DNA-LLM is a project aimed at training a Language Model specifically for DNA sequences. The model is designed to pre-train on a large dataset of DNA sequences and then fine-tune for a specific task, such as virus classification.

Select a transformer-based architecture suitable for language modeling tasks. GPT (Generative Pre-trained Transformer) is recommended.

Assemble a diverse and representative dataset of DNA sequences for pre-training the language model.

Tokenize the DNA sequences into tokens suitable for the chosen model architecture. Each token represents a nucleotide or a group of nucleotides.

Design a language modeling architecture. The objective is typically to predict the next token in a sequence.

Define a loss function that measures the difference between the predicted token probabilities and the actual next token.

Train the model on the pre-training dataset. This step is computationally intensive and may require significant resources.

Assemble a new dataset where each DNA sequence is labeled as belonging to a virus or not.

Remove the head responsible for predicting the next token in the pre-trained model and replace it with a new head suitable for binary classification.

Define a new loss function suitable for binary classification, such as binary cross-entropy.

Fine-tune the model on the virus classification dataset. Initialize the model with the pre-trained weights and train the new head on the new dataset.

Evaluate the performance of the fine-tuned model on a separate validation set. Adjust hyperparameters as needed.

Use the fine-tuned model for predicting whether a new DNA sequence belongs to a virus or not.