Exploring the Intersection of Schizophrenia, Machine Learning, and Genomics: Scoping Review
BackgroundAn 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...
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| Format: | Article |
| Language: | English |
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JMIR Publications
2024-11-01
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| Series: | JMIR Bioinformatics and Biotechnology |
| Online Access: | https://bioinform.jmir.org/2024/1/e62752 |
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| author | Alexandre Hudon Mélissa Beaudoin Kingsada Phraxayavong Stéphane Potvin Alexandre Dumais |
| author_facet | Alexandre Hudon Mélissa Beaudoin Kingsada Phraxayavong Stéphane Potvin Alexandre Dumais |
| author_sort | Alexandre Hudon |
| collection | DOAJ |
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BackgroundAn 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.
ObjectiveThis study aims to conduct a systematic scoping review of the use of machine learning algorithms with genomic data in the field of schizophrenia.
MethodsTo 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.
ResultsThe 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.
ConclusionsSeveral 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. |
| format | Article |
| id | doaj-art-a23f9d5e72384bd2a3219b39f1c81902 |
| institution | Kabale University |
| issn | 2563-3570 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Bioinformatics and Biotechnology |
| spelling | doaj-art-a23f9d5e72384bd2a3219b39f1c819022024-11-15T19:30:50ZengJMIR PublicationsJMIR Bioinformatics and Biotechnology2563-35702024-11-015e6275210.2196/62752Exploring the Intersection of Schizophrenia, Machine Learning, and Genomics: Scoping ReviewAlexandre Hudonhttps://orcid.org/0000-0002-4868-0928Mélissa Beaudoinhttps://orcid.org/0000-0002-0169-8055Kingsada Phraxayavonghttps://orcid.org/0000-0003-3113-9104Stéphane Potvinhttps://orcid.org/0000-0003-1624-378XAlexandre Dumaishttps://orcid.org/0000-0002-4480-0064 BackgroundAn 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. ObjectiveThis study aims to conduct a systematic scoping review of the use of machine learning algorithms with genomic data in the field of schizophrenia. MethodsTo 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. ResultsThe 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. ConclusionsSeveral 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.https://bioinform.jmir.org/2024/1/e62752 |
| spellingShingle | Alexandre Hudon Mélissa Beaudoin Kingsada Phraxayavong Stéphane Potvin Alexandre Dumais Exploring the Intersection of Schizophrenia, Machine Learning, and Genomics: Scoping Review JMIR Bioinformatics and Biotechnology |
| title | Exploring the Intersection of Schizophrenia, Machine Learning, and Genomics: Scoping Review |
| title_full | Exploring the Intersection of Schizophrenia, Machine Learning, and Genomics: Scoping Review |
| title_fullStr | Exploring the Intersection of Schizophrenia, Machine Learning, and Genomics: Scoping Review |
| title_full_unstemmed | Exploring the Intersection of Schizophrenia, Machine Learning, and Genomics: Scoping Review |
| title_short | Exploring the Intersection of Schizophrenia, Machine Learning, and Genomics: Scoping Review |
| title_sort | exploring the intersection of schizophrenia machine learning and genomics scoping review |
| url | https://bioinform.jmir.org/2024/1/e62752 |
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