使用大型语言模型自动化候选基因优先级:从天真的评分到基于文献的验证
Taushif Khan1, Mohammed Toufiq1, Marina Yurieva1
1The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, Connecticut, 06032, United States.
Bioinformatics (Oxford, England)
|October 10, 2025
概括
这项研究提供了一个新的框架,用于使用大型语言模型 (LLM) 和文献验证来优先考虑治疗基因标. 该方法成功地识别了与败血症相关的基因和新型治疗候选者,克服了LLM的局限性,获得可靠的生物学见解.
科学领域:
- 生物医学研究的研究.
- 计算生物学是一种计算生物学.
- 基因组学就是基因组学.
背景情况:
- 转录基因研究产生了大量的基因数据,这给确定治疗点带来了挑战.
- 大型语言模型 (LLM) 显示了基因优先级的前景,但往往是幻觉和缺乏验证.
- 对专家知识进行系统的验证对于可靠的基因优先级确定至关重要.
研究的目的:
- 使用LLM和文献开发和验证系统基因优先级的计算框架.
- 为了确定高可信度的治疗性毒症基因标.
- 通过严格的验证,克服LLMs在生物医学研究中的局限性.
主要方法:
- 开发了一个两阶段的框架,将LLM基础的选与文献验证相结合.
- 用多标准评估和从败血症出版物中提取增强生成来选血相关性的基因.
- 一个忠诚度评估系统确保了LLM预测与文献证据保持一致.
主要成果:
- 该框架确定了609个与败血症相关的基因,其过效率高,富含炎症途径.
- 确定了30个超高可信度的治疗候选者,包括新的标和已知的败血症基因.
- 基准验证实现了71.2%的回忆,将计算信心与证据质量相关联.
结论:
- 该框架将不可靠的LLM输出转化为系统验证的生物见解.
- 它为优先考虑实验验证和以文献为指导的生物标志物发现提供了一个实用的工具.
- 模块化设计允许适应其他疾病,提供了一种多功能方法.
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