Identifying Biomedical Research Frontiers via Knowledge Flow: A Self-Supervised Graph Clustering Framework

Corresponding author: TANG Xiaoli, tang.xiaoli@imicams.ac.cn

DOI: 10.12201/bmr.202605.00070

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  Abstract: Purpose/Significance Driven by the exponential growth of biomedical information and the increasing heterogeneity of knowledge sources, conventional frontier identification techniques are struggling to maintain efficacy. This study develops a computational framework designed to quantify knowledge spillover pathways between science and technology, aiming to uncover the latent associations and evolutionary dynamics bridging basic research discoveries and technological applications.This framework offers instrumental decision support for optimizing research topic selection and R&D strategic planning.Method/Process This study develops an identification framework fusing implicit semantics and heterogeneous topological linkages. The process begins with the construction of a bipartite knowledge flow network spanning papers and patents. Leveraging PubMedBERT for semantic encoding and K-nearest neighbor for edge augmentation, we establish a dense semantic-topological structure. Subsequently, a tri-modal gated encoder is introduced to adaptively integrate nodes textual, temporal, and structural attributes into a unified representation. The framework utilizes a self-supervised joint optimization strategy to concurrently optimize node representations and community detection. Ultimately, research frontiers with significant translational potential are identified using a custom ‘Frontier Index derived from link prediction probabilities. Result/Conclusion Empirical results in the field of breast cancer demonstrate that the proposed framework effectively mitigates the sparsity issues inherent in traditional bipartite citation networks. It successfully identifies three archetypal frontier communities: Theoretical Burst, Industrial Maturation, and Sci-Tech Resonance. Visualization analysis further elucidates the latent knowledge flows emanating from basic research clusters (e.g., molecular subtyping of triple-negative breast cancer) toward applied research sectors (e.g., targeted combination therapies and AI-assisted imaging diagnosis). These findings suggest that the framework excels in semantic depth and proactive early-warning capabilities, serving as a robust computational tool for science and technology (S&T) intelligence analysis in the biomedical domain.

  Key words: identifying frontiers; knowledge flow; Graph Clustering; link prediction; breast cancer

  Submit time: 20 May 2026

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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  1	2026-03-18	10.12201/bmr.202605.00070V1	Download

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