EAGP: an efficient generative augmentation framework for phage protein classification under severe class imbalance

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ID: 317327
2026
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Abstract
Abstract Motivation The accurate classification of phage proteins is critical for advancing bacteriophage research. Despite the proliferation of machine learning approaches in this domain, the persistent issue of data imbalance continues to hinder performance, particularly for rare protein sequences. Previous attempts to address this by re-weighting minority classes have faced limitations due to insufficient feature extraction capabilities. Results In this paper, we introduce EAGP, a novel approach that integrates a generative model—functionally equivalent to a WGAN yet tailored for one-dimensional data—with the Evolutionary Scale Modeling (ESM) protein large language model for robust feature extraction. EAGP exhibits exceptional performance in binary classification and protein function annotation tasks. Crucially, our method not only improves overall classification efficacy but also significantly alleviates the performance degradation typically observed in minority classes. Availability and Implementation The data and code underlying this article are available in GitHub at https://github.com/Innerly/EAGP and have been archived on Zenodo at https://doi.org/10.5281/zenodo.19928069. Supplementary information Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7164853003 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors J S Li, Hui Li, Yansu Wang, Quan Zou, Hongling Zhu
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag373
URL
https://doi.org/10.1093/bioinformatics/btag373
Keywords Keywords not found

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