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. 2023 Dec 15;13(12):1713.
doi: 10.3390/jpm13121713.

From Data to Insights: Machine Learning Empowers Prognostic Biomarker Prediction in Autism

Ecmel Mehmetbeyoglu  1   2 , Abdulkerim Duman  3 , Serpil Taheri  2   4 , Yusuf Ozkul  2   5 , Minoo Rassoulzadegan  2   6
Affiliations

From Data to Insights: Machine Learning Empowers Prognostic Biomarker Prediction in Autism

Ecmel Mehmetbeyoglu et al. J Pers Med. .

Abstract

Autism Spectrum Disorder (ASD) poses significant challenges to society and science due to its impact on communication, social interaction, and repetitive behavior patterns in affected children. The Autism and Developmental Disabilities Monitoring (ADDM) Network continuously monitors ASD prevalence and characteristics. In 2020, ASD prevalence was estimated at 1 in 36 children, with higher rates than previous estimates. This study focuses on ongoing ASD research conducted by Erciyes University. Serum samples from 45 ASD patients and 21 unrelated control participants were analyzed to assess the expression of 372 microRNAs (miRNAs). Six miRNAs (miR-19a-3p, miR-361-5p, miR-3613-3p, miR-150-5p, miR-126-3p, and miR-499a-5p) exhibited significant downregulation in all ASD patients compared to healthy controls. The current study endeavors to identify dependable diagnostic biomarkers for ASD, addressing the pressing need for non-invasive, accurate, and cost-effective diagnostic tools, as current methods are subjective and time-intensive. A pivotal discovery in this study is the potential diagnostic value of miR-126-3p, offering the promise of earlier and more accurate ASD diagnoses, potentially leading to improved intervention outcomes. Leveraging machine learning, such as the K-nearest neighbors (KNN) model, presents a promising avenue for precise ASD diagnosis using miRNA biomarkers.

Keywords: autism; machine learning; miRNAs.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The flowchart of the machine learning method for identification of miRNAs. K-nearest neighbors (KNN); support vector machine (SVM); random forest (RF).
Figure 2
Figure 2
Box violin plots of relative miR-3613-3p (A), miR-150-5p (B), miR-19a-3p (C), miR-361-5p (D), miR-499a-5p (E), miR-126-3p (F); expression level in ASD (n = 45) and healthy control (n = 21). Asterisks denote significant differences by unpaired t-test, **** < 0.0001.
Figure 3
Figure 3
Evaluation of the diagnostic effectiveness of autism-specific miRNAs’ biomarkers. ROC curve of (A) miR-150-5p, (B) miR-19a-3p, (C) miR-361-5p, (D) miR-499a-5p, (E) miR499a-5p, and (F) miR-126-3p with area under the curve (AUC) values. ROC, receiver operating characteristics.
Figure 4
Figure 4
Pearson’s correlation analysis of six miRNAs. (A) Correlations between miRNAs in the ASD patients. (B) Correlations between six miRNAs in the healthy control.
Figure 5
Figure 5
Confusion matrix for all ML models. The confusion matrix was used to compare the different machine learning algorithms.
Figure 6
Figure 6
Functional enrichment analysis of target genes based on gene ontology regarding molecular function and (A) KEGG pathway analysis. (B) mTOR pathway analysis IRS1, PIK3R2, and VEGFA.
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Grants and funding

The APC was funded by Cardiff University.

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