%0 Journal Article
%@ 2563-3570
%I JMIR Publications
%V 5
%N 
%P e62752
%T Exploring the Intersection of Schizophrenia, Machine Learning, and Genomics: Scoping Review
%A Hudon,Alexandre
%A Beaudoin,Mélissa
%A Phraxayavong,Kingsada
%A Potvin,Stéphane
%A Dumais,Alexandre
%+ Department of psychiatry and addictology, Université de Montréal, 2900 Edouard Montpetit Blvd, Montréal, QC, H3T 1J4, Canada, 1 514 648 8461, alexandre.dumais@umontreal.ca
%K schizophrenia
%K genomic data
%K machine learning
%K artificial intelligence
%K classification techniques
%K psychiatry
%K mental health
%K genomics
%K predictions
%K ML
%K psychiatric
%K synthesis
%K review methods
%K searches
%K scoping review
%K prediction models
%D 2024
%7 15.11.2024
%9 Review
%J JMIR Bioinform Biotech
%G English
%X Background: An increasing body of literature highlights the integration of machine learning with genomic data in psychiatry, particularly for complex mental health disorders such as schizophrenia. These advanced techniques offer promising potential for uncovering various facets of these disorders. A comprehensive review of the current applications of machine learning in conjunction with genomic data within this context can significantly enhance our understanding of the current state of research and its future directions. Objective: This study aims to conduct a systematic scoping review of the use of machine learning algorithms with genomic data in the field of schizophrenia. Methods: To conduct a systematic scoping review, a search was performed in the electronic databases MEDLINE, Web of Science, PsycNet (PsycINFO), and Google Scholar from 2013 to 2024. Studies at the intersection of schizophrenia, genomic data, and machine learning were evaluated. Results: The literature search identified 2437 eligible articles after removing duplicates. Following abstract screening, 143 full-text articles were assessed, and 121 were subsequently excluded. Therefore, 21 studies were thoroughly assessed. Various machine learning algorithms were used in the identified studies, with support vector machines being the most common. The studies notably used genomic data to predict schizophrenia, identify schizophrenia features, discover drugs, classify schizophrenia amongst other mental health disorders, and predict the quality of life of patients. Conclusions: Several high-quality studies were identified. Yet, the application of machine learning with genomic data in the context of schizophrenia remains limited. Future research is essential to further evaluate the portability of these models and to explore their potential clinical applications. 
%M 39546776
%R 10.2196/62752
%U https://bioinform.jmir.org/2024/1/e62752
%U https://doi.org/10.2196/62752
%U http://www.ncbi.nlm.nih.gov/pubmed/39546776