We analyzed Medical Subject Headings (MeSH) from 21.6 million research articles indexed by PubMed to map this vast space of entities and their relations, providing insights into the origins and future of biomedical convergence. Detailed analysis of MeSH co-occurrence networks identifies three robust knowledge clusters: the vast universe of microscopic biological entities and structures; systems, disease and diagnostics; and emergent biological and social phenomena underlying the complex problems driving the health, behavioral and brain science frontiers. These domains integrated from the 1990s onward by way of technological and informatic capabilities that introduced highly controllable, scalable and permutable research processes and invaluable imaging techniques for illuminating fundamental structure-function-behavior questions. Article-level analysis confirms a positive relationship between team size and topical diversity, and shows convergence to be increasing in prominence but with recent saturation. Together, our results invite additional policy support for cross-disciplinary team assembly to harness transdisciplinary convergence.
翻译:我们分析了由PubMed编制索引的2 160万篇医学主题标题(MesH),从PubMed编写的2 160万篇研究文章中分析了医学主题标题(MesH),这些研究文章绘制了实体及其关系的广阔空间,为生物医学融合的起源和未来提供了深刻的见解。对MesH共同发生的网络进行的详细分析确定了三个强有力的知识组群:微生物实体和结构的广阔宇宙;系统、疾病和诊断;以及导致健康、行为和大脑科学前沿的复杂问题背后的新出现的生物和社会现象。从1990年代开始,这些领域通过技术和信息能力而融合起来,引入了高度可控、可扩展和可变化的研究过程以及宝贵的成像技术,用于说明基本结构-功能-行为问题。 专业层面分析证实团队规模和专题多样性之间的积极关系,表明趋同将随着近期的饱和而日益突出。我们的成果共同要求为跨学科小组会议提供额外的政策支持,以利用跨学科的趋同。