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Biomarkers in mother s plasma predict a type of autism in offspring with 100% accuracy

 E-Mail Using machine learning, researchers at the UC Davis MIND Institute have identified several patterns of maternal autoantibodies highly associated with the diagnosis and severity of autism. Their study, published Jan. 22 in Molecular Psychiatry, specifically focused on maternal autoantibody-related autism spectrum disorder (MAR ASD), a condition accounting for around 20% of all autism cases. The implications from this study are tremendous, said Judy Van de Water, a professor of rheumatology, allergy and clinical immunology at UC Davis and the lead author of the study. It s the first time that machine learning has been used to identify with 100% accuracy MAR ASD-specific patterns as potential biomarkers of ASD risk.

Maternal Autoantibodies Highly Associated With Diagnosis of Autism

Read Time: Using machine learning, researchers at the UC Davis MIND Institute have identified several patterns of maternal autoantibodies highly associated with the diagnosis and severity of autism. Their study, published in  Molecular Psychiatry, specifically focused on maternal autoantibody-related autism spectrum disorder (MAR ASD), a condition accounting for around 20% of all autism cases. The implications from this study are tremendous, said Judy Van de Water, a professor of rheumatology, allergy and clinical immunology at UC Davis and the lead author of the study. It s the first time that machine learning has been used to identify with 100% accuracy MAR ASD-specific patterns as potential biomarkers of ASD risk.

Machine learning identifies patterns of maternal autoantibodies associated with autism

Machine learning identifies patterns of maternal autoantibodies associated with autism Using machine learning, researchers at the UC Davis MIND Institute have identified several patterns of maternal autoantibodies highly associated with the diagnosis and severity of autism. Their study, published Jan. 22 in Molecular Psychiatry, specifically focused on maternal autoantibody-related autism spectrum disorder (MAR ASD), a condition accounting for around 20% of all autism cases. The implications from this study are tremendous. It s the first time that machine learning has been used to identify with 100% accuracy MAR ASD-specific patterns as potential biomarkers of ASD risk. Judy Van de Water, Professor of Rheumatology, Allergy and Clinical Immunology, UC Davis and Study s Lead Author

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