通过机器学习分析Omics数据来识别抑郁症:范围审查
Brittany Taylor1, Mollie Hobensack2, Stephanie Niño de Rivera1
1School of Nursing, Columbia University, New York, NY, United States.
JMIR nursing
|July 19, 2024
概括
机器学习和omics数据分析显示了客观抑郁症诊断的前景. 这些方法可以识别生物标记,帮助临床医生,特别是护士,更有效地诊断和治疗抑郁症.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算精神病学是一种计算精神病学.
- 遗传学 遗传学 是一个
背景情况:
- 抑郁症影响全球超过3亿人,诊断依赖于主观症状.
- 心理健康提供者的短缺需要创新的诊断方法.
- 奥米克斯方法 (基因组学,转录组学,表观组学,微生物组学) 提供了对抑郁症的客观生物学见解.
研究的目的:
- 进行对机器学习 (ML) 应用在omics数据分析中用于抑郁症识别的范围审查.
- 探索客观的,数据驱动的洞察力,以改善抑郁症诊断.
主要方法:
- 根据PRISMA-ScR指南进行范围审查.
- 在3个数据库中搜索相关文献.
- 独立选和对15项选定的研究进行批判性评价.
主要成果:
- 确定了15篇相关论文,使用各种奥米克 (基因组学,转录组学,表观组学,多组学,微生物组学).
- 常见的ML方法包括随机森林,支持矢量机,k-最近邻居和人工神经网络.
- 欧米克斯方法在识别与抑郁症相关的变体方面表现相似.
结论:
- 使用ML的Omics数据分析显示,在识别与抑郁症相关的变异方面,性能相似.
- 所有评估的ML方法在分析抑郁症的OMIC数据方面都表现良好.
- 这些发现支持将omics和ML整合到客观抑郁症评估和护士及时临床干预中.
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