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

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.