Uncovering biologically relevant Autism subtypes using advanced machine learning techniques

Authors

DOI:

https://doi.org/10.46570/utjms.vol12-2024-1222

Keywords:

autism, Machine learning, bioinformatics, neuroimaging, Symposium

Author Biographies

  • Christopher Vento, University of Toledo

    Doctoral Candidate - Bioinformatics 

    Laboratory of Autism & Social Affective Neuroscience (ASAN) 

    College of Medicine and Life Sciences 

    Department of Psychiatry

    Department of Neuroscience

    University of Toledo 

  • Joseph Cubells, Emory University - Department of Genetics

    Emory University

    Department of Human Genetics
    Associate Professor

  • Larry Young, Emory University

    Department of Psychiatry, Emory University School of Medicine

  • Elissar Andari, Univeristy of Toledo

    Assistant Professor

    Laboratory of Autism & Social Affective Neuroscience (ASAN)

    College of Medicine and Life Sciences

    Joint appointment

    Department of Neurosciences

    Department of Psychiatry

    University of Toledo

     

    Adjunct Assistant Professor

    Department of Psychiatry and Behavioral Sciences

    Emory University

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Published

2024-05-31

Issue

Vol. 12 No. 3 (2024): Graduate Research Annual Forum

Section

Perspectives in Psychiatry: A Learner’s Viewpoint

License

Copyright (c) 2024 Gabby , Joseph Cubells, Larry Young, Elissar Andari (Author)

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How to Cite

1.
Uncovering biologically relevant Autism subtypes using advanced machine learning techniques. Translation. 2024;12(3). doi:10.46570/utjms.vol12-2024-1222

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