现任皇帝的明智角色和未来的有远见的努力:在个性化和精确医学中推进纵向微生物组元组学数据的动态方法
Vera-Khlara S Oh1, Robert W Li2
1Big Biomedical Data Integration and Statistical Analysis (DIANA) Research Center, Department of Data Science, College of Natural Sciences, Jeju National University, Jeju City, Jeju Do, 63243, South Korea.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|November 13, 2024
概括
动态微生物组多omics数据集成是个性化医学的关键. 先进的方法为疾病病因提供了洞察力,但为了可靠的分析和未来的进展,需要标准化的协议.
科学领域:
- 微生物组研究的研究.
- 个性化医疗是个性化的医疗.
- 多主题数据分析数据分析.
背景情况:
- 疾病病因是复杂的,需要纵向生物标志物.
- 来自先进测序的微生物组数据提供了对健康影响的见解.
- 整合动态微生物组数据对于精准医学至关重要.
研究的目的:
- 探索用于整合纵向微生物组多组数据的动态方法.
- 综合研究,提出最佳实践,并突出创新技术.
- 检查先进的动态方法,AI和统一的分析框架.
主要方法:
- 审查目前微生物多组组数据集成的动态方法.
- 对人工智能和深度学习工具进行批判性检查.
- 分析跨微生物,代谢物和基因的数据聚合.
主要成果:
- 动态方法提供了对宿主微生物相互作用的见解.
- 挑战包括缺乏验证和数据资源的黄金标准.
- 工作流的相互依赖性显著影响研究结果.
结论:
- 先进的动态方法对于个性化和精准医学至关重要.
- 提供最佳实践和路线图,以应对当前的挑战.
- 在研究间分析和可靠的协议上达成共识对于未来的进展至关重要.
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