PMID- 38138941
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231225
IS  - 2075-4426 (Print)
IS  - 2075-4426 (Electronic)
IS  - 2075-4426 (Linking)
VI  - 13
IP  - 12
DP  - 2023 Dec 15
TI  - From Data to Insights: Machine Learning Empowers Prognostic Biomarker Prediction 
      in Autism.
LID - 10.3390/jpm13121713 [doi]
LID - 1713
AB  - 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.
FAU - Mehmetbeyoglu, Ecmel
AU  - Mehmetbeyoglu E
AUID- ORCID: 0000-0002-4597-6811
AD  - Department of Cancer and Genetics, Cardiff University, Cardiff CF14 4XN, UK.
AD  - Betul-Ziya Eren Genome and Stem Cell Center, Erciyes University, Kayseri 38280, 
      Turkey.
FAU - Duman, Abdulkerim
AU  - Duman A
AUID- ORCID: 0000-0003-2747-1294
AD  - School of Engineering, Cardiff University, Cardiff CF24 3AA, UK.
FAU - Taheri, Serpil
AU  - Taheri S
AD  - Betul-Ziya Eren Genome and Stem Cell Center, Erciyes University, Kayseri 38280, 
      Turkey.
AD  - Department of Medical Biology, Erciyes University, Kayseri 38280, Turkey.
FAU - Ozkul, Yusuf
AU  - Ozkul Y
AD  - Betul-Ziya Eren Genome and Stem Cell Center, Erciyes University, Kayseri 38280, 
      Turkey.
AD  - Department of Medical Genetics, Erciyes University, Kayseri 38280, Turkey.
FAU - Rassoulzadegan, Minoo
AU  - Rassoulzadegan M
AD  - Betul-Ziya Eren Genome and Stem Cell Center, Erciyes University, Kayseri 38280, 
      Turkey.
AD  - Inserm-CNRS, Universite Cote d'Azur, 06107 Nice, France.
LA  - eng
PT  - Journal Article
DEP - 20231215
PL  - Switzerland
TA  - J Pers Med
JT  - Journal of personalized medicine
JID - 101602269
PMC - PMC10744627
OTO - NOTNLM
OT  - autism
OT  - machine learning
OT  - miRNAs
COIS- The authors declare no conflict of interest.
EDAT- 2023/12/23 12:44
MHDA- 2023/12/23 12:45
PMCR- 2023/12/15
CRDT- 2023/12/23 01:19
PHST- 2023/11/10 00:00 [received]
PHST- 2023/12/10 00:00 [revised]
PHST- 2023/12/12 00:00 [accepted]
PHST- 2023/12/23 12:45 [medline]
PHST- 2023/12/23 12:44 [pubmed]
PHST- 2023/12/23 01:19 [entrez]
PHST- 2023/12/15 00:00 [pmc-release]
AID - jpm13121713 [pii]
AID - jpm-13-01713 [pii]
AID - 10.3390/jpm13121713 [doi]
PST - epublish
SO  - J Pers Med. 2023 Dec 15;13(12):1713. doi: 10.3390/jpm13121713.