UNSTRUCTURED
The aim of this study was to identify the intellectual structure and evolutionary trends of precision medicine (PM) research through the application of various social network analysis and visualization methods. The bibliographies of papers published between 2008 and 2017 were extracted from the Web of Science database. Based on the statistics of keywords in papers, a co-word network was generated and used to calculate network indicators of the entire network and local networks. Communities were then detected to identify sub-directions of PM research. Topological maps of networks, including networks between communities and within each community, were drawn to reveal the correlation structure. An evolutionary graph and a strategic graph were finally produced to reveal research venation and trends in discipline communities. The results show that PM research involves very broad themes and is not balanced overall. A minority of themes with high-frequency and network indicators, such as biomarkers, cancer, diagnostics, drugs, genetics, genomics, pharmacogenetics, pharmacogenomics, prediction, target therapy, and therapy, can be considered the core areas in PM research. However, there are five balanced theme directions with distinguished statuses and tendencies: cancer, genomics, biomarkers, pharmacogenomics, and imaging. Cancer, biomarkers, and pharmacogenomics are indicated as the main focused and well-developed branches. Genomics and imaging are shown to be isolated and undeveloped. However, some emerging themes, such as asthma, Parkinson’s disease, and melanoma, were also discovered indicating a new promising application area for a PM model. The implications of PM in the study will provide reasonable and effective support for researchers, funders, policy makers and clinicians.