DiMB-RE:为饮食微生物组协会挖掘科学文献
Gibong Hong1, Veronica Hindle2, Nadine M Veasley3
1School of Information Sciences, University of Illinois Urbana-Champaign, Champaign, IL 61820, United States.
概括
我们创建了DiMB-RE,这是一个用于饮食微生物组研究的大体. 在这些数据上训练的NLP模型显示出了解健康和营养的潜力,尽管复杂的信息提取仍然存在挑战.
科学领域:
- 生物医学信息学 生物医学信息学
- 微生物组研究 微生物组研究
- 营养科学 营养科学
背景情况:
- 了解饮食-微生物组相互作用对于健康和疾病至关重要.
- 生物医学文献包含了大量关于这些关联的非结构化信息.
- 需要自动化方法来有效地提取和分析这些复杂的数据.
研究的目的:
- 开发一个全面的语料库 (DiMB-RE),对饮食与微生物组的关联进行注释.
- 训练和评估自然语言处理 (NLP) 模型来识别这些关联.
- 支持个性化的营养策略,并进一步了解微生物组在健康中的作用.
主要方法:
- 构建了DiMB-RE,一个包含15个实体和13个关系类型的语料库.
- 精心调整的最先进的NLP模型用于命名实体,触发器和关系提取.
- 基准生成型大型语言模型 (GPT-4o-mini,GPT-4o) 在零和一次性设置中.
主要成果:
- DiMB-RE包含来自165个出版物的14450个实体和4206个关系.
- 精心调整的NLP模型在命名实体识别方面获得了F1得分0.800,在关系提取方面获得了0.445.
- 生成模型的准确性低于微调模型;结果部分注释改善了关系提取.
结论:
- DiMB-RE是一个大型,多样化的关于饮食与微生物群相互作用的集体.
- 在DiMB-RE上微调的NLP模型突出了这个领域信息提取的复杂性.
- DiMB-RE作为生物医学文献挖掘的基准;模型和语料库是公开的.
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