Machine Learning-Based Blood RNA Signature for Diagnosis of Autism Spectrum Disorder

被引:4
作者
Voinsky, Irena [1 ]
Fridland, Oleg Y.
Aran, Adi [2 ,3 ]
Frye, Richard E. [4 ,5 ]
Gurwitz, David [1 ,6 ]
机构
[1] Tel Aviv Univ, Fac Med, Dept Human Mol Genet & Biochem, IL-69978 Tel Aviv, Israel
[2] Shaare Zedek Med Ctr, IL-91031 Jerusalem, Israel
[3] Hebrew Univ Jerusalem, Inst Drug Res, Fac Med, Sch Pharm,Obes & Metab Lab, IL-91240 Jerusalem, Israel
[4] Autism Discovery & Treatment Fdn, Phoenix, AZ 85050 USA
[5] Rossignol Med Ctr, Phoenix, AZ 85050 USA
[6] Tel Aviv Univ, Sagol Sch Neurosci, IL-69978 Tel Aviv, Israel
关键词
machine learning; RNA biomarkers; blood RNA-sequencing; autism spectrum disorder (ASD); CHILDREN; RISK; GENE; IDENTIFICATION; COMPARABILITY; EXPRESSION;
D O I
10.3390/ijms24032082
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Early diagnosis of autism spectrum disorder (ASD) is crucial for providing appropriate treatments and parental guidance from an early age. Yet, ASD diagnosis is a lengthy process, in part due to the lack of reliable biomarkers. We recently applied RNA-sequencing of peripheral blood samples from 73 American and Israeli children with ASD and 26 neurotypically developing (NT) children to identify 10 genes with dysregulated blood expression levels in children with ASD. Machine learning (ML) analyzes data by computerized analytical model building and may be applied to building diagnostic tools based on the optimization of large datasets. Here, we present several ML-generated models, based on RNA expression datasets collected during our recently published RNA-seq study, as tentative tools for ASD diagnosis. Using the random forest classifier, two of our proposed models yield an accuracy of 82% in distinguishing children with ASD and NT children. Our proof-of-concept study requires refinement and independent validation by studies with far larger cohorts of children with ASD and NT children and should thus be perceived as starting point for building more accurate ML-based tools. Eventually, such tools may potentially provide an unbiased means to support the early diagnosis of ASD.
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页数:10
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