1. Introduction
Scientific literature classification is essential for organizing biomedical knowledge and evaluating research trends. The Triangle of Biomedicine (TB) offers a geometric representation of how publications are distributed across human, animal, and molecular-cellular research domains. This framework supports translational medicine by visually mapping the focus and trajectory of biomedical studies. Yet, the integration of citation-based analysis with TB classification presents a novel opportunity to enhance understanding of research dynamics.
2. Methodology for Citation Vector Generation
To determine the evolving position of biomedical articles in the TB, this study introduces a method for generating citation vectors based on MeSH (Medical Subject Headings) term distributions. These vectors are calculated using the metadata of directly cited articles in PubMed, quantifying the proportion of citations within human, animal, and molecular-cellular domains. This approach enables researchers to track the translational movement of an article's influence through its citations.
3. Mapping Citation Dynamics in the Triangle of Biomedicine
The TB is used not only to map the initial categorization of a biomedical paper but also to assess the shift in its disciplinary influence over time through citations. By analyzing the citation vectors, researchers can determine if a paper originally focused on molecular research, for instance, later impacts human studies. This dynamic positioning offers deeper insights into the translational value and interdisciplinary nature of biomedical publications.
4. Translational Distance and the Human-Animal-Molecular Continuum
Citation vector analysis also enables the measurement of translational distance—a conceptual metric reflecting how far an article travels from its original research domain toward others. This is particularly useful in evaluating the extent to which molecular or animal studies contribute to human-centered biomedical advancements, thereby providing evidence for the real-world impact of foundational research.
5. Information Entropy as a Measure of Citation Diversity
To complement the citation vector analysis, information entropy is applied to quantify the diversity and spread of MeSH terms in the citation networks of different article sets. High entropy indicates broader interdisciplinary influence, while low entropy suggests a concentrated impact within a specific domain. Studying entropy dynamics offers a novel metric for understanding the translational consistency or evolution of research contributions.
6. Implications for Research Evaluation and Policy
This multidimensional approach to analyzing biomedical literature has practical implications for science policy, funding allocation, and translational medicine. By identifying articles with wide-ranging citation vectors and high entropy, stakeholders can better assess the real-world applicability of research. Furthermore, this method provides a framework for evaluating how different fields contribute to human health outcomes, aiding strategic decision-making in research development.
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