米特朗:代谢动态感知科尔莫戈罗夫-阿诺德生物年龄估计网络
概括
我们开发了METRON,一个深度学习框架,使用类固醇代谢学来预测生物年龄. METRON准确地估计了生物年龄,并确定了衰老的关键代谢驱动因素,优于其他方法.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 老年学是指老年学的学科.
背景情况:
- 生物年龄,一个生理测量,对于评估健康风险和衰老至关重要.
- 类固醇代谢学提供了对衰老的洞察力,但由于复杂的代谢相互作用而面临挑战.
- 现有的模型很难捕捉代谢网络中的非线性.
研究的目的:
- 介绍METRON,这是一个深度学习框架,用于从类固醇代谢学数据中预测生物年龄.
- 开发一种能够捕捉复杂代谢物相互作用和依赖性的模型.
- 提高生物年龄预测模型的可解释性.
主要方法:
- 开发了METRON,这是一个使用代谢物相互作用感知模块 (MIPM) 的深度学习框架.
- 集成了一个集团-理性的科尔摩戈罗夫-阿诺德网络,以捕捉复杂的代谢依赖.
- 验证了METRON的性能与现有的机器学习和深度学习方法相比.
主要成果:
- 与其他方法相比,METRON在预测生物年龄方面表现出了有前途的表现.
- 该框架确定了脱氨 (DHEA) 作为已知的衰老标志物.
- 鉴定出17-基孕 (17-OH-P4) 是与下丘脑-垂体-上腺轴相关的关键特征.
结论:
- 使用类固醇代谢学,METRON有效地预测生物年龄.
- 该框架提供了可解释性,揭示了衰老的关键代谢驱动因素.
- 梅特龙推进了对新陈代谢对衰老过程的贡献的理解.
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