探索精神分裂症,机器学习和基因组学的交叉点:范围审查
Alexandre Hudon1,2,3, Mélissa Beaudoin4,5, Kingsada Phraxayavong6
1Department of psychiatry and addictology, Faculty of Medicine, Université de Montréal, Montréal, QC, Canada.
JMIR bioinformatics and biotechnology
|November 15, 2024
概括
机器学习与基因组数据相结合,显示出理解精神分裂症的前景. 目前的应用是有限的,但包括预测诊断和患者的结果,突出需要进一步的研究.
科学领域:
- 精神病学是一个精神病学.
- 基因组学就是基因组学.
- 计算神经科学是一种神经科学.
背景情况:
- 机器学习 (ML) 与基因组数据的整合在精神病学中越来越多,用于治疗诸如精神分裂症之类的复杂疾病.
- 先进的ML技术为揭示精神分裂症的多方面的方面提供了潜力.
- 对ML和基因组数据应用的审查可以提高对当前研究和未来方向的理解.
研究的目的:
- 在精神分裂症研究中对机器学习算法与基因组数据的利用进行系统范围审查.
主要方法:
- 使用MEDLINE,科学网络,PsycNet (PsycINFO) 和谷歌学者 (2013-2024) 进行了系统的范围审查.
- 评估了专注于精神分裂症,基因组数据和机器学习的交叉研究.
主要成果:
- 从最初的2437篇文章中,对21项研究进行了彻底评估.
- 支持向量机是最常用的ML算法.
- 基因组数据被用于预测精神分裂症,识别特征,药物发现,分类和预测生活质量.
结论:
- 确定了几项高质量的研究,但在精神分裂症中使用基因组数据的ML应用仍然有限.
- 未来的研究对于评估模型可移植性和探索临床应用至关重要.
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