Qu Wei, Chen Ruihan, Wang Yang, Zeng Zhiqi, Gao Cheng, Pan Weiqi, Qian Tao, Hon Chitin, Yang Zifeng
State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou City, Guangdong Province, China.
College of Sciences, China Jiliang University, Hangzhou City, Zhejiang Province, China.
China CDC Wkly. 2025 Apr 4;7(14):473-481. doi: 10.46234/ccdcw2025.078.
Seasonal influenza poses a significant public health burden, causing substantial morbidity and mortality worldwide each year. In this context, timely and accurate vaccine strain selection is critical to mitigating the impact of influenza outbreaks. This article aims to develop an adaptive, universal, and convenient method for predicting antigenic variation in influenza A(H1N1), thereby providing a scientific basis to enhance the biannual influenza vaccine selection process.
The study integrates adaptive Fourier decomposition (AFD) theory with multiple techniques - including matching pursuit, the maximum selection principle, and bootstrapping - to investigate the complex nonlinear interactions between amino acid substitutions in hemagglutinin (HA) proteins (the primary antigenic protein of influenza virus) and their impact on antigenic changes.
Through comparative analysis with classical methods such as Lasso, Ridge, and random forest, we demonstrate that the AFD-type method offers superior accuracy and computational efficiency in identifying antigenic change-associated amino acid substitutions, thus eliminating the need for time-consuming and expensive experimental procedures.
In summary, AFD-based methods represent effective mathematical models for predicting antigenic variations based on HA sequences and serological data, functioning as ensemble algorithms with guaranteed convergence.Following the sequence of indicators specified in , we perform a series of operations on A, including feature extension, extraction, and rearrangement, to generate a new input dataset [Formula: see text] for the prediction step. With this newly prepared input, we can compute the predicted results as [Formula: see text].
季节性流感给公共卫生带来了重大负担，每年在全球范围内导致大量发病和死亡。在此背景下，及时准确地选择疫苗毒株对于减轻流感疫情的影响至关重要。本文旨在开发一种适应性强、通用且便捷的方法来预测甲型（H1N1）流感的抗原变异，从而为改进每年两次的流感疫苗选择过程提供科学依据。
该研究将自适应傅里叶分解（AFD）理论与多种技术相结合，包括匹配追踪、最大选择原则和自举法，以研究血凝素（HA）蛋白（流感病毒的主要抗原蛋白）中氨基酸取代之间的复杂非线性相互作用及其对抗原变化的影响。
通过与套索回归、岭回归和随机森林等经典方法进行比较分析，我们证明了AFD类型的方法在识别与抗原变化相关的氨基酸取代方面具有更高的准确性和计算效率，从而无需进行耗时且昂贵的实验程序。
总之，基于AFD的方法是基于HA序列和血清学数据预测抗原变异的有效数学模型，作为具有保证收敛性的集成算法发挥作用。按照[具体内容中]指定指标的顺序，我们对A进行一系列操作，包括特征扩展、提取和重新排列，以生成用于预测步骤的新输入数据集[公式：见原文]。利用这个新准备的输入，我们可以计算预测结果为[公式：见原文]。
