BASIL DB:生物活性语义集成和链接数据库
David Jackson1, Paul Groth2, Hazar Harmouch2
1University of Amsterdam, Amsterdam, The Netherlands. d.i.jackson@uva.nl.
Journal of biomedical semantics
|August 13, 2025
概括
BASIL DB是一个新的知识图数据库,使用自然语言处理来组织生物活性化合物,食物和健康影响. 该资源增强了研究效率,并为个性化营养和疾病预防发现了洞察力.
科学领域:
- 营养科学 营养科学
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 食物和植物中的生物活性化合物提供了抗氧化和抗炎作用等健康益处.
- 个性化营养和疾病预防方面的研究正在扩大,但面临着数据复杂性和文献增长的挑战.
- 生物活性语义整合和链接数据库 (BASIL DB) 是为了使用知识图的方法来解决这些挑战而开发的.
研究的目的:
- 为生物活性化合物创建一个可扩展和全面的知识图数据库.
- 简化对生物活性化合物,食品及其对健康影响的数据的组织和分析.
- 促进对生物活性化合物在疾病预防和个性化营养中的作用的研究.
主要方法:
- 从结构化数据库和PubMed收集随机对照试验 (RCT) 的数据.
- 数据预处理涉及清理不一致性和结构化数据.
- 利用自然语言处理 (NLP) 工具,包括大型语言模型 (LLM),从临床试验中提取数据.
- 将提取的数据集成到知识图中,将食品,生物活性和健康状况作为节点,并将它们的相互作用作为加权边缘.
主要成果:
- 巴西尔 DB 包含 433 种化合物, 40,296 篇研究论文, 7,256 篇健康影响和 4,197 篇食品.
- 数据库提供查询和可视化功能,包括交互式图表和自定义过.
- 用户可以探索生物活性物和健康影响之间的关系,提高研究效率.
结论:
- BASIL DB是一个结构化的知识图资源,用于探索生物活性物,食品和健康结果之间的关系.
- 它代表了朝着一种系统的,数据驱动的方法迈出的一步,以了解生物活性化合物的健康影响.
- 未来的工作包括数据库扩展和方法改进,以弥合传统和常规营养方法.
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