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Can Large Language Models Classify and Generate Antimicrobial Resistance Genes?
Conference proceeding

Can Large Language Models Classify and Generate Antimicrobial Resistance Genes?

Hyunwoo Yoo, Haebin Shin and Gail Rosen
Proceedings of the 24th Workshop on Biomedical Language Processing, pp 240-248
01 Jan 2025
DOI: https://doi.org/10.18653/v1/2025.bionlp-1.21
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https://doi.org/10.18653/v1/2025.bionlp-1.21View
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Abstract

Computer Science, Artificial Intelligence Engineering, Biomedical Language & Linguistics Linguistics Science & Technology Computer Science Engineering Social Sciences Technology
This study explores the application of generative Large Language Models (LLMs) in DNA sequence analysis, highlighting their advantages over encoder-based models like DNABERT2 and Nucleotide Transformer. While encoder models excel in classification, they struggle to integrate external textual information. In contrast, generative LLMs can incorporate domain knowledge, such as BLASTn annotations, to improve classification accuracy even without fine-tuning. We evaluate this capability on antimicrobial resistance (AMR) gene classification, comparing generative LLMs with encoder-based baselines. Results show that LLMs significantly enhance classification when supplemented with textual information. Additionally, we demonstrate their potential in DNA sequence generation, further expanding their applicability. Our findings suggest that LLMs offer a novel paradigm for integrating biological sequences with external knowledge, bridging gaps in traditional classification methods.

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Collaboration types
International collaboration
Web of Science research areas
Computer Science, Artificial Intelligence
Engineering, Biomedical
Language & Linguistics
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