Predicting Autism Spectrum Disorder Using Blood-based Gene Expression Signatures and Machine Learning
Clinical Psychopharmacology and Neuroscience
;
: 47-52, 2017.
Article
Dans Anglais
| WPRIM
| ID: wpr-41579
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.
Texte intégral:
Disponible
Indice:
WPRIM (Pacifique occidental)
Sujet Principal:
Trouble autistique
/
Marqueurs biologiques
/
Expression des gènes
/
Études de cohortes
/
Techniques d'aide à la décision
/
Sensibilité et spécificité
/
Analyse sur microréseau
/
Transcriptome
/
Machine à vecteur de support
/
Ensemble de données
Type d'étude:
Etude diagnostique
/
Etude d'étiologie
/
Etude d'incidence
/
Étude observationnelle
/
Étude pronostique
/
Facteurs de risque
Limites du sujet:
Humains
langue:
Anglais
Texte intégral:
Clinical Psychopharmacology and Neuroscience
Année:
2017
Type:
Article
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