从生物医学文献中挖掘药物标相互作用,使用基于化学和基因描述的集合变压器模型
Jehad Aldahdooh1,2, Ziaurrehman Tanoli3,4, Jing Tang1
1Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki 00290, Finland.
Bioinformatics advances
|August 2, 2024
概括
这项研究利用自然语言处理和预训练的语言模型从生物医学文献中提取药物向相互作用 (DTI). 结合基因和化学描述的整体方法在DTI提取中取得了最高的性能.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算生物学 计算生物学
- 自然语言处理自然语言处理.
背景情况:
- 药物向相互作用 (DTI) 对药物发现和理解药物机制至关重要.
- 从广泛的生物医学文献中提取DTI是具有挑战性的,但至关重要的.
- 自然语言处理 (NLP) 和预训练的语言模型为自动信息提取提供了有希望的解决方案.
研究的目的:
- 应用NLP和预训练过的变压器语言模型来提取DTI.
- 评估组合方法,将基因和化学描述结合起来,以获得最佳的DTI提取性能.
- 来自Entrez Gene和UniProt数据库的不同基因文字描述的有效性进行比较.
主要方法:
- 将DTI提取作为实体关系提取问题的框架.
- 使用预先训练的变压器语言模型,包括BERT.
- 开发了一个集体模型,集成基因描述 (Entrez Gene) 和化学描述 (比较毒基因组学数据库).
主要成果:
- 整体模型在隐藏的DrugProt测试集上获得了80.6 F1分,在提交的模型中排名第一.
- 结合基因和化学描述对于最佳性能至关重要.
- 对比分析提供了对不同基因描述来源 (Entrez Gene,UniProt) 对性能影响的见解.
结论:
- 基于NLP的文本挖掘,特别是使用基因和化学描述,显著提高了药物向提取.
- 拟议的组合模型在从文献中识别DTI方面表现出高效率.
- 这种方法通过改善关键生物关系的提取来促进有效的药物发现.
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