一个开源的生命科学知识图表生态系统
Tiffany J Callahan1,2, Ignacio J Tripodi3, Adrianne L Stefanski4
1Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA. tiffany.callahan@cuanschutz.edu.
Scientific data
|April 11, 2024
概括
PheKnowLator自动化了可定制表示的表型知识图 (KG) 的 FAIR 构建. 这种语义生态系统解决了生物医学数据整合的挑战,提供了灵活性,而不牺牲性能或可用性.
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
- 数据科学数据科学数据科学
背景情况:
- 翻译性研究需要在多个尺度上整合生物数据.
- 多omics和测序方面的进步产生了庞大的数据集,这带来了重大整合挑战.
- 现有的知识图 (KG) 构建方法在复杂的生物医学问题上的知识表示上提供了有限的灵活性.
研究的目的:
- 引入PheKnowLator,这是一个用于自动化,FAIR (可查找,可访问,可互操作,可重复使用) KG建设的语义生态系统.
- 为生物医学KG提供一个完全可定制的知识表示框架.
- 解决现有的KG建筑工具在灵活性和选择方面的局限性.
主要方法:
- 开发了PheKnowLator作为一个有KG构建资源,分析工具和基准的语义生态系统.
- 包括数据准备API,SPARQL终端资源和抽象算法.
- 设计用于在本体学基础的KG中完全可定制的知识表示.
主要成果:
- 评估了PheKnowLator与现有的开源KG构建方法相比.
- 分析了使用12个大规模KG构建任务的计算性能.
- 证明PheKnowLator能够实现完全可定制的KG,同时保持性能和可用性.
结论:
- PheKnowLator提供了一种灵活和自动化的方法来构建FAIR生物医学知识图.
- 生态系统支持可定制的知识表示,克服现有工具的局限性.
- PheKnowLator增强了用于翻译研究的多学科数据的整合.
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