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使用双重质谱数据预测遗传代谢障碍,借助人工神经网络

Pembe Soylu Üstkoyuncu¹, Nurettin Üstkoyuncu²

¹Division of Pediatric Nutrition and Metabolism, Department of Pediatrics, Faculty of Medicine, Health Sciences University, Kayseri, Turkiye.

Turkish journal of medical sciences

|September 19, 2024

概括

人工神经网络准确地预测代谢的先天性错误,使用双重质谱数据. 这项技术为儿童的早期诊断提供了有前途的进展,改善了患者的治疗结果.

科学领域:

生物化学生物化学
计算生物学计算生物学
儿科医学儿科医学

背景情况:

双重质谱 (TMS) 对于诊断代谢障碍至关重要,如氨基酸代谢障碍,有机酸性病和脂肪酸氧化障碍.
通过TMS,可以快速准确地诊断代谢天生的错误 (IEM).

研究的目的:

为了预测儿童代谢的先天性错误,使用在TMS数据上训练的人工神经网络 (ANN).
评估不同ANN模型的IEM预测性能.

主要方法:

经过培训和测试,ANN使用2938名患者的TMS数据集进行了培训和测试,其中有47个和13个参数.
开发和比较各种ANN模型,包括具有一个,两个和三个隐藏层的模型,并利用修正线性单元激活功能.

主要成果:

该ANN模型的准确性很高,A3模型达到99.3%,B2模型达到99.2%.
模型B2对确定的遗传代谢障碍显示了80%的灵敏度.
曲线下的面积值为A3模型的0.87和B2模型的0.90.

结论:

开发的ANN可以准确地预测代谢的先天性错误.
对IEM的预测技术的进步对于早期检测和改善患者护理至关重要.

关键词:

人工智能的人工智能是人工智能.人工神经网络的人工神经网络孩子们的孩子们的孩子们的孩子们.代谢过程中的先天性错误.预测预测预测预测

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