Predicting Autism Spectrum Disorder Using Blood-based Gene Expression Signatures and Machine Learning.
- Author:
Dong Hoon OH
1
;
Il Bin KIM
;
Seok Hyeon KIM
;
Dong Hyun AHN
Author Information
- Publication Type:Original Article
- Keywords: Autism spectrum disorder; Blood; Microarray analysis; Transcriptome; Machine learning; Decision support techniques
- MeSH: Autism Spectrum Disorder*; Autistic Disorder*; Biomarkers; Cohort Studies; Dataset; Decision Support Techniques; Gene Expression*; Humans; Machine Learning*; Microarray Analysis; Sensitivity and Specificity; Support Vector Machine; Transcriptome*; Young Adult
- From:Clinical Psychopharmacology and Neuroscience 2017;15(1):47-52
- CountryRepublic of Korea
- Language:English
- 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 learning algorithms. RESULTS: Hierarchical cluster analysis showed that subjects with ASD were relatively well-discriminated from controls. Based on the support vector machine and K-nearest neighbors analysis, validation of 19-DE probes with a test dataset resulted in an overall class prediction accuracy of 93.8% as well as a sensitivity and specificity of 100% and 87.5%, respectively. CONCLUSION: The results of our exploratory study suggest that the gene expression profiles identified from the peripheral blood samples of young adults with ASD can be used to identify a biological signature for ASD. Further study using a larger cohort and more homogeneous datasets is required to improve the diagnostic accuracy.
