A Novel Matching Pursuit Modeling Strategy Based on Adaptive Fourier Decomposition Theory for Predicting Antigenic Variation of Influenza A (H1N1).
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 <i>I</i>, we perform a series of operations on A<sub>1</sub>, 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].
Authors
- Chi Tin Hon (ORCID: https://orcid.org/0000-0002-5658-3503)
- Weiqi Pan (ORCID: https://orcid.org/0009-0003-1989-4008)
- Zifeng Yang (ORCID: https://orcid.org/0000-0002-2681-4171)
- Yang Wang (ORCID: https://orcid.org/0000-0002-6527-864X)
- Cheng Gao (ORCID: https://orcid.org/0000-0002-2182-0804)
- Wei Qu (ORCID: https://orcid.org/0000-0002-3935-6763)
- Ruihan Chen
- Tao Qian (ORCID: https://orcid.org/0009-0008-9235-5613)
- Zhiqi Zeng
Institutions
- University of Missouri (US)
Publication Details
- Journal
- PubMed
- Published
- 2025-04-04
- DOI
- https://doi.org/10.46234/ccdcw2025.078
- Primary Topic
- Influenza Virus Research Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00