# Enhanced Viral Genome Classification Using Large Language Models
## 1 Introduction  
Genomic sequence classification is a fundamental task in bioinformatics that helps understand the taxonomy of viruses. The taxonomic classification of a virus becomes especially important during a pandemic or widespread epidemic. Identifying the virus taxonomy is crucial for tailoring targeted treatments, expediting the development of drugs and vaccines, and enhancing public health response strategies, including outbreak control measures [1]. There are around 10 nonillion (\(10^{31}\)) individual viruses existing on the Earth [2]. The genomic sequence frequently undergoes mutations that alter its genetic structure, resulting in a new variant, making it difficult to identify its taxonomy. Several bioinformatics tools utilize the local alignment method to classify genome sequences. BLAST version 1.30+ (Basic Local Alignment Search Tool) is one of the most widely used tools for comparing a DNA sequence with a database of known DNA sequences and identifying regions of similarity [3]. The classification is based on the top matching sequences in the database. Some tools utilize local and global alignment methods, such as the Smith-Waterman algorithm [3], for classifying DNA sequences. All thesealignment methods can achieve high precision only if the sequence is already known and present in the database. As the genome sequence tends to mutate, many of the strains remain unclassified. To overcome the above limitation, AI uses alignment-free methods such as extracting features from DNA sequences, including k-mer [3], statistical features [4] such as GC content, dinucleotide frequencies, codon usage bias, and the presence of specific motifs. These extracted features are utilized by machine-learning models, such as Random Forest (RF), K-nearest neighbor (KNN), Naive Bayes (NB), Decision Tree (DT), and Support Vector Machine (SVM) [5; 6; 7], to create an accurate classification model for classifying genomic sequences. Deep learning algorithms are more powerful than machine learning algorithms as they can automatically extract complex features from datasets and learn complex patterns autonomously. In contrast, machine learning algorithms are more versatile and often sufficient for more straightforward tasks. Machine learning algorithms are effective with small datasets. However, as both the dataset size and DNA sequence length increase, deep learning models tend to outperform traditional machine learning approaches. Models such as CNN, LSTM, and hybrid models like CNN-LSTM and CNN-Bidirectional LSTM are used in DNA sequence classification [8; 9; 10]. Recently, Transformers have gained significant attention after the release of ChatGPT (3.1). Transformers are primarily used for NLP tasks, such as text generation, summarization, and prediction. These Transformer models can also be used to understand genomic sequences, which are similar to natural language [11]. Given the similarities between genomic sequences and natural language, Transformers can be effectively applied to understanding genomic sequences. Their capabilities encompass a range of tasks, including genomic sequence classification, promoter prediction, gene expression prediction, and other related functions. In this study, we utilized pre-trained large language models (LLMs) that employ the Transformer architecture as their foundational framework [12; 13; 14; 15; 16]. These LLMs are initially trained on vast datasets and subsequently fine-tuned specifically on Human DNA sequences as well as DNA from various other species. This fine-tuning enables the models to effectively understand and interpret the language of DNA. LLMs can handle long DNA sequences, which can be challenging for traditional methods.