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通过深度学习和梯度提升识别的小型免疫钟

Alena Kalyakulina^1,2,3, Igor Yusipov^1,2,3, Elena Kondakova^3,4

¹Research Center for Trusted Artificial Intelligence, Ivannikov Institute for System Programming of the Russian Academy of Sciences, Moscow, Russia.

Frontiers in immunology

|September 11, 2023

概括

我们开发了SImAge,这是一种新的免疫年龄表,仅使用10个生物标志物. 这个模型准确地预测了时间表年龄,为衰老研究提供了更简单,更有效的工具.

科学领域:

免疫学免疫学免疫学
计算生物学计算生物学
人工智能的人工智能

背景情况:

衰老与炎症增加和免疫系统变化有关.
这些与年龄相关的免疫变化可以驱动疾病和全身炎症.

研究的目的:

开发一种简化模型,使用最小的生物标志物集来预测"免疫年龄".
利用先进的机器学习技术从免疫学数据中准确预测年龄.

主要方法:

利用弹性网,渐变增强的决策树和深度神经网络 (DANet,SAINT,FT-Transformer,TabNet) 来从细胞因子数据中建模时间年龄.
应用SHAP值来减少维度,以确定关键的与年龄相关的免疫学参数.
使用前10个已识别的免疫学参数构建了SImAge时钟.

主要成果:

在SImAge模型中,FT-Transformer深度神经网络实现了最佳性能.
在一个独立的测试数据集上,获得了6.94年的平均绝对误差和0.939的皮尔森相关系数 (ρ).
可解释的人工智能方法提供了个体参与者级别的模型解释.

结论:

成功开发了SImAge模型,这是一个基于10个参数的免疫年龄钟,FT-Transformer是最佳的深度学习架构.
与现有的免疫学资料研究相比,SImAge在较少的输入特征上显示出具有竞争力的准确性.
深度神经网络在免疫特征分析中表现优于梯度增强树的性能,并被推用于未来的研究.

关键词:

老化生物标志物的生物标志物深度神经网络是一个神经网络.可解释的人工智能免疫学概况免疫学概况基于树的模型模型.

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