Abstract
The co-occurrence analysis of medical subject heading (MeSH) terms in the bibliographic database is used in both bibliometrics and text mining fields. Because MeSH itself contains a hierarchical structure and MeSH descriptors represent different semantic types (i.e., disease, chemicals), the biclustering analysis of distinct semantic MeSH terms may be a novel approach for knowledge discovery. This study aimed to bicluster high-frequency MeSH terms based on their co-occurrence of distinct semantic types in a MeSH tree, so as to represent the structure (or status) of a scientific topic more specifically. The study was mainly comprised of four parts: construction of a MeSH term co-occurrence matrix of distinct semantic types, the biclustering algorithm, case study, and comparison. The first three parts were completed using R and the gCLUTO software. In the case study section, more specific knowledge models about the techniques and corresponding applications were discovered, proving that the method proposed in this study was valid and could be used in the universe of knowledge discovery.
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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Li Fang and Xiaobei Zhou. The first draft of the manuscript was written by Li Fang, and all authors commented on the previous versions of the manuscript. All authors read and approved the final manuscript. We thank International Science Editing (http://www.internationalscienceediting.com) for editing this manuscript.
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Fang, L., Zhou, X. & Cui, L. Biclustering high-frequency MeSH terms based on the co-occurrence of distinct semantic types in a MeSH tree. Scientometrics 124, 1179–1190 (2020). https://doi.org/10.1007/s11192-020-03496-4
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DOI: https://doi.org/10.1007/s11192-020-03496-4