# Exploring The Potential Of GANs In Biological Sequence Analysis

arXiv: https://arxiv.org/abs/2303.02421 · HTML: https://mlsanity.com/paper/2303.02421/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 34 references that are in the map.
- First posted on arXiv 4 Mar 2023.

## Builds on (most cited first, 3 of 3)

- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Spike2Vec: An Efficient and Scalable Embedding Approach for COVID-19 Spike Sequences](https://mlsanity.com/paper/2109.05019/) (2021)
- [PWM2Vec: An Efficient Embedding Approach for Viral Host Specification from Coronavirus Spike Sequences](https://mlsanity.com/paper/2201.02273/) (2022)

## Built on by (most cited first, 4 of 4)

- [ViralVectors: Compact and Scalable Alignment-free Virome Feature Generation](https://mlsanity.com/paper/2304.02891/) (2023)
- [Generative AI-based data augmentation for improved bioacoustic classification in noisy environments](https://mlsanity.com/paper/2412.01530/) (2024)
- [Rebalancing the Scales: A Systematic Mapping Study of Generative Adversarial Networks (GANs) in Addressing Data Imbalance](https://mlsanity.com/paper/2502.16535/) (2025)
- [EPIC: Enhancing Privacy through Iterative Collaboration](https://mlsanity.com/paper/2411.05167/) (2024)

## Abstract (arXiv)

Biological sequence analysis is an essential step toward building a deeper understanding of the underlying functions, structures, and behaviors of the sequences. It can help in identifying the characteristics of the associated organisms, like viruses, etc., and building prevention mechanisms to eradicate their spread and impact, as viruses are known to cause epidemics that can become pandemics globally. New tools for biological sequence analysis are provided by machine learning (ML) technologies to effectively analyze the functions and structures of the sequences. However, these ML-based methods undergo challenges with data imbalance, generally associated with biological sequence datasets, which hinders their performance. Although various strategies are present to address this issue, like the SMOTE algorithm, which creates synthetic data, however, they focus on local information rather than the overall class distribution. In this work, we explore a novel approach to handle the data imbalance issue based on Generative Adversarial Networks (GANs) which use the overall data distribution. GANs are utilized to generate synthetic data that closely resembles the real one, thus this generated data can be employed to enhance the ML models' performance by eradicating the class imbalance problem for biological sequence analysis. We perform 3 distinct classification tasks by using 3 different sequence datasets (Influenza A Virus, PALMdb, VDjDB) and our results illustrate that GANs can improve the overall classification performance.