用机器学习和卷积神经网络使用等离子体蛋白质组进行阿尔茨海默病早期查和分阶段检测
Hengyu Qin1, Xiumin Shi1, Yibo Zhu1
1School of Information and Electronics, Beijing Institute of Technology, Beijing, China.
The European journal of neuroscience
|May 20, 2024
概括
这项研究引入了一种使用血蛋白学和机器学习诊断阿尔茨海默病 (AD) 的新型非侵入性方法. 该方法确定可靠的生物标志物用于早期AD检测和分期,改进了传统方法.
科学领域:
- 神经退行性疾病的神经退行性疾病
- 发现生物标志物的发现.
- 计算生物学是一种计算生物学.
背景情况:
- 阿尔茨海默病 (AD) 是一种进展性神经退行性疾病,其特点是认知能力下降.
- 目前的诊断方法依赖于粉样蛋白生物标志物,通常需要大量沉积,阻碍早期检测.
- 需要具有成本效益的,非侵入性的方法来早期和准确的AD诊断和分期.
研究的目的:
- 开发一种新的,非侵入性的阿尔茨海默病 (AD) 诊断方法,使用血蛋白学.
- 为了确定AD进展的血蛋白生物标志物的纵向面板.
- 创建用于早期AD检测和分期的机器学习模型.
主要方法:
- 血蛋白质组学被用来分析蛋白质配置文件.
- 组合学习模型 (CatBoost) 用于生物标志物面板的开发.
- 开发了一个神经网络算法,使用血蛋白检测AD阶段.
主要成果:
- 确定了一个可靠的AD生物标志物的纵向面板.
- CatBoost模型在区分轻度认知障碍和认知正常个体时,在操作曲线下的面积达到≥0.90.
- 神经网络模型在检测阿尔茨海默氏症的三个阶段中显示了0.83的准确性,超过了传统的机器学习模型.
结论:
- 血蛋白质组学与机器学习相结合,为阿尔茨海默病提供了一个有前途的非侵入性诊断策略.
- 已识别的生物标志物面板和开发的模型有助于早期发现和分期AD.
- 这种方法有可能显著改善AD查和管理.
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