自动检测和从生物医学论文中的表格中提取关键资源
Ibrahim Burak Ozyurt1, Anita Bandrowski1
1FDI Lab, University of California, San Diego, 9500 Gilman Drive, M/C 0608, La Jolla, CA 92093-0608, USA.
bioRxiv : the preprint server for biology
|October 28, 2024
概括
研究人员开发了自动化工具,从BioRxiv预版中提取关键资源表. 这提高了生物医学文献中抗体和细胞系等重要研究材料的可查性.
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:
- 表格对于确定研究资源和试剂至关重要.
- STAR*方法表提高了资源的可查性,但其范围有限.
- 从预印件中自动提取表格可以扩大资源的可发现性.
研究的目的:
- 开发自动化方法,从BioRxiv预版中提取和结构化关键资源表.
- 在更广泛的生物医学文献中提高研究资源的可查性.
主要方法:
- 利用多式联网管道,使用机器学习进行页面识别.
- 用于表检测和结构识别的表转换器模型.
- 开发了一种用于行过分分割的新语言模型,以增强文本提取.
主要成果:
- 实现了大约0.90的网格表相似性 (GriTS) 评分,比0.12显著改善.
- 成功地自动地从PDF预印件中提取关键资源表.
结论:
- 开发的自动化工具可以有效地从BioRxiv.com提取研究资源表.
- 这一进步有助于在更大的文献库中提高关键科学投入的可查性.
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