The Influence of Biomedical Research on Future Business Funding: Analyzing Scientific Impact and Content in Industrial Investments

TL;DR

The research incorporates bibliometric analyses along with SBIR data to yield a holistic view of the science- industry interface, opening avenues for more strategic resource allocation and policy developments aimed at fostering innovation.

Abstract

This paper investigates the relationship between scientific innovation in biomedical sciences and its impact on industrial activities, focusing on how the historical impact and content of scientific papers influenced future funding and innovation grant application content for small businesses. The research incorporates bibliometric analyses along with SBIR (Small Business Innovation Research) data to yield a holistic view of the science-industry interface. By evaluating the influence of scientific innovation on industry across 10,873 biomedical topics and taking into account their taxonomic relationships, we present an in-depth exploration of science-industry interactions where we quantify the temporal effects and impact latency of scientific advancements on industrial activities, spanning from 2010 to 2021. Our findings indicate that scientific progress substantially influenced industrial innovation funding and the direction of industrial innovation activities. Approximately 76% and 73% of topics showed a correlation and Granger-causality between scientific interest in papers and future funding allocations to relevant small businesses. Moreover, around 74% of topics demonstrated an association between the semantic content of scientific abstracts and future grant applications. Overall, the work contributes to a more nuanced and comprehensive understanding of the science-industry interface, opening avenues for more strategic resource allocation and policy developments aimed at fostering innovation.

Figures (7)

• Figure 1: The bidirectional flow of breakthroughs between academia and the industry. Above the timeline, blue boxes represent scientific discoveries that have later catalyzed commercial applications, as indicated by the arrows leading to red boxes. Below the timeline, red boxes mark industry innovations that have subsequently inspired scientific exploration and advancement, traced back to blue boxes. The interchanging paths underscore the dynamic exchange between scientific inquiry and industrial application across different fields.
• Figure 2: Illustration of shared MeSH term labels between a PubMed paper ABBASI2021104121 and an SBIR project description (https://www.sbir.gov/node/1327383).
• Figure 3: This figure illustrates the calculation of the Cross-Correlation Area Under the Curve (CCAUC) ratio, a measure of the lead-lag relationship between scientific advancements and industrial activities. The line graphs on the left side, with science represented by blue lines and industry by red lines, track the sum of impact scores over time (note that the line graphs are schematic representations and do not depict actual trends; they are included solely for illustration purposes to demonstrate the conceptual framework). In the top cross-correlation plot, a rightward skew with a larger blue area indicates scenarios where scientific trends precede industrial ones, yielding a greater CCAUC ratio. The bottom plot shows the inverse, with a leftward skew and a more pronounced red area where industry leads science. The CCAUC ratio itself is derived by dividing the positive-lagged area (science leading) by the negative-lagged area (industry leading) under the cross-correlation curve. This distinction between line graphs and cross-correlation plots highlights not only the direction but also the temporal lag and the quantitative extent of impact between scientific research and industrial application.
• Figure 4: For each year pair, scientific and industrial abstracts are spatially mapped and normalized for similarity calculations. Each pair of embeddings generates a unique similarity value for its respective position in the grid. The contextual similarity between current scientific abstracts and future industrial project descriptions populates in the upper triangle, and vice versa in the lower triangle. The triangular ratio ($tr$), representing the influence of scientific context on future industrial projects, is the cumulative sum of similarities in the upper to lower triangle.
• Figure 5: The precentage of CCAUC ratios exceeding one (see "\ref{['sec:cc']}") (top) and significant p-values (bottom) for both frequency (see "\ref{['sec:freq']}") and impact (see "\ref{['sec:imp']}") representations of the scientific and industrial data at multiple depths of MeSH terms. The x-axis denotes the topic resolution within the MeSH hierarchy. The error bar represents the deviation of this ratio as we shrink the window size ($\mathfrak{w}$).