Predicting Anti-microbial Resistance using Large Language Models (2401.00642v1)

Abstract: During times of increasing antibiotic resistance and the spread of infectious diseases like COVID-19, it is important to classify genes related to antibiotic resistance. As natural language processing has advanced with transformer-based LLMs, many LLMs that learn characteristics of nucleotide sequences have also emerged. These models show good performance in classifying various features of nucleotide sequences. When classifying nucleotide sequences, not only the sequence itself, but also various background knowledge is utilized. In this study, we use not only a nucleotide sequence-based LLM but also a text LLM based on PubMed articles to reflect more biological background knowledge in the model. We propose a method to fine-tune the nucleotide sequence LLM and the text LLM based on various databases of antibiotic resistance genes. We also propose an LLM-based augmentation technique to supplement the data and an ensemble method to effectively combine the two models. We also propose a benchmark for evaluating the model. Our method achieved better performance than the nucleotide sequence LLM in the drug resistance class prediction.