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一个神经网络模型,用于快速预测分析物聚焦在同位电泳

Amit Jangra^1,2, Shaurya Shriyam^1,3, Juan G Santiago⁴

¹Department of Mechanical Engineering, Indian Institute of Technology Delhi, New Delhi, India.

Electrophoresis

|December 7, 2023

概括

一个新的神经网络 (NN) 模型准确地预测了分析物是否集中在同位电泳 (ITP) 缓冲系统中. 这个工具,IONN,为研究人员快速选ITP化学物质,达到超过97.7%的预测准确度.

科学领域:

分析化学分析化学
计算化学的计算化学

背景情况:

隔离电泳 (ITP) 是一种强大的分离技术.
为ITP选择最佳的缓冲系统可能是耗时的.
预测模型可以加速ITP方法的开发.

研究的目的:

开发和演示一个神经网络 (NN) 模型,用于预测ITP中的分析焦点.
为评估ITP缓冲系统提供快速准确的工具.
创建一个用户友好的应用程序来选ITP条件.

主要方法:

训练并测试了一个NN模型,使用超过20,000个阴离子和阴离子ITP模拟.
输入参数包括离子流动性,酸解离常数和度比.
对阴离子和阴离子ITP的实验数据进行了验证的预测.

主要成果:

对于ITP中的分析聚焦,NN模型实现了超过97.7%的预测准确度.
通过实验结果验证其预测,证明了NN模型的成功应用.
开发了一个免费的基于Web的应用程序,名为IONN (神经网络上的异分离).

结论:

开发的NN模型为ITP缓冲系统选择提供了快速而准确的预测.
IONN应用程序可以快速选潜在的ITP电解质系统.
这种方法显著加快了基于ITP的分离的优化过程.

关键词:

是一个 isotachophoresisis.机器学习是机器学习.神经网络的神经网络的神经网络

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