Uncovering biologically relevant Autism subtypes using advanced machine learning techniques

Authors

  • Gabby University of Toledo https://orcid.org/0000-0001-9516-3694
  • Joseph Cubells Emory University - Department of Genetics
  • Larry Young Emory University
  • Elissar Andari Univeristy of Toledo

DOI:

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

Keywords:

autism, Machine learning, bioinformatics, neuroimaging, Symposium

Author Biographies

Gabby , 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

Downloads

Published

2024-05-31

How to Cite

Vento, C., Cubells, J., Young, L., & Andari, E. (2024). Uncovering biologically relevant Autism subtypes using advanced machine learning techniques. Translation: The University of Toledo Journal of Medical Sciences, 12(3). https://doi.org/10.46570/utjms.vol12-2024-1222

Issue

Section

Graduate Research Annual Forum