Prediction of transformative breakthroughs in biomedical research
Scientific breakthroughs are rare, as is contemporaneous recognition of their initial expression. Faster, more efficient identification of topics likely to produce future breakthroughs would speed scientific and technological progress. We introduce an AI/ML-detected signature in co-citation networks that recognizes such topics up to twelve years before the breakthrough itself occurs. Our findings illustrate how a better understanding of the scientific process may lead to greater scientific returns.
Authors
- Matthew T. Davis (ORCID: https://orcid.org/0000-0002-2485-6458)
- Salsabil Arabi
- B. Ian Hutchins (ORCID: https://orcid.org/0000-0001-7657-552X)
- George M. Santangelo (ORCID: https://orcid.org/0000-0002-7201-3164)
- Rebecca A. Meseroll (ORCID: https://orcid.org/0000-0001-7373-3991)
- Payam Meyer (ORCID: https://orcid.org/0000-0002-0780-2415)
- Travis Hoppe (ORCID: https://orcid.org/0000-0002-4694-3050)
- Kristine A. Willis (ORCID: https://orcid.org/0000-0003-1790-0753)
- Brad Busse (ORCID: https://orcid.org/0000-0002-5970-3961)
Institutions
- National Institutes of Health (US)
- University of Wisconsin–Madison (US)
- Division of Program Coordination Planning and Strategic Initiatives (US)
- National Cancer Institute (US)
Publication Details
- Journal
- bioRxiv (Cold Spring Harbor Laboratory)
- Published
- 2025-12-17
- DOI
- https://doi.org/10.64898/2025.12.16.694385
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 0.00