Evidence map›Paper›PMID 40376443›Full record

ArticleChina CDC weekly2025

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

Wei Qu, Ruihan Chen, Yang Wang, Zhiqi Zeng, Cheng Gao, Weiqi Pan, Tao Qian, Chitin Hon, Zifeng Yang

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PubMed Full text at PMC Publisher
In one paragraph

Article in China CDC weekly, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Wei Qu *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.
Ruihan Chen *Department of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology, Macao Special Administrative Region, China.
Yang WangState 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.
Zhiqi ZengGuangzhou key laboratory for clinical rapid diagnosis and early warning of infectious diseases, KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou City, Guangdong Province, China.
Cheng GaoDepartment of Electrical Engineering & Computer Science, College of Engineering, University of Missouri, Columbia, MO, USA.
Weiqi PanState 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.
Tao QianMacau Center for Mathematical Sciences, Macao University of Science and Technology, Macao Special Administrative Region, China.
Chitin HonDepartment of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology, Macao Special Administrative Region, China.
Zifeng YangState 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.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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

Indexed as

Adaptive Fourier DecompositionAntigenic VariationInfluenza A (H1N1)Prediction

Identifiers

PMID40376443
PMCPMC12075454

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