Application of Artificial Neural Network to Nucleic Acid Analysis: Accurate Discrimination for Untypical Real-Time Fluorescence Curves With High Specificity and Sensitivity
Application of Artificial Neural Network to Nucleic Acid Analysis: Accurate Discrimination for Untypical Real-Time Fluorescence Curves With High Specificity and Sensitivity
复制标题
人工神经网络在核酸分析中的应用：准确判别不典型的实时荧光曲线，具有高特异性和灵敏度
DOI：
10.1115/1.4056150
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Xianbo Qiu
中科院分区：
文献类型：
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作者：
Guijun Miao;Xiaodan Jiang;Yunping Tu;Lulu Zhang;Duli Yu;Shizhi Qian;Xianbo Qiu
抽象。作为聚合酶链反应（PCR）的一个分支，对流PCR（CPCR）能够实现基于伪等温加热的自由热对流的高效热循环，这可能有利于床旁（POC）核酸分析。类似于传统的PCR或等温扩增，由于一些问题，例如，由于试剂、引物设计、反应器、反应动力学、扩增状态、温度和加热条件等原因，在CPCR检测中，有时会出现不典型的阳性或阴性实时荧光曲线。特别是当不典型的低阳性和阴性检测之间的部分特征混合在一起时，使用传统的循环阈值（Ct值）方法很难区分它们。为了解决CPCR、传统PCR或等温扩增中可能出现的这一问题，作为示例，开发了一种人工神经网络（ANN）建模的人工智能（AI）分类方法，而不是使用复杂的数学建模和信号处理策略，以提高核酸检测的准确性。已经证明，即使使用简单的ANN模型，检测特异性和灵敏度也可以显著提高。可以估计，基于AI建模的开发方法可以用于解决类似的问题与PCR或等温扩增方法。
Abstract. As a division of polymerase chain reaction (PCR), convective PCR (CPCR) is able to achieve highly efficient thermal cycling based on free thermal convection with pseudo-isothermal heating, which could be beneficial to point-of-care (POC) nucleic acid analysis. Similar to traditional PCR or isothermal amplification, due to a couple of issues, e.g., reagent, primer design, reactor, reaction dynamics, amplification status, temperature and heating condition, and other reasons, in some cases of CPCR tests, untypical real-time fluorescence curves with positive or negative tests will show up. Especially, when parts of the characteristics between untypical low-positive and negative tests are mixed together, it is difficult to discriminate between them using traditional cycle threshold (Ct) value method. To handle this issue which may occur in CPCR, traditional PCR or isothermal amplification, as an example, instead of using complicated mathematical modeling and signal processing strategy, an artificial intelligence (AI) classification method with artificial neural network (ANN) modeling is developed to improve the accuracy of nucleic acid detection. It has been proven that both the detection specificity and sensitivity can be significantly improved even with a simple ANN model. It can be estimated that the developed method based on AI modeling can be adopted to solve similar problem with PCR or isothermal amplification methods.