Using machine learning to identify blood biomarkers for early diagnosis of autism
Using machine learning tools to analyze 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, more effective therapies sooner.
The identification of nine serum proteins that strongly predict ASD were reported in a study published today by
PLOS ONE.
Earlier diagnosis, followed by prompt therapeutic support and intervention, could have a significant impact on the 1 in 59 children diagnosed with autism in the United States. Being able to identify children on the autism spectrum when they are toddlers could make a big difference, says Dwight German, Ph.D., professor of psychiatry at UT Southwestern and senior author of the study.