将策划整合到科学出版中,以训练人工智能模型
Jorge Abreu-Vicente1, Hannah Sonntag1, Thomas Eidens1
1EMBO, Heidelberg, 69117, Germany.
Bioinformatics (Oxford, England)
|December 27, 2025
概括
这项研究将数据策划整合到学术出版中,为机器学习创建了一个大型数据集 (SourceData-NLP). 这样可以更好地分析生物医学研究数据和文本.
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:
- 从学术文章中提取高通量数据和结构化标签对于机器学习和二次分析至关重要.
- 现有的方法缺乏与出版工作流程的整合和实验角色和方法的全面注释.
- 需要在科学文献中进行先进的生物实体识别和注释.
研究的目的:
- 在学术出版过程中嵌入多式联络数据策划.
- 为培训生物医学研究中的AI模型创建一个全面的数据集.
- 为了提高图表面板和标题的注释准确度.
主要方法:
- 在学术出版工作流程中集成多式联络数据策划.
- 利用自然语言处理和作者反进行注释.
- 有注释的细分图片面板和标题来自分子和细胞生物学文章.
- 开发了新的AI任务来评估数据集的实用性,包括命名实体识别和上下文依赖的语义分析.
主要成果:
- 创建了SourceData-NLP数据集,包含来自3,223篇文章中的18,689个数字中的超过62万个注释的生物医学实体.
- 附注包括八个生物实体类别和实验角色/方法.
- 证明了数据集对人工智能模型训练在命名实体识别,图片标题细分和新的语义任务中的实用性.
- 展示了多模式应用程序,用于将图形细分为面板和标题.
结论:
- 源数据-NLP数据集显著增强了生物医学研究中的机器学习应用.
- 将数据策划整合到出版中,简化了创建有价值,结构化的数据集的过程.
- 开发的模型和数据集有助于对科学数字和文本进行高级分析.
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