在IBD中Omics和Multi-Omics:没有整合,没有突破
1Department of Inflammation & Immunity, Lerner Research Institute, Cleveland, OH 44195, USA.
多领域的研究,利用大数据和人工智能,正在改变炎症性肠病 (IBD) 的研究. 虽然对于临床工具来说很有希望,但目前的IBD多组数据主要使用了回顾性分析,需要前性数据集来获得更大的好处.
科学领域:
- 生物医学研究的研究.
- 计算生物学是一种计算生物学.
- 基因组学和系统生物学
背景情况:
- 像炎症性肠病 (IBD) 这样的复杂疾病需要超越传统实验的先进研究方法.
- 复杂的技术 (测序,质谱) 和人工智能正在使疾病研究中的"大数据"方法成为可能.
- 目前正在向in silico方法转变,用于调查未知原因的疾病.
研究的目的:
- 提供关于炎症性肠病 (IBD) 中omics和multi-omics的综合评估.
- 讨论各种OMIC与IBD病原学的相关性以及多OMIC数据的潜力.
- 探索将多学科见解转化为IBD管理的临床工具.
主要方法:
- 综述IBD中omics和multi-omics的最新知识.
- 讨论omics与IBD病原发生的相关性.
- 对当前IBD多学科研究局限性的批判性分析和优化建议.
主要成果:
- 多omics大数据有潜力用于生物标志物识别,预测疾病过程,并使IBD的精准医学成为可能.
- 目前的IBD多学科研究主要基于回顾性,横截面数据.
- 一个重要的限制是依赖描述性关联,而不是强大的纵向前性数据集.
结论:
- 多学科的方法对于促进IBD的理解和临床管理至关重要.
- 需要通过前性研究优化IBD多组数据,以获得更大的临床和治疗益处.
- 预计在未来,多组体分析将被整合到IBD常规管理中.
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