技术与信息学能力的涌现促进生物医学融合
Biomedical Convergence Facilitated by the Emergence of Technological and Informatic Capabilities
AI总结：
通过分析PubMed中2160万篇文献的MeSH共现网络，识别出三大知识集群，揭示自1990年代起技术与信息学能力推动的生物医学融合趋势，并发现团队规模与主题多样性正相关且融合近期趋于饱和，建议加强跨学科团队政策支持。
摘要：
我们分析了PubMed索引的2160万篇研究文章的Medical Subject Headings (MeSH)，以映射这一庞大的实体及其关系空间，为生物医学融合的起源与未来提供见解。对MeSH共现网络的详细分析识别出三个稳健的知识集群：微观生物实体与结构的庞大领域；系统、疾病与诊断；以及驱动健康、行为和脑科学前沿的复杂问题背后的涌现生物与社会现象。这些领域从1990年代起通过技术与信息学能力实现整合，这些能力引入了高度可控、可扩展和可排列的研究流程，以及照亮基础结构-功能-行为问题的宝贵成像技术。文章层面分析证实了团队规模与主题多样性之间的正相关关系，并表明融合的显著性在增加，但近期出现饱和。总之，我们的结果呼吁为跨学科团队组建提供更多政策支持，以驾驭跨学科融合。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
