Lyu Xiaoguang, Hu Jiming, Dong Weiguo, Xu Xin
The Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.
School of Information Management, Wuhan University, Wuhan, China.
JMIR Med Inform. 2020 Feb 4;8(2):e11287. doi: 10.2196/11287.
Precision medicine (PM) is playing a more and more important role in clinical practice. In recent years, the scale of PM research has been growing rapidly. Many reviews have been published to facilitate a better understanding of the status of PM research. However, there is still a lack of research on the intellectual structure in terms of topics.
This study aimed to identify the intellectual structure and evolutionary trends of PM research through the application of various social network analysis and visualization methods.
The bibliographies of papers published between 2009 and 2018 were extracted from the Web of Science database. Based on the statistics of keywords in the papers, a coword network was generated and used to calculate network indicators of both the entire network and local networks. Communities were then detected to identify subdirections 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 showed that PM research involves extensive themes and, overall, is not balanced. A minority of themes with a high frequency and network indicators, such as Biomarkers, Genomics, Cancer, Therapy, Genetics, Drug, Target Therapy, Pharmacogenomics, Pharmacogenetics, and Molecular, can be considered the core areas of PM research. However, there were five balanced theme directions with distinguished status and tendencies: Cancer, Biomarkers, Genomics, Drug, and Therapy. These were shown to be the main branches that were both focused and well developed. Therapy, though, was shown to be isolated and undeveloped.
The hotspots, structures, evolutions, and development trends of PM research in the past ten years were revealed using social network analysis and visualization. In general, PM research is unbalanced, but its subdirections are balanced. The clear evolutionary and developmental trend indicates that PM research has matured in recent years. The implications of this study involving PM research will provide reasonable and effective support for researchers, funders, policymakers, and clinicians.
精准医学（PM）在临床实践中发挥着越来越重要的作用。近年来，PM研究规模迅速增长。已发表了许多综述以促进对PM研究现状的更好理解。然而，在主题方面仍缺乏对知识结构的研究。
本研究旨在通过应用各种社会网络分析和可视化方法来识别PM研究的知识结构和演变趋势。
从Web of Science数据库中提取2009年至2018年发表的论文的参考文献。基于论文中关键词的统计，生成共词网络并用于计算整个网络和局部网络的网络指标。然后检测社群以识别PM研究的子方向。绘制网络拓扑图，包括社群之间和每个社群内部的网络，以揭示相关结构。最终生成进化图和战略图以揭示学科社群的研究脉络和趋势。
结果表明，PM研究涉及广泛的主题，总体而言并不平衡。少数高频且具有网络指标的主题，如生物标志物、基因组学、癌症、治疗、遗传学、药物、靶向治疗、药物基因组学、药物遗传学和分子，可以被视为PM研究的核心领域。然而，有五个地位和趋势显著的平衡主题方向：癌症、生物标志物、基因组学、药物和治疗。这些被证明是既受关注又发展良好的主要分支。不过，治疗被证明是孤立且未得到充分发展的。
利用社会网络分析和可视化揭示了过去十年中PM研究的热点、结构、演变和发展趋势。总体而言，PM研究是不平衡的，但其子方向是平衡的。清晰的演变和发展趋势表明PM研究近年来已经成熟。本研究对PM研究的启示将为研究人员、资助者、政策制定者和临床医生提供合理有效的支持。
