Rapid detection of hepatitis B virus DNA level based on interval-point data fusion of infrared spectra
Rapid detection of hepatitis B virus DNA level based on interval-point data fusion of infrared spectra
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DOI：
10.1002/jbio.202200251
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发表时间：
2022-10-11
影响因子：
2.8
通讯作者：
Li, Yuanpeng
中科院分区：
文献类型：
--
作者：
Chen, Jiaze;Ma, Jinfang;Li, Yuanpeng
乙型肝炎是一种由乙型肝炎病毒（HBV）引起的传染病。近年来，HBV-DNA水平因其详细信息比其他血清学指标更受临床关注。不幸的是，常见的临床检测HBV-DNA水平的方法因其耗时而受到限制。本研究将红外光谱与机器学习相结合，探讨近红外（NIR）和中红外（MIR）光谱快速检测HBV-DNA水平的可行性。基于偏最小二乘判别分析（PLS-DA）建模方法，分别构建了最优NIR和MIR模型以及传统的数据融合模型。考虑到区间数据和点数据在机器学习中的权重不等，采用区间点数据融合方法与其他传统的数据融合方法进行比较。研究结果表明，NIR和MIR光谱的间隔点数据融合结合PLS-DA建模可以为HBV-DNA水平检测提供一种快速的方法。
Hepatitis B is an infectious disease cause by the hepatitis B virus (HBV). In recent years, HBV-DNA level clinically gets more attention for its detailed information than other serological markers. Unfortunately, common clinical method for HBV-DNA level detection is limited for its hours consuming. This study combined infrared spectroscopy with machine learning to investigate the feasibility of near-infrared (NIR) and mid-infrared (MIR) spectra for rapid detection of HBV-DNA level. Based on partial least squares-discriminant analysis (PLS-DA) modeling method, the optimal NIR and MIR models and traditional data fusion models were constructed, respectively. Considering inequal weight between interval and point data in machine learning, interval-point data fusion method was used to compare with other traditional date fusion methods. The results of the study illustrate that interval-point data fusion of NIR and MIR spectra combined with PLS-DA modeling can provide a rapid method for HBV-DNA level detection.