Blood biomarker discovery for autism spectrum disorder
2021
The Johnson Center for Child Health and Development, Austin, USA
Using machine learning tools to analyse hundreds of proteins, UT Southwestern researchers have identified a group of biomarkers in blood that could lead to an earlier diagnosis of children with autism spectrum disorder (ASD) and, in turn, earlier and more effective therapies.
For the study, serum samples from 76 boys with ASD and 78 from typically developing boys, all ages 18 months to 8 years, were examined.
More than 1,100 proteins were examined using the SomaLogic SOMAScanTM analysis platform. A panel of nine proteins was identified as optimal for predicting ASD using three computational methods. methods. All nine proteins in the biomarker panel were significantly different in boys with ASD compared with typically developing boys and were significantly correlated with ASD severity as measured by ADOS (Autism Diagnostic Observation Schedule) total scores. The researchers evaluated the biomarker panel's quality using machine learning.
Blood biomarker discovery for autism spectrum disorder: A proteomic analysis
Laura Hewitson
Added on: 03-05-2021
[1] https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0246581[2] https://www.technologynetworks.com/neuroscience/news/machine-learning-helps-identify-autism-blood-biomarkers-345993
