在人类疾病中应用多奥米克分析
Chongyang Chen1,2, Jing Wang3, Donghui Pan1
1Key Laboratory of Nuclear Medicine Ministry of Health Jiangsu Key Laboratory of Molecular Nuclear Medicine Jiangsu Institute of Nuclear Medicine Wuxi China.
MedComm
|August 3, 2023
概括
本综述提供了一个全面的指南,多omics,整合基因组学,转录组学和蛋白质组学疾病研究. 它详细介绍了先进的机器学习方法和在精准医学中的应用,包括癌症和神经退行性疾病.
科学领域:
- 生物医学研究生物医学研究
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 综合基因组学,转录组学,蛋白组学和代谢组学的多组学对于推进人类疾病研究至关重要.
- 现有的评论往往侧重于技术发展或特定疾病,缺乏对多种疾病的整体看法.
研究的目的:
- 提供系统和全面的介绍,多omics技术及其应用.
- 详细介绍综合分析方法,特别是机器学习和深度学习方法.
- 引导研究人员在新兴领域的多omics医学研究.
主要方法:
- 探索多主题技术类别和实验设计方面的考虑.
- 专注于综合分析方法,包括机器学习和深度学习算法.
- 审查癌症,神经退行性疾病,衰老和药物发现中的应用.
主要成果:
- 概述了各种多学科技术类别及其整合策略.
- 强调机器学习和深度学习在分析复杂的多学科数据方面的关键作用.
- 展示了各种医疗领域的应用,并确定了相关的开源工具和数据库.
结论:
- 多种OMIC的整合,特别是单细胞和空间的多种OMIC,对于未来的疾病研究和精确医学至关重要.
- 解决当前的挑战和未来的方向在多omics数据分析和应用.
- 为进入多学科领域的研究人员提供了必要的指导.
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