Busse, B. L. | Search bioRxiv

bioRxiv2025

A thirty-year trend of increasing clinical orientation at the National Institutes of Health

It is widely recognized that funding for biomedical research supports the development of major medical advances. However, little systematic effort has been made to determine whether a link exists between the types of funding opportunities that are available to scientists and progress towards new treatments in the clinic. To better understand this relationship, we analyzed the funding opportunities offered by the National Institutes of Health (NIH) over a span of thirty-two years, together with the resulting portfolio of applications and awards. We found NIH funding opportunity announcements became more numerous and increasingly clinically oriented over that span, following a trend that parallels the increasing clinical and translational orientation of both NIH grant applications and NIH-funded publications. Surprisingly, this increase appears to be independent of the representation of clinician-scientists in the NIH workforce.

scientific communication and education↗

bioRxiv2025

Prediction of transformative breakthroughs in biomedical research

The ability to predict scientific breakthroughs at scale would accelerate the pace of discovery and improve the efficiency of research investments. Recent advances in artificial intelligence, graph theory, and computing power have provided new ways to pursue this elusive goal. We have identified a common signature within co-citation networks that accurately predicts the occurrence of breakthroughs in medical research, on average more than 5 years in advance of the subsequent publication(s) that announced the discovery. A combination of features produces these diagnostic signals: a burst of papers exploring a novel scientific concept, an unusually high number of very influential papers in specialty journals, and low topical cohesion of the associated content. We analyzed two different periods separated by 20 years to show that the kinetics of breakthrough formation are conserved, suggesting that our approach can be used to predict which topics will produce future transformative discoveries. Significance statementScientific 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.

scientific communication and education↗