From Data to Insights: Machine Learning Empowers Prognostic Biomarker Prediction in Autism
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 a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Journal of Personalized Medicine |
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| Online Access: | https://www.mdpi.com/2075-4426/13/12/1713 |
| _version_ | 1797380385104986112 |
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| author | Ecmel Mehmetbeyoglu Abdulkerim Duman Serpil Taheri Yusuf Ozkul Minoo Rassoulzadegan |
| author_facet | Ecmel Mehmetbeyoglu Abdulkerim Duman Serpil Taheri Yusuf Ozkul Minoo Rassoulzadegan |
| author_sort | Ecmel Mehmetbeyoglu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T20:36:28Z |
| format | Article |
| id | doaj.art-0b5d389b6f4c4355971805a638bcdd41 |
| institution | Directory Open Access Journal |
| issn | 2075-4426 |
| language | English |
| last_indexed | 2024-03-08T20:36:28Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Personalized Medicine |
| spelling | doaj.art-0b5d389b6f4c4355971805a638bcdd412023-12-22T14:20:03ZengMDPI AGJournal of Personalized Medicine2075-44262023-12-011312171310.3390/jpm13121713From Data to Insights: Machine Learning Empowers Prognostic Biomarker Prediction in AutismEcmel Mehmetbeyoglu0Abdulkerim Duman1Serpil Taheri2Yusuf Ozkul3Minoo Rassoulzadegan4Department of Cancer and Genetics, Cardiff University, Cardiff CF14 4XN, UKSchool of Engineering, Cardiff University, Cardiff CF24 3AA, UKBetul-Ziya Eren Genome and Stem Cell Center, Erciyes University, Kayseri 38280, TurkeyBetul-Ziya Eren Genome and Stem Cell Center, Erciyes University, Kayseri 38280, TurkeyBetul-Ziya Eren Genome and Stem Cell Center, Erciyes University, Kayseri 38280, TurkeyAutism 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.https://www.mdpi.com/2075-4426/13/12/1713autismmiRNAsmachine learning |
| spellingShingle | Ecmel Mehmetbeyoglu Abdulkerim Duman Serpil Taheri Yusuf Ozkul Minoo Rassoulzadegan From Data to Insights: Machine Learning Empowers Prognostic Biomarker Prediction in Autism Journal of Personalized Medicine autism miRNAs machine learning |
| title | From Data to Insights: Machine Learning Empowers Prognostic Biomarker Prediction in Autism |
| title_full | From Data to Insights: Machine Learning Empowers Prognostic Biomarker Prediction in Autism |
| title_fullStr | From Data to Insights: Machine Learning Empowers Prognostic Biomarker Prediction in Autism |
| title_full_unstemmed | From Data to Insights: Machine Learning Empowers Prognostic Biomarker Prediction in Autism |
| title_short | From Data to Insights: Machine Learning Empowers Prognostic Biomarker Prediction in Autism |
| title_sort | from data to insights machine learning empowers prognostic biomarker prediction in autism |
| topic | autism miRNAs machine learning |
| url | https://www.mdpi.com/2075-4426/13/12/1713 |
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