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从奥米克到人工智能绘制2型糖尿病的病原性路径

Siobhán O'Sullivan¹, Lu Qi², Pierre Zalloua^3,4

¹Department of Biological Sciences, College of Medicine and Health Sciences, Khalifa University, Abu Dhabi, UAE.

|July 17, 2025

概括

了解2型糖尿病 (T2D) 需要探索其生化途径和器官间的沟通. 这次审查整合了多omics和AI,以揭示针对T2D治疗的分子驱动因素.

科学领域:

生物化学生物化学
系统生物学系统生物学
代谢性疾病研究研究

背景情况:

2型糖尿病 (T2D) 的病理生理学是复杂的,涉及复杂的生化途径和器官间的通信.
了解分子功能障碍是将代谢障碍与临床T2D表型联系起来的关键.
当前的研究需要先进的方法来整合多样化的生物数据,以获得整体的观点.

研究的目的:

综合目前关于T2D分子结构的证据.
要突出关键的生化途径和驱动T2D的细胞机制.
探索多omics和AI的整合,以推进T2D研究和治疗.

主要方法:

综述多omics数据 (基因组学,转录组学,蛋白质组学,代谢组学,微生物组学).
应用单细胞技术来识别特定于细胞类型的T2D驱动因素.
利用人工智能驱动的分析和机器学习来实现高维数据集集成.

主要成果:

确定了与T2D相关的关键分子通路 (例如PI3K-Akt,AMPK,mTOR,JNK,Sirtuins).
突出了肠道微生物群在调节宿主新陈代谢和炎症中的作用.
在T2D中展示了AI在发现分子签名和监管网络方面的力量.

结论:

一种以途径为中心的系统生物学方法为T2D提供了机械的洞察力.
整合多omics和AI对于患者分层和精确的糖尿病护理至关重要.
这种方法弥合了分子研究和T2D干预的临床应用之间的差距.

关键词:

人工智能的人工智能是人工智能.临床翻译临床翻译数字双胞胎是一个数字双胞胎.多个学科的整合.精准医学是一门精准医学.系统生物学系统生物学 2 型糖尿病 2 型糖尿病

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