This software would rapidly identify disease or symptom exacerbators that may be due to genetic mutation of environmental stressors in individuals with ME/CFS. This will provide a better understanding of the symptom expression in ME/CFS and help identify secondary issues or chronic triggers that are exacerbating/maintaining the disease.
Identifying these issues or stressors will enable them to be more accurately treated and improve quality of life in people with ME/CFS
The software developed would use machine learning to identify outlier anomalies that are consistent across genomics, proteomics, and metabolomics data from an individual. A report would be produced to highlight the probability of genetic mutations relating to a functional effect.
The data required to develop this software will be immense and will consist of many previously produced datasets from our collaborators. A significant amount of priming and testing of this software will occur through the accumulation of genomics, metabolomics and symptom data from 300 ME / CFS patients.
Gene mutations, metabolite levels and symptom data will be analyzed to identify outlier metabolites and metabolites that relates strongly to symptom data. Genetic mutations and environmental factors will be considered causes of these outliers by machine-learning technology.
Software will be developed specifically to store patterns developed from patient data and more rapidly check those patterns against new patient data to rapidly identify potential issues that may be exacerbating or triggering symptoms in people with ME / CFS.