Machine Learning-Based Blood RNA Signature for Diagnosis of Autism Spectrum Disorder

Irena Voinsky, Oleg Y. Fridland, Adi Aran, Richard E. Frye*, David Gurwitz*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Early diagnosis of autism spectrum disorder (ASD) is crucial for providing appropriate treatments and parental guidance from an early age. Yet, ASD diagnosis is a lengthy process, in part due to the lack of reliable biomarkers. We recently applied RNA-sequencing of peripheral blood samples from 73 American and Israeli children with ASD and 26 neurotypically developing (NT) children to identify 10 genes with dysregulated blood expression levels in children with ASD. Machine learning (ML) analyzes data by computerized analytical model building and may be applied to building diagnostic tools based on the optimization of large datasets. Here, we present several ML-generated models, based on RNA expression datasets collected during our recently published RNA-seq study, as tentative tools for ASD diagnosis. Using the random forest classifier, two of our proposed models yield an accuracy of 82% in distinguishing children with ASD and NT children. Our proof-of-concept study requires refinement and independent validation by studies with far larger cohorts of children with ASD and NT children and should thus be perceived as starting point for building more accurate ML-based tools. Eventually, such tools may potentially provide an unbiased means to support the early diagnosis of ASD.

Original languageEnglish
Article number2082
JournalInternational Journal of Molecular Sciences
Issue number3
StatePublished - Feb 2023


  • RNA biomarkers
  • autism spectrum disorder (ASD)
  • blood RNA-sequencing
  • machine learning


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