Researchers from Northwestern University, Ben Gurion University, Harvard University, and MIT have utilized AI-enhanced precision medicine to identify a novel subtype of autism.
This groundbreaking approach provides a multidimensional evidence-based classification of autism and could pave the way for the development of targeted biomedical screening and intervention tools.
Autism currently affects approximately 1 in 54 children in the US, with boys being four times more likely to be diagnosed. The research, led by Dr. Yuan Luo, utilized a precision medicine approach to study dyslipidemia-associated autism, which represents 6.55% of all diagnosed autism spectrum disorders in the US. By overlaying various research and healthcare data, including genetic mutation data, the team was able to define this specific subtype of autism.
They used an AI algorithm graph clustering technique on gene expression data to identify clusters of gene exons that function together during brain development. Exons are the coding parts of genes that play a crucial role in cell and organ function, including the brain.
This approach of overlaying different layers of information could be applied to subtyping other genetically complex diseases as well. The discovery of the association between dyslipidemia and autism has led to subsequent studies and clinical trials aiming to promote early screening and intervention for autism, potentially changing the current diagnostic paradigm.
The original article can be found here.
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