Biomedical Convergence Facilitated by the Emergence of Technological and Informatic Capabilities

Abstract

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.

arXiv comment12 pages, 4 figures; 8 pages of Supplementary Information

References in corpus (12)

• Fast unfolding of communities in large networks
• Principles of scientific research team formation and evolution
• Invention as a Combinatorial Process: Evidence from U.S. Patents
• Mapping Topics and Topic Bursts in PNAS
• The Memory of Science: Inflation, Myopia, and the Knowledge Network
• Grand challenges and emergent modes of convergence science
• A quantitative perspective on ethics in large team science
• Detecting global bridges in networks
• Quantifying the distribution of editorial power and manuscript decision bias at the mega-journal PLOS ONE
• Absolute and specific measures of research group excellence
• Accelerating Scientific Discovery by Formulating Grand Scientific Challenges
• Evolution of biomedical innovation quantified via billions of distinct article-level MeSH keyword combinations