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使用机器学习技术和遗传环境特征对阿尔茨海默病和正常受试者进行分类

Yu-Hua Huang¹, Yi-Chun Chen¹, Wei-Min Ho¹

¹Department of Neurology, Chang Gung Memorial Hospital Linkou Medical Center and College of Medicine, Chang-Gung University, Taoyuan, Taiwan.

Journal of the Formosan Medical Association = Taiwan yi zhi

|December 3, 2023

概括

人工神经网络 (ANN) 使用遗传和环境因素准确识别了阿尔茨海默病 (AD). 关键预测因素包括教育,心理评估和特定基因变异,如CASS4 rs7274581.

科学领域:

计算生物学是一种计算生物学.
遗传学是一个遗传学.
神经科学是一个神经科学.

背景情况:

阿尔茨海默病 (AD) 是一种复杂的神经退行性疾病,受遗传和环境因素的影响.
人工神经网络 (ANN) 对于AD的诊断准确性,考虑到这些多因素的影响,仍然未得到充分研究.

研究的目的:

通过整合常见的遗传和环境风险因素,评估ANN在识别阿尔茨海默病 (AD) 中的准确性.
将ANN的性能与其他机器学习模型 (如随机森林 (RF) 和支持矢量机器 (SVM)) 进行比较.

主要方法:

分析了184名可能的AD患者和3773名健康的老年人 (≥65岁) 的队列.
使用51个与AD相关的单核酸多态 (SNP) 和8个环境因素作为输入特征来开发多层ANN.
用传统的统计方法验证模型性能,并与RF和SVM算法进行比较.

主要成果:

在对AD的分类方面,ANN模型实现了高精度 (0.98),灵敏度 (0.95) 和特异性 (0.96).
除了年龄和遗传数据,表现仍然很强 (精度:0.97,灵敏度:0.94,特异性:0.96).
功能重要性分析强调精神评估,教育年限和特定的SNP (CASS4 rs7274581,PICALM rs3851179,TOMM40 rs2075650) 对于AD预测至关重要.

结论:

在阿尔茨海默病分类方面,ANN模型表现出显著的准确性,敏感性和特异性.
特定的遗传变异,包括CASS4 rs7274581,PICALM rs3851179和TOMM40 rs2075650,是阿尔茨海默病的重要预测因素.
该ANN模型的性能与RF和SVM相美,证实了它在管理AD的复杂性方面的实用性.

关键词:

阿尔茨海默病的疾病阿尔茨海默病的疾病.人工神经网络 (ANN) 是一个人工神经网络.机器学习机器学习单个核酸的多态性.整个基因组的基因型鉴定.整个基因组测序的测序

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