利用大型语言模型来预测Mycobacterium结核病的抗生素耐药性
Conrad Testagrose1, Sakshi Pandey1, Mohammadali Serajian1
1Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL 32611, United States.
Bioinformatics (Oxford, England)
|July 15, 2025
概括
大型语言模型 (LLMs) 使用基因组数据预测Mycobacterium结核病 (MTB) 的抗生素耐药性. 这种新的方法提高了诊断能力,并指导了个性化的结核病治疗策略.
科学领域:
- 基因组医学是基因组医学.
- 计算生物学 计算生物学
- 传染性疾病 传染性疾病
背景情况:
- 在Mycobacterium结核病 (MTB) 中抗生素耐药性构成了全球主要的健康威胁.
- 准确预测耐药性对于有效治疗和控制耐药菌株传播至关重要.
- 目前的方法需要广泛的数据策划,并与新兴药物耐药性作斗争.
研究的目的:
- 开发和评估一种使用大语言模型 (LLM) 预测MTB抗生素耐药性的新方法.
- 在基因组数据上利用自然语言处理技术来识别抗性模式.
- 探索LLMs在适应新抗生素和发现新型耐药机制方面的潜力.
主要方法:
- 培训和评估基于LLM模型 (LLMTB) 的基因组数据来自12,185个CRyPTIC分离物.
- 利用基于变压器的LLM和自然语言处理来分析复杂的基因组序列.
- 评估模型在预测各种抗生素耐药性的性能.
主要成果:
- 在预测MTB菌株抗生素耐药性的过程中,LLMTB模型取得了很高的性能.
- 通过微调或几次射击学习,LLM证明了对新药的适应性,减少了数据策划需求.
- 该模型确定了关键基因,基因间区域和新型抗性机制,提供了更深入的生物学见解.
结论:
- 在MTB的抗生素耐药性预测中,LLMTB提供了一个变革性的转变.
- 该方法提高了诊断能力,并支持针对结核病的个性化治疗计划.
- 这种方法在全球范围内对抗结核病和抗菌素耐药性作出了重大贡献.
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