人工神经网络建模和优化电化学生物传感器用于基于miR-155的血乳腺癌检测
Aydin Imani1, Soleiman Hosseinpour2, Mostafa Azimzadeh3
1Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran. Imani.aydin@yahoo.com.
Scientific reports
|February 9, 2026
概括
这项研究引入了使用人工神经网络 (ANN) 和遗传算法 (GA) 的数据驱动方法,以优化微RNA-155 (miR-155) 检测的生物传感器制造,加速诊断工具开发.
科学领域:
- 生物标志物检测检测 生物标志物检测
- 生物传感器技术技术
- 计算机建模 计算建模
背景情况:
- 微RNA-155 (miR-155) 是癌症和炎症疾病中的一个关键生物标志物.
- 目前的生物传感器开发依赖于低效的试错优化.
- 对miR-155的敏感和可靠的检测方法对于诊断至关重要.
研究的目的:
- 开发一个数据驱动的框架,以优化生物传感器制造参数.
- 使用人工智能建模制造参数和生物传感器输出之间的非线性关系.
- 为加速开发具有成本效益,高性能生物传感器用于miR-155检测.
主要方法:
- 使用人工神经网络 (ANN) 和自适应神经模糊推理系统 (ANFIS) 进行建模.
- 使用遗传算法 (GA) 来优化制造参数.
- 在生物传感器输出电流和六个制造参数的数据集上训练模型.
主要成果:
- 该ANN方法表现出优于ANFIS的性能,达到0.9845.5的R2值.
- 确定了最大化生物传感器输出电流 (223 nA) 的最佳制造参数.
- ANN-GA框架显著减少了实验代和加速优化.
结论:
- 一个ANN-GA框架为快速优化生物传感器参数提供了一个有效的策略.
- 这种数据驱动的方法可以降低材料消耗和开发成本.
- 加快的发展支持生物传感器的临床转化,用于早期检测miR-155.
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