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From data to insights: machine learning empowers prognostic biomarker prediction in Autism

Mehmetbeyoglu Duman, Ecmel, Duman, Abdulkerim, Taheri, Serpil, Ozkul, Yusuf and Rassaulzadegan, Minoo 2023. From data to insights: machine learning empowers prognostic biomarker prediction in Autism. Journal of Personalized Medicine 13 (12) , 1713. 10.3390/jpm13121713

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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 downreg- ulation 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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Subjects: R Medicine > R Medicine (General)
Publisher: MDPI
ISSN: 2075-4426
Date of First Compliant Deposit: 2 January 2024
Date of Acceptance: 12 December 2023
Last Modified: 11 Jan 2024 10:22

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