Developing a Predictive Platform for <em>Salmonella</em> Antimicrobial Resistance Based on a Large Language Model and Quantum Computing

Yujie You; Kan Tan; Zekun Jiang; Le Zhang

doi:10.1016/j.eng.2025.01.013

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Engineering ›› 2025, Vol. 48 ›› Issue (5) : 174-184. DOI: 10.1016/j.eng.2025.01.013

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Developing a Predictive Platform for Salmonella Antimicrobial Resistance Based on a Large Language Model and Quantum Computing

Yujie You^a ,
Kan Tan^a ,
Zekun Jiang^a^,^b ,
Le Zhang^a^,^b^,^*

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Abstract

As a common foodborne pathogen, Salmonella poses risks to public health safety, common given the emergence of antimicrobial-resistant strains. However, there is currently a lack of systematic platforms based on large language models (LLMs) for Salmonella resistance prediction, data presentation, and data sharing. To overcome this issue, we firstly propose a two-step feature-selection process based on the chi-square test and conditional mutual information maximization to find the key Salmonella resistance genes in a pan-genomics analysis and develop an LLM-based Salmonella antimicrobial-resistance predictive (SARPLLM) algorithm to achieve accurate antimicrobial-resistance prediction, based on Qwen2 LLM and low-rank adaptation. Secondly, we optimize the time complexity to compute the sample distance from the linear to logarithmic level by constructing a quantum data augmentation algorithm denoted as QSMOTEN. Thirdly, we build up a user-friendly Salmonella antimicrobial-resistance predictive online platform based on knowledge graphs, which not only facilitates online resistance prediction for users but also visualizes the pan-genomics analysis results of the Salmonella datasets.

Keywords

Salmonella resistance prediction / Pan-genomics / Large language model / Quantum computing / Bioinformatics

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Yujie You, Kan Tan, Zekun Jiang, Le Zhang. Developing a Predictive Platform for Salmonella Antimicrobial Resistance Based on a Large Language Model and Quantum Computing. Engineering, 2025, 48(5): 174‒184 https://doi.org/10.1016/j.eng.2025.01.013

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