Adversarial Examples for DNA Classification

Pre-trained language models such as DNABERT2 and Nucleotide Transformer, which are trained on DNA sequences, have shown promising performance in DNA sequence classification tasks. The classification ability of these models stems from language models trained on vast amounts of DNA sequence samples, followed by fine-tuning with relatively smaller classification datasets. However, these text-based systems are not robust enough and can be vulnerable to adversarial examples. While adversarial attacks have been widely studied in text classification, there is limited research in DNA sequence classification. In this paper, we adapt commonly used attack algorithms in text classification for DNA sequence classification. We evaluated the impact of various attack methods on DNA sequence classification at the character, word, and sentence levels. Our findings indicate that actual DNA language model sequence classifiers are vulnerable to these attacks.

Predicting Anti-microbial Resistance using Large Language Models

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 language models, many language models 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 language model but also a text language model based on PubMed articles to reflect more biological background knowledge in the model. We propose a method to fine-tune the nucleotide sequence language model and the text language model 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 language model in the drug resistance class prediction.