Open AccessMultivariate techniques enable a biochemical classification of children with autism spectrum disorder versus typically‐developing peers: A comparison and validation study
Author(s) -
Howsmon Daniel P.,
Vargason Troy,
Rubin Robert A.,
Delhey Leanna,
Tippett Marie,
Rose Shan,
Bennuri Sirish C.,
Slattery John C.,
Melnyk Stepan,
James S. Jill,
Frye Richard E.,
Hahn Juergen
Publication year - 2018
Publication title -
bioengineering and translational medicine
Language(s) - English
Resource type - Journals
ISSN - 2380-6761
DOI - 10.1002/btm2.10095
Subject(s) - autism spectrum disorder , autism , multivariate statistics , linear discriminant analysis , multivariate analysis , neurodevelopmental disorder , cohort , psychology , population , medicine , developmental psychology , machine learning , artificial intelligence , computer science , pathology , environmental health
Autism spectrum disorder (ASD) is a developmental disorder which is currently only diagnosed through behavioral testing. Impaired folate‐dependent one carbon metabolism (FOCM) and transsulfuration (TS) pathways have been implicated in ASD, and recently a study involving multivariate analysis based upon Fisher Discriminant Analysis returned very promising results for predicting an ASD diagnosis. This article takes another step toward the goal of developing a biochemical diagnostic for ASD by comparing five classification algorithms on existing data of FOCM/TS metabolites, and also validating the classification results with new data from an ASD cohort. The comparison results indicate a high sensitivity and specificity for the original data set and up to a 88% correct classification of the ASD cohort at an expected 5% misclassification rate for typically‐developing controls. These results form the foundation for the development of a biochemical test for ASD which promises to aid diagnosis of ASD and provide biochemical understanding of the disease, applicable to at least a subset of the ASD population.