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Analysis of MeSH co-occurrence networks based upon 21.6 million research articles indexed by PubMed identifies three robust knowledge clusters: micro-level biological entities and structures; meso-level representations of systems, and diseases and diagnostics; and emergent macro-level biological and social phenomena. Analysis of cross-cluster dynamics shows how these domains integrated from the 1990s onward via technological and informatic capabilities\u00a0\u2014 captured by MeSH belonging to the \u201cTechnology, Industry, and Agriculture\u201d (J) and \u201cInformation Science\u201d (L) branches\u00a0\u2014 representing highly controllable, scalable and permutable research processes and invaluable imaging techniques for illuminating fundamental yet transformative structure\u2013function\u2013behavior questions. Our results indicate that 8.2% of biomedical research from 2000 to 2018 include MeSH terms from both the J and L MeSH branches, representing a 291% increase from 1980s levels. Article-level MeSH analysis further identifies the increasing prominence of cross-domain integration, and confirms a positive relationship between team size and topical diversity. Journal-level analysis reveals variable trends in topical diversity, suggesting that demand and appreciation for convergence science vary by scholarly community. Altogether, we develop a knowledge network framework that identifies the critical role of techno-informatic inputs as convergence bridges\u00a0\u2014 or catalyzers of integration across distinct knowledge domains\u00a0\u2014 as highlighted by the 1990s genomics revolution, and onward in contemporary brain, behavior and health science initiatives.<\/jats:p>","DOI":"10.1142\/s0219525923500030","type":"journal-article","created":{"date-parts":[[2023,5,17]],"date-time":"2023-05-17T07:12:18Z","timestamp":1684307538000},"source":"Crossref","is-referenced-by-count":10,"title":["BIOMEDICAL CONVERGENCE FACILITATED BY THE EMERGENCE OF TECHNOLOGICAL AND INFORMATIC CAPABILITIES"],"prefix":"10.1142","volume":"26","author":[{"given":"DONG","family":"YANG","sequence":"first","affiliation":[{"name":"Department of Management of Complex Systems, Ernest and Julio Gallo Management Program, School of Engineering, University of California, Merced, California 95343, USA"}]},{"given":"IOANNIS","family":"PAVLIDIS","sequence":"additional","affiliation":[{"name":"Computational Physiology Laboratory, Department of Computer Science, University of Houston, Houston, Texas 77204, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0955-3483","authenticated-orcid":false,"given":"ALEXANDER MICHAEL","family":"PETERSEN","sequence":"additional","affiliation":[{"name":"Department of Management of Complex Systems, Ernest and Julio Gallo Management Program, School of Engineering, University of California, Merced, California 95343, USA"}]}],"member":"219","published-online":{"date-parts":[[2023,6,26]]},"reference":[{"key":"S0219525923500030BIB001","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1287\/mnsc.47.1.117.10671","volume":"47","author":"Fleming L.","year":"2001","journal-title":"Manage. 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