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Eagp: An Efficient Generative Augmentation Framework For Phage Protein Classification Under Severe Class Imbalance.

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Eagp: An Efficient Generative Augmentation Framework For Phage Protein Classification Under Severe Class Imbalance.

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Phage Phenomics: Physiological Approaches to Characterize Novel Viral Proteins

Phage Phenomics: Physiological Approaches to Characterize Novel Viral Proteins

Published on: June 11, 2015

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

Jiaru Li¹, Haoxiang Li¹, Yansu Wang^2,3

¹Faculty of Information Science and Engineering, Ocean University of China, Shandong 266100, China.

Bioinformatics (Oxford, England)

|June 15, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces EAGP, a novel method combining generative models and protein language models to accurately classify phage proteins, especially rare ones. EAGP significantly improves classification performance and addresses data imbalance issues in bacteriophage research.

Keywords:

Bacteriophage classification Imbalanced learning Protein language models Wasserstein GAN

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Area of Science:

Bioinformatics
Computational Biology
Machine Learning in Genomics

Background:

Accurate phage protein classification is crucial for bacteriophage research.
Data imbalance, especially with rare protein sequences, hinders machine learning model performance.
Previous methods using re-weighting have limited feature extraction capabilities.

Purpose of the Study:

To introduce EAGP, a novel approach for robust phage protein classification.
To address data imbalance and improve performance on rare protein sequences.
To enhance protein function annotation and binary classification tasks.

Main Methods:

Integration of a generative model (WGAN-like for 1D data) with the Evolutionary Scale Modeling (ESM) protein large language model.

Utilizing ESM for robust feature extraction from protein sequences.

Application of the EAGP approach to binary classification and protein function annotation.

Main Results:

EAGP demonstrates exceptional performance in classification and annotation tasks.
The method significantly improves overall classification efficacy.
EAGP effectively alleviates performance degradation in minority classes.

Conclusions:

EAGP offers a powerful solution for phage protein classification, particularly for imbalanced datasets.
The integration of generative models and LLMs enhances feature extraction and classification accuracy.
This approach advances bacteriophage research by improving the analysis of rare protein sequences.