Analyzing the Chinese landscape in anti-diabetic drug research: leading knowledge production institutions and thematic communities.
Analyzing the Chinese landscape in anti-diabetic drug research: leading knowledge production institutions and thematic communities.
复制标题
在抗糖尿病药物研究中分析中国景观：领先的知识生产机构和主题社区。
DOI：
10.1186/s13020-016-0084-y
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发表时间：
2016
期刊：
影响因子：
4.9
通讯作者：
Hu Y
中科院分区：
文献类型：
--
作者：
Deng J;Sitou K;Zhang Y;Yan R;Hu Y
抗糖尿病药物的发现是一个活跃的中医药研究领域。本研究旨在利用基于学术出版物共同作者的网络系统方法来规划中国的抗糖尿病药物研究。我们专注于识别领先的知识生产机构，分析它们之间的相互作用，发现具有高度内部关联的社区，并探索未来的研究方向。从科学引文索引扩展数据库中检索2009-2013年主题“糖尿病”和主题类别“药理学与药学”下发表的目标文章，其中作者地址字段中包含“中国”、“台湾”、“香港”或“澳门”（或“澳门”）及其书目信息（例如文章标题、作者、关键词和作者单位） 地址）分析。采用社交网络方法构建基于共同出版物的机构协作网络。 Gephi 软件用于可视化网络，并使用中心性测量来分析机构之间的关系。基于文章关键词和Rsc值的主题分析揭示了网络社区的研究热点和方向。确定了前50名机构；其中包括上海交通大学、国立台湾大学、北京大学和中国药科大学。台湾地区的机构倾向于与台湾以外的机构合作，但大陆地区的机构对外合作的兴趣较低。采用Louvain算法检测出14个主题社区，并进一步标记其高频特征关键词，如中药、糖尿病并发症、氧化应激、药代动力学、胰岛素抵抗等。关键词“中药”包含一系列与中药相关的主题，包括小檗碱、黄酮类化合物、黄芪多糖、大黄素和人参皂苷。这些关键词表明了进一步抗糖尿病药物研究的潜在领域。程度中心性与非核心关键词的 Rsc 值之间的相关性为 -0.641（P = 0.013），这表明专注于稀有研究领域的社区通常被其他人孤立，合作的机会较低。通过更好地了解中国抗糖尿病药物研究的现状，在特定研究领域寻找专家和机构的研究人员和学者可以快速发现他们的目标群体，然后选择最合适的潜在合作者，并为未来的研究建议优先研究方向。本文的在线版本 (doi:10.1186/s13020-016-0084-y) 包含补充材料，可供授权用户使用。
The discovery of anti-diabetic drugs is an active Chinese medicine research area. This study aims to map out anti-diabetic drug research in China using a network-based systemic approach based on co-authorship of academic publications. We focused on identifying leading knowledge production institutions, analyzing interactions among them, detecting communities with high internal associations, and exploring future research directions. Target articles published in 2009–2013 under the topic “diabetes” and subject category “pharmacology & pharmacy,” with “China,” “Taiwan,” “Hong Kong,” or “Macao” (or “Macau”) in the authors’ address field were retrieved from the science citation index expanded database and their bibliographic information (e.g., article title, authors, keywords, and authors’ affiliation addresses) analyzed. A social network approach was used to construct an institutional collaboration network based on co-publications. Gephi software was used to visualize the network and relationships among institutes were analyzed using centrality measurements. Thematic analysis based on article keywords and Rsc value was applied to reveal the research hotspots and directions of network communities. The top 50 institutions were identified; these included Shanghai Jiao Tong University, National Taiwan University, Peking University, and China Pharmaceutical University. Institutes from Taiwan tended to cooperate with institutes outside Taiwan, but those from mainland China showed low interest in external collaboration. Fourteen thematic communities were detected with the Louvain algorithm and further labeled by their high-frequency and characteristic keywords, such as Chinese medicines, diabetic complications, oxidative stress, pharmacokinetics, and insulin resistance. The keyword Chinese medicines comprised a range of Chinese medicine-related topics, including berberine, flavonoids, Astragaluspolysaccharide, emodin, and ginsenoside. These keywords suggest potential fields for further anti-diabetic drug research. The correlation of −0.641 (P = 0.013) between degree centrality and the Rsc value of non-core keywords indicates that communities concentrating on rare research fields are usually isolated by others and have a lower chance of collaboration. With a better understanding of the Chinese landscape in anti-diabetic drug research, researchers and scholars looking for experts and institutions in a specific research area can rapidly spot their target community, then select the most appropriate potential collaborator and suggest preferential research directions for future studies. The online version of this article (doi:10.1186/s13020-016-0084-y) contains supplementary material, which is available to authorized users.