Exploring the Potential of GANs in Biological Sequence Analysis.

Area of Science:

• Bioinformatics
• Computational Biology
• Machine Learning

Background:

• Biological sequence analysis is crucial for understanding organism functions and developing disease prevention strategies.
• Machine learning (ML) methods offer powerful tools for sequence analysis but struggle with imbalanced datasets common in biology.
• Existing methods like SMOTE focus on local data, potentially overlooking global class distribution.

Purpose of the Study:

• To introduce a novel approach using generative adversarial networks (GANs) to address data imbalance in biological sequence analysis.
• To leverage GANs for generating synthetic biological data that captures overall data distribution.
• To enhance the performance of ML models in biological sequence classification tasks.

Main Methods:

• Utilized generative adversarial networks (GANs) to generate synthetic biological sequence data.
• Applied GAN-generated data to mitigate class imbalance issues in biological datasets.
• Evaluated the approach on four distinct classification tasks using diverse sequence datasets (Influenza A Virus, PALMdb, VDjDB, Host).

Main Results:

• GANs effectively generated synthetic data that closely resembles real biological sequences.
• The GAN-based approach significantly improved classification performance across all tested datasets.
• This method demonstrated superior handling of data imbalance compared to traditional strategies.

Conclusions:

• Generative adversarial networks offer a promising solution for data imbalance in biological sequence analysis.
• GANs enhance machine learning model accuracy by providing a more representative dataset.
• This work paves the way for more robust and reliable biological sequence analysis tools.