自动检测和从生物医学论文表中提取关键资源
Ibrahim Burak Ozyurt1, Anita Bandrowski2
1FDI Lab Dept of Neuroscience, UCSD, 9500 Gilman Drive M/C 0608, La Jolla, CA, 92093-0608, USA. iozyurt@health.ucsd.edu.
BioData mining
|March 21, 2025
概括
自动化管道从生物医学PDF中提取关键资源表,显著提高数据准确性和研究可重复性. 这提高了抗体和细胞系等重要研究组件的可查性.
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
- 科学出版科学出版
背景情况:
- 关键资源表提高了科学研究中的信息可用性和透明度.
- 提取这些表格的现有方法在范围和可用性方面是有限的.
- 从预印件中自动提取关键资源表可以扩大对结构化研究数据的访问.
研究的目的:
- 开发和评估自动化管道,从生物医学PDF中提取关键资源表.
- 提高对研究资源的结构化信息的准确性和完整性.
- 提高科学研究的可查性和可重复性.
主要方法:
- 使用机器学习开发了四个端到端的表提取管道.
- 表格变压器模型用于表格检测和结构识别.
- 一个字符级GPT语言模型,对合成数据进行了微调,改善了关键资源提取.
主要成果:
- 开发的管道在提取关键资源表方面明显优于GROBID工具.
- 最好的管道实现了0.90的网格表相似性 (GriTS) 评分,而GROBID的0.12.
- 该系统准确地识别和重建关键资源表中的实体.
结论:
- 自动提取管道提高了从表中确定关键资源的准确性.
- 这些工具可以在BioRxiv等预印服务器上部署,以帮助作者纠正错误.
- 公共可用的代码和模型将促进更广泛的采用,并提高科学可重复性.
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