Predicting Autism Spectrum Disorder Using Blood-based Gene Expression Signatures and Machine Learning.

ABSTRACT:

Objective

The aim of this study was to identify a transcriptomic signature that could be used to classify subjects with autism spectrum disorder (ASD) compared to controls on the basis of blood gene expression profiles. The gene expression profiles could ultimately be used as diagnostic biomarkers for ASD.

Methods

We used the published microarray data (GSE26415) from the Gene Expression Omnibus database, which included 21 young adults with ASD and 21 age- and sex-matched unaffected controls. Nineteen differentially expressed probes were identified from a training dataset (n=26, 13 ASD cases and 13 controls) using the limma package in R language (adjusted p value ＜0.05) and were further analyzed in a test dataset (n=16, 8 ASD cases and 8 controls) using machine learnin

SUBMITTER: Oh DH 

PROVIDER: S-EPMC5290715 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature