Fellowship*
Funding dates: January 2022 - September 2024
* Supported by the Lady Edith Wolfson Fellowship Programme
About the project
Currently, we do not know the best way to subgroup and classify neurodegenerative diseases since overlapping disease mechanisms are often not taken into account. Reclassifying these diseases will allow for the best targeted therapeutic approaches and potential treatments. This project involves reclassifying these diseases based on a combination of biological measures. This will include genetic profile, epigenetics (a system controlling whether genes are switched on or off) and the level of a nerve protein found in the blood called neurofilament. The variations of these three areas will be analysed for both MND and Frontal temporal dementia (FTD) and machine learning will then be used to find patterns that correspond to different subgroups. If this allows for the formation of new subgroups, then they can be used group people together for clinical trials and to understand the underlying biology of the conditions.
What could this mean for MND research?
MND is a highly complex disease, with lots of different biological pathways affected. As such the biological pathways affected can differ in people living with MND. It is thought that a range of different of new therapies will be needed to be effective for everyone living with MND. This project will work to further understand the biology behind MND and try to find sub-groups of MND. These sub-groups can then be used to put people living with MND together. It may then help us understand why treatments may be more beneficial to one group over the other and help us find treatments for each of the subgroups.
Project code: 975-799
