{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T03:46:29Z","timestamp":1773805589777,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T00:00:00Z","timestamp":1702598400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Cardiff University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JPM"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/jpm13121713","type":"journal-article","created":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T03:16:33Z","timestamp":1702610193000},"page":"1713","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["From Data to Insights: Machine Learning Empowers Prognostic Biomarker Prediction in Autism"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4597-6811","authenticated-orcid":false,"given":"Ecmel","family":"Mehmetbeyoglu","sequence":"first","affiliation":[{"name":"Department of Cancer and Genetics, Cardiff University, Cardiff CF14 4XN, UK"},{"name":"Betul-Ziya Eren Genome and Stem Cell Center, Erciyes University, Kayseri 38280, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2747-1294","authenticated-orcid":false,"given":"Abdulkerim","family":"Duman","sequence":"additional","affiliation":[{"name":"School of Engineering, Cardiff University, Cardiff CF24 3AA, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Serpil","family":"Taheri","sequence":"additional","affiliation":[{"name":"Betul-Ziya Eren Genome and Stem Cell Center, Erciyes University, Kayseri 38280, Turkey"},{"name":"Department of Medical Biology, Erciyes University, Kayseri 38280, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yusuf","family":"Ozkul","sequence":"additional","affiliation":[{"name":"Betul-Ziya Eren Genome and Stem Cell Center, Erciyes University, Kayseri 38280, Turkey"},{"name":"Department of Medical Genetics, Erciyes University, Kayseri 38280, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minoo","family":"Rassoulzadegan","sequence":"additional","affiliation":[{"name":"Betul-Ziya Eren Genome and Stem Cell Center, Erciyes University, Kayseri 38280, Turkey"},{"name":"Inserm-CNRS, Universit\u00e9 C\u00f4te d\u2019Azur, 06107 Nice, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1016\/S0140-6736(18)31129-2","article-title":"Seminar Autism spectrum disorder","volume":"392","author":"Lord","year":"2018","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1287","DOI":"10.1016\/j.cell.2019.07.037","article-title":"Getting to the Cores of Autism","volume":"178","author":"Iakoucheva","year":"2019","journal-title":"Cell"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1177\/1073858420921378","article-title":"Autism Spectrum Disorders: Multiple Routes to, and Multiple Consequences of, Abnormal Synaptic Function and Connectivity","volume":"27","author":"Carroll","year":"2021","journal-title":"Neuroscientist"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1097\/GIM.0b013e3181ff67ba","article-title":"Genetics in Medicine Autism spectrum disorders\u2014A genetics review","volume":"13","author":"Miles","year":"2011","journal-title":"Genet. 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