一个全面的大规模生物医学知识图表,用于人工智能驱动的数据驱动的生物医学研究
Yuan Zhang1,2, Xin Sui1, Feng Pan2
1Department of Statistics, Florida State University, Tallahassee, FL 32306.
bioRxiv : the preprint server for biology
|January 3, 2024
概括
我们构建了iKraph,这是一个来自PubMed摘要的大型生物医学知识图,实现了信息提取的人类水平的准确性. 这使得先进的自动化知识发现和药物重新利用成为可能,大大改善了研究洞察力.
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:
- 科学文献的增长需要先进的数据整合工具.
- 知识图 (KG) 对于整合异构的生物医学数据至关重要.
- 自动化知识发现 (AKD) 面临着无结构文本和准确性的挑战.
研究的目的:
- 从PubMed摘要中构建一个大规模,高精度的生物医学知识图 (iKraph).
- 通过整合公共数据库和基因组学数据来提高KG的全面性.
- 开发和评估AKD方法,用于诸如药物重用等应用.
主要方法:
- 利用一流的NLP管道从PubMed摘要中提取信息.
- 来自40个公共数据库和高通量基因组学数据的综合数据.
- 开发了一种概率推理方法,用于识别间接因果关系.
主要成果:
- 构建了iKraph,KG的内容超过了手动策划,并且与人类注释相匹配.
- 启用了严格的AKD性能评估.
- 每月确定600-1400种候选药物用于COVID-19的重新用途,经过验证的发现.
结论:
- iKraph KG和AKD方法为生物医学研究提供了强大的资源.
- 开发的方法显著推进了自动化知识发现和药物重新用途.
- 一个云平台可用于学术访问KG和工具.
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