The team were able to identify unique combinations of metabolites that could prevent or potentially be early warning signs for Parkinson’s disease. (Credit: Shutterstock)

Scientists have developed a tool that shows early promise in detecting Parkinson’s disease years before the first symptoms start appearing. The researchers used neural networks to analyze biomarkers in patients’ bodily fluids.

The machine learning tool, called CRANK-MS, which stands for Classification and Ranking Analysis using Neural network, generates Knowledge from Mass Spectrometry. The researchers say the tool taked into account that metabolites can have associations with other metabolites — which is where the machine learning comes in.

The researchers examined blood samples taken from healthy individuals gathered by the Spanish European Prospective Investigation into Cancer and Nutrition (EPIC). Focusing on 39 patients who developed Parkinson’s up to 15 years later, the team ran their machine learning program over datasets containing extensive information about metabolites — the chemical compounds that the body creates when breaking down food, drugs or chemicals.

After comparing these metabolites to those of 39 matched control patients — people in the same study who didn’t go on to develop Parkinson’s — the team identified unique combinations of metabolites that could prevent or potentially be early warning signs for Parkinson’s.

The CRANK-MS tool is publicly available to any researchers who would like to use machine learning for disease diagnosis using metabolomics data.

Source 

Topics:
Artificial Intelligence Medical Predictive Models and Predictive Analytics

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