生物医学研究的景观
Rita González-Márquez1,2, Luca Schmidt1,2, Benjamin M Schmidt3
1Hertie Institute for AI in Brain Health, University of Tübingen, Germany.
Patterns (New York, N.Y.)
|July 15, 2024
概括
研究人员使用PubMedBERT和t-SNE.NET创建了2100万篇生物医学文章的二维地图. 这张地图可视化了科学趋势,包括COVID-19研究和机器学习的采用,为生命科学文献提供了新的见解.
科学领域:
- 生物医学信息学 生物医学信息学
- 图书统计学 图书统计学
- 计算生物学 计算生物学
背景情况:
- 生物医学出版物的指数增长给跟踪科学进步和理解领域演变带来了挑战.
- 目前用于分析大规模科学文献的现有方法通常在范围和可扩展性方面受到限制.
研究的目的:
- 开发一个全面的二维 (2D) 整个生物医学文献库的地图.
- 为了使科学出版物中的趋势,新兴领域和特定现象的可视化和分析.
主要方法:
- 利用了PubMed数据库中的2100万篇英语文章的摘要.
- 使用PubMedBERT大型语言模型进行抽象嵌入.
- 应用了针对大型数据集优化的t分布式静态邻居嵌入 (t-SNE) 来生成二维地图.
主要成果:
- 成功创建了一个2D地图,代表了广泛的生物医学文献.
- 分析了COVID-19研究的出现,神经科学的演变以及机器学习的采用.
- 研究了学术作者的性别不平衡以及收回的纸厂文章的流行情况.
结论:
- 2D地图为导航和理解生物医学研究复杂的景观提供了一个新的工具.
- 互动网站有助于探索和发现科学文献中的见解.
- 这种方法可以帮助研究人员识别趋势,理解学科转变,并解决著作权和研究完整性等问题.
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