Using machine learning models, researchers at Michigan Medicine have identified a potential way to diagnose amyotrophic lateral sclerosis, or ALS, earlier from a blood sample, a study suggests. The models, which analyze blood for biomarkers through gene expression with RNA sequencing to detect ALS, also have the potential to predict disease severity—and how long a person might live with the neurodegenerative condition.
Investigators found more than 2,500 unique genes that express differently in ALS compared to controls, many of which were linked to the immune system. They input the data into a machine learning model, XGBoost, which they trained to predict whether ALS was present. After narrowing panels down to contain between 27 and 46 genes, the model predicted ALS with up to 91% accuracy.
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