抑郁症的新型综合框架:结合网络药理学,人工智能和多态学,专注于微生物群-肠-大脑轴
Lele Zhang1, Kai Chen2, Shun Li1
1School of Biological and Food Engineering, Fuyang Normal University, 100 Qinghe West Road, Fuyang 236037, China.
Current issues in molecular biology
|January 30, 2026
概括
本综述提出了一个使用人工智能 (AI) 和多omics数据的综合框架,以了解主要抑郁症 (MDD). 这种方法旨在揭示疾病机制并确定MDD的个性化治疗方法.
科学领域:
- 计算生物学 计算生物学
- 系统生物学 系统生物学
- 精神病学是一个精神病学.
背景情况:
- 大型抑郁症 (MDD) 由于其复杂而异质的病理生理学,对全球健康构成重大挑战.
- 传统的单一治疗策略不足以解决MDD的多方面的性质.
- 系统层面的理解对于推进MDD管理至关重要.
研究的目的:
- 为MDD研究提供一个整合性框架,将网络药理学,人工智能 (AI) 和多omics技术结合起来.
- 展示AI驱动的多omics数据集成如何可以构建MDD的预测网络模型.
- 确定基本疾病机制,患者生物型和MDD的新疗法目标.
主要方法:
- 利用网络药理学来分析复杂的生物相互作用.
- 应用人工智能 (AI) 来整合和分析多omics数据 (基因组学,蛋白质组学,代谢组学).
- 检查微生物群-肠-大脑 (MGB) 轴作为MDD病理生理学的案例研究.
主要成果:
- 多omics数据的AI驱动集成使得MDD的预测网络模型的构建成为可能.
- 该框架有助于识别疾病机制,临床相关的生物型和潜在的治疗点.
- 微生物群-肠-大脑轴被强调为一个关键区域,涉及MDD中神经免疫,代谢和神经可塑性改变.
结论:
- 一个集成的AI和多omics框架为MDD提供了基于机制的个性化疗法的途径.
- 翻译应用包括人工智能辅助的生物标志物发现,药物重新定位和精确精神病学数字双胞胎模型.
- 未来的成功取决于整合纵向的多omics数据,数字表型和可解释的AI (XAI).
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