Researchers used artificial intelligence and genetic data from more than 43,000 people to identify biological patterns that could reshape how one of the world’s most common neurological disorders is diagnosed and treated.

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    The researchers found that machine-learning-based genetic risk scores distinguished the groups better than conventional polygenic risk scores.

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