In order to study and analyze the effect of government funding on the promotion of scientific research in the field of medicine and to help the government manage research funds more rationally, this study proposes a framework for analyzing the relationship between entities in the field of medicine and funds. The framework consists of four parts: biomedical abstracts acquisition, NIH funding information acquisition and biomedical entity extraction; Development trend analysis of biomedical entity; Analysis of the most funded entities; Analysis of the relationship between entity research popularity and government funding. The results of preliminary analysis are as follows: the field of genetic research is in a period of rapid development, while the field of species research is in a “flat period”; Disease research catch NIH's continuous attention; the stimulating effect of government funding on the research popularity is decreasing, which is affected by various factors.
• Applied computing → Bioinformatics • Applied computing → Computing in government • Information systems → Information retrieval
Entitymetrics,
By 2019, the total number of literatures in PubMed, the
database of biomedical papers, has reached 29 million [
This paper proposes a framework for analyzing the relationship between biomedical entities and funds, as shown in Figure 1. Copyright 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
In Figure 1, the analysis framework can be divided into four
main modules: data acquisition and entity extraction;
Development trend analysis of biomedical entity; Analysis of the
most funded entities; Analysis of the relationship between entity
research popularity and government funding.
1. Data acquisition and entity extraction. Obtaining biomedical
data from PubMed between 1988 and 2017, obtaining funding
information and relevant research papers of project outputs from
NIH funding database, and biomedical entity extraction based on
BERN [
Species 84,203
Disease 36,704 Gene/Protein 25,489
Drug/Chemical 134,574 3. Analysis of the most funded entities. Combined with the biomedical entity data, the entities mentioned in the NIH project output articles are extracted to count the amount of funding for the entities. We define the funding for an entity as the sum of the funding for all articles in which the entity appears. 4. Analysis of the relationship between entity research popularity and government funding. We define the entity research popularity as the number of papers in which the entity is occurred. Thus, the annual number of four types of entities is counted according to the year of research paper in which the entity is located. The years 1988, 1998, 2008, and 2017 are selected, with the entity's research popularity as the vertical axis, and the entity's annual funding amount calculated by step 3 as the horizontal axis to create scatter plots. 3 3.1
Based on the change of the number of research entities of each type, the development trend of biomedical fields in the past three decades is analyzed. The number of entities studied in each year is the number of biomedical entity types mentioned in all papers published in that year. Figure 2 shows the number of research entities for each type over time. From the perspective of development trend, the number of gene/protein entities is rising the fastest and is in the stage of rapid development. The research on species entities is in the flat stage and is less numerous. 13 14
Entity ID 15
Based on biomedical entities in the four fields (Species, disease, gene/protein and drug/chemical), the years 1988, 1998, 2008 and 2017 are selected for scatter plotting, and the relationship between entity's research popularity and government funding is visually analyzed, to identify the driving effect of the fund on research in each field from the entity's perspective.
As shown in Figure 3, in 1988, a small increase in funding is followed by a significant increase in research popularity. In the following three years, the linear fit reveals that with the passage of time and the increase of the funding amount, the stimulating effect of funding amounts on the popularity of species research slows down.
As shown in Figure 4, The linear coefficient obtained by fitting the linear trend of disease entities in four years is slightly larger than that obtained by species entities. Like the species entity, in 1988, a small increase in funding is followed by a significant increase in research popularity. As the years go by, the increase in funding amount is greater than the increase in research popularity, the slope of the fitted line gradually decreases, which means the stimulating effect of the funding amount on the research popularity is gradually slowing down. In 2017, there are more entities with high funding and low research popularity, which of course could be related to the emergence of new types of entities. As shown in Figure 5, for gene/protein entities, the initial trend in 1988 is similar to the first two (species entity and disease entity), while later, especially in 2017, it is clear that the upper limit of research popularity has declined over time. As shown in
Figure 6, the presentation pattern drug/chemical entities is similar to that of gene/protein entities. The upper limit of research popularity has declined over time, which indicates that as the years go by, the amount of funding does not play a significant role in the drug/chemical entity's effect on research popularity anymore.
The above analysis follows that the influencing factors of the change of entity research popularity should be multi-faceted and complex, rather than simply being linearly influenced by research funding, and the complexity increases with the increase of years. 4 4.1
Studies linking entities to government funding and exploration of trends from an entity perspective barely visible as far as we know. This study puts forward a preliminary research idea, applying the idea of entitymetrics to biomedical field from the perspective of scientific research funds, and carries out a preliminary research trend exploration and knowledge discovery. The conclusions are as follows: a) the field of genetic research is in a period of rapid development, while the field of species research is in a “flat period”; b) Disease research catch NIH’s continuous attention; c) the stimulating effect of government funding on the research popularity is decreasing, which is affected by various factors. These findings provide the basis for a followup study. 4.2
Inspired by the initial results, our future work will focus on a more in-depth exploration of the relationship between government funding and entity development. In this study, we summarized the Fang Tan et al. trends in four categories of entities in the biomedical field and counted the entities that received the highest funding. However, Is there any commonality among these entities? Is entity-related research with any certain characteristics always more likely to be funded by the government? In addition, current research shows that the incentive effect of increased government funding on research in various fields is decreasing, while the impact of other factors such as the continuity of government funding on research popularity has not been explored. Therefore, further research will be conducted on the study of the characteristics of the funded entities and the rules of government funding.
We acknowledge the editors and the anonymous reviewers for insightful suggestions on this work.