A Novel Matching Pursuit Modeling Strategy Based on Adaptive Fourier Decomposition Theory for Predicting Antigenic Variation of Influenza A (H1N1).

Introduction: 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.

Methods: 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.

Results: 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.

Conclusion: 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].