Uncovering biologically relevant Autism subtypes using advanced machine learning techniques
ASD biotypes
DOI:
https://doi.org/10.46570/utjms-2024-1180Keywords:
autism, biotypes, multimodal, machine learning, random forest, resting-state functional connectivity, superior temporal sulcus, salience network, Symposium
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Published
2024-11-21
How to Cite
Vento, C. "Gabby", Cubells, J., Young, L., & Andari, E. (2024). Uncovering biologically relevant Autism subtypes using advanced machine learning techniques: ASD biotypes. Translation: The University of Toledo Journal of Medical Sciences, 13(S1). https://doi.org/10.46570/utjms-2024-1180
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Departments of Neuroscience and Psychiatry Research Symposium
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Copyright (c) 2024 Gabby, Joseph Cubells, Larry Young, Elissar Andari (Author)
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