Mapping theme trends and knowledge structures for human neural stem cells:a quantitative and co-word biclustering analysis for the 2013-2018 period

Neural stem cells,which are capable of multi-potential differentiation and self-renewal,have recently been shown to have clinical potential for repairing central nervous system tissue damage.However,the theme trends and knowledge structures for human neural stem cells have not yet been studied bibliometrically.In this study,we retrieved 2742 articles from the PubMed database from 2013 to 2018 using "Neural Stem Cells" as the retrieval word.Co-word analysis was conducted to statistically quantify the characteristics and popular themes of human neural stem cell-related studies.Bibliographic data matrices were generated with the Bibliographic Item Co-Occurrence Matrix Builder.We identified 78 high-frequency Medical Subject Heading(MeSH)terms.A visual matrix was built with the repeated bisection method in gCLUTO software.A social network analysis network was generated with Ucinet 6.0 software and GraphPad Prism 5 software.The analyses demonstrated that in the 6-year period,hot topics were clustered into five categories.As suggested by the constructed strategic diagram,studies related to cytology and physiology were well-developed,whereas those related to neural stem cell applications,tissue engineering,metabolism and cell signaling,and neural stem cell pathology and virology remained immature.Neural stem cell therapy for stroke and Parkinson’s disease,the genetics of microRNAs and brain neoplasms,as well as neuroprotective agents,Zika virus,Notch receptor,neural crest and embryonic stem cells were identified as emerging hot spots.These undeveloped themes and popular topics are potential points of focus for new studies on human neural stem cells.

Co-word clustering analysis for nursing safety management research focuses by PubMed

Objective： To analyze hot research areas and the present research status of nursing safety management in PubMed. Methods： PubMed was searched using ＂safety management＂ for the literature on nursing safety management. BICOMB 2.0 and SPSS 20.0 software were used to analyze high-frequency keywords and conduct co-word clustering analysis. Results： We searched for totally 2353 articles related to our topic and extracted 19 high-frequency keywords （27.50%）. Five research focuses were concluded, including： study on nursing safety culture; team work to promote nursing safety; practice of nursing safety management; workplace violence against nursing staffs; nursing safety and quality evaluation standard. Conclusion： Analysis of the hotspots of nursing safety management in the past 10 years will contribute to understanding the research emphases and trend of development, and provide reference for the study and practice of nursing safety management.

The hot issues of studies in China on digital information resources: Based on co-word analysis

With the SPSS and the help of factor method and hierarchical clustered method,journal articles on digital information resources(DIR) from CNKI in the past ten years are analyzed with a co-word analytical method in this paper. The hot issues of studies on DIR and the relationship between those subjects are analyzed in this investigation as well.

Research status and hotspots of economic evaluation in nursing by co-word clustering analysis

Objective:The aim of this study is to discover research status and hotspots of economic evaluation(EE)in nursing area using co-word cluster analysis.Methods:Medical Subject Heading(MeSH)term“cost–benefit analysis”was searched in PubMed and nursing journals were limited by the function of filter.The information of author,country,year,journal,and keywords of collected paper was extracted and exported to Bicomb 2.0 system,where high-frequency terms and other data could be further mined.SPSS 19.0 was used for cluster analysis to generate dendrogram.Results:In all,3,020 articles were found and 10,573 MeSH terms were detected;among them,1,909 were MeSH major topics and generated 42 high-frequency terms.The consequence of dendrogram showed seven clusters,representing seven research hotspots:skin administration,infection prevention,education program,nurse education and management,EE research,neoplasm patient,and extension of nurse function.Conclusions:Nursing EE research involved multiple aspects in nursing area,which is an important indicator for decision-making.Although the number of papers is increasing,the quality of study is not promising.Therefore,further study may be required to detect nurses’knowledge of economic analysis method and their attitude to apply it into nursing research.More nursing economics course could carry out in nursing school or hospitals.

Bibliometric analysis of subject trends and knowledge structures of gut microbiota

BACKGROUND Gut microbiota is an emerging field of research,with related research having breakthrough development in the past 15 years.Bibliometric analysis can be applied to analyze the evolutionary trends and emerging hotspots in this field.AIM To study the subject trends and knowledge structures of gut microbiota related research fields from 2004 to 2018.METHODS The literature data on gut microbiota were identified and downloaded from the PubMed database.Through biclustering analysis,strategic diagrams,and social network analysis diagrams,the main trend and knowledge structure of research fields concerning gut microbiota were analyzed to obtain and compare the research hotspots in each period.RESULTS According to the strategic coordinates and social relationship network map,Clostridium Infections/microbiology,Clostridium Infections/therapy,RNA,Ribosomal,16S/genetics,Microbiota/genetics,Microbiota/immunology,Dysbiosis/immunology,Infla-mmation/immunology,Fecal Microbiota Transplantation/methods,Fecal Microbiota Transplantation can be used as an emerging research hotspot in the past 5 years(2014-2018).CONCLUSION Some subjects were not yet fully studied according to the strategic coordinates;and the emerging hotspots in the social network map can be considered as directions of future research.

Content analysis of documents using neural networks: A study of Antarctic science research articles published in international journals

Content analysis of scientific papers emanating from Antarctic science research during the 25 years period （1980-- 2004） has been carried out using neural network based algorithm-CATPAC. A total of 10 942 research articles published in Science Citation Indexed （SCI） journals were used for the study. Normalized co-word matrix from 35 most-used significant words was used to study the semantic association between the words. Structural Equivalence blocks were constructed from these 35 most-used words. Four-block model solution was found to be optimum. The density table was dichotomized using the mean density of the table to derive the binary matrix, which was used to construct the network map. Network maps represent the thematic character of the blocks. The blocks showed preferred connection in establishing semantic relationship with the blocks, characterizing thematic composition of Antarctic science research. The analysis has provided an analytical framework for carrying out studies on the con- tent of scientific articles. The paper has shown the utility of co-word analysis in highlighting the important areas of research in Antarctic science.

A Study on the Focuses and Fronts of International Second Language Acquisition Researches -Statistical Analysis of the Articles Published in Language Learning(2012-2016)

Keywords word frequency and co-word analysis are adopted in an attempt to to investigate the focuses and fronts of inter-national second language acquisition(SLA) researches by analyzing the key words in articles published in the internationally re-nowned SLA academic journal Language Learning from 2012 to 2016. It is found that the best researched topics in SLA are vocabu-lary acquisition, explicit knowledge, form-focused teaching, language use, and so on, among which learner's language attracts themost attention. In terms of research methods, they become more diversified and interdisciplinary, as empirical studies take a domi-nant position and experiments still play a leading role, displaying an interdisciplinary feature.

我国献血护理研究的热点主题和趋势——基于CNKI数据库的文献计量学和可视化分析

目的分析我国献血护理研究的现状,探讨未来研究热点趋势。方法以“献血”和“护理”为主题词在CNKI数据库检索发表的相关文献,应用书目共现分析系统BICOMB 2.0软件提取全部文献的主要主题词、发表年份、发表期刊、作者、作者单位等信息,用gCLUTO软件进行高频主题词的双聚类分析,使用Netdraw软件进行主题词社会网络分析。结果纳入文献1269篇,其中最早的文献发表于1980年,此后发文量整体呈现递增趋势,2019年达到最高峰的109篇,之后有所回落。发文量最多的期刊为《中国医药指南》(80篇),发文量最多的作者为江苏省盐城市中心血站的严书梅(11篇),发文量最多的机构为江苏省盐城市中心血站(39篇)。共选取63个高频主题词,双聚类为7类,结合双聚类分析和可视化网络图,可见我国献血护理研究热点主要集中于无偿献血、献血不良反应、机采血小板、心理护理等方面。结论运用文献计量学和可视化分析可以揭示我国献血护理研究的现状,预测未来研究热点。

AN IMPROVED BICLUSTERING ALGORITHM AND ITS APPLICATION TO GENE EXPRESSION SPECTRUM ANALYSIS

Cheng and Church algorithm is an important approach in biclustering algorithms. In this paper, the process of the extended space in the second stage of Cheng and Church algorithm is improved and the selections of two important parameters are discussed. The results of the improved algorithm used in the gene expression spectrum analysis show that, compared with Cheng and Church algorithm, the quality of clustering results is enhanced obviously, the mining expression models are better, and the data possess a strong consistency with fluctuation on the condition while the computational time does not increase significantly.

Big data in healthcare:Conceptual network structure,key challenges and opportunities

Big data is a concept that deals with large or complex data sets by using data analysis tools(e.g.,data mining,machine learning)to analyze information extracted from several sources systematically.Big data has attracted wide attention from academia,for example,in supporting patients and health professionals by improving the accuracy of decision-making,diagnosis and disease prediction.This research aimed to perform a Bibliometric Performance and Network Analysis(BPNA)supported by a Scoping Review(SR)to depict the strategic themes,thematic evolution structure,main challenges and opportunities related to the concept of big data applied in the healthcare sector.With this goal in mind,4857 documents from the Web of Science covering the period between 2009 to June 2020 were analyzed with the support of SciMAT software.The bibliometric performance showed the number of publications and citations over time,scientific productivity and the geographic distribution of publications and research fields.The strategic diagram yielded 20 clusters and their relative importance in terms of centrality and density.The thematic evolution structure presented the most important themes and how it changes over time.Lastly,we presented the main challenges and future opportunities of big data in healthcare.

DFCluster:An efficient algorithm to mine maximal differential biclusters for single pilot operations task synthesis safety analysis

With the continuous advancement of the avionics system,crew members are correspondingly reduced,and Single Pilot Operations(SPO)has attracted widespread attention from scholars.To meet the flight requirements in SPO mode,it is necessary to further strengthen air-ground coordination system integration,but at the same time,there will be some safety issues caused by resource integration,function fusion,and task synthesis.Aimed at the safety problems caused by task synthesis,an efficient differential bicluster mining algorithm--DFCluster algorithm is proposed in this paper to discover potential hazardous elements or propagation mechanisms through mining the resource-function matrixes.To mine efficiently,several pruning techniques are designed for generating maximal biclusters without candidate maintenance.The experimental results show that the DFCluster algorithm is more efficient than the existing differential biclustering algorithms under different scales of artificial datasets and public datasets.Then,a typical flight scenario is designed based on SPO air-ground collaborative system architecture,and combined with our proposed DFCluster algorithm for task synthesis safety analysis.Based on the mining results,the SPO airground collaborative system architecture is modified,which ultimately improves the safety of the SPO system.

基于自然指数刊文数据对全球医学研究领域热点的探析

自然指数从2014年提出以来,针对既有指标开展的研究如火如荼,但基于其基础数据的科学计量学分析相对匮乏。本文选择全球医学研究领域自然指数期刊为研究对象展开科学计量分析。基于共词分析和社会复杂网络理论,利用科学计量工具BICOMB及Ucinet分析近两年间发文热点的社会网络结构;同时,利用gCLUTO软件对其词篇矩阵进行双向聚类,探索其研究前沿结构构成。研究揭示了全球医学研究领域的热点前沿,为该领域发展方向提供参考,并分析自然指数在评估科研时的优缺点。

利用共词分析挖掘国际护理信息学研究热点

对PubMed收录的护理信息学领域的文献进行共词双聚类分析,探究该领域研究热点,绘制战略坐标图,展示这些研究热点的发展现状和趋势,为该领域研究人员了解护理信息学现状以及开展相关研究提供参考。

基于文献计量学的2006-2015年国际康复护理研究热点分析

目的 :分析国际康复护理研究热点并探讨相关趋势。方法 :应用双向聚类的文献计量学对2006-2015年Pub Med数据库中收录的康复护理文献进行分析。结果 :2006-2010年的研究热点主要集中在居家康复护理,康复过程中护士角色和患者体验等6个方面;2011-2015年的研究热点主要集中在康复过程中护士职业安全教育、患者生活质量、跌倒的预防等5个方面。结论 :患者康复过程中的安全问题、生活质量、功能恢复、延伸护理等方面有进一步的发展空间,是国内未来开展进一步研究的方向。

全球大数据与健康管理的研究热点聚类分析

揭示全球大数据与健康管理的研究热点。采用文献计量学和双向聚类分析法。发现全球大数据与健康管理现已达到年均发文量1 000篇以上;全球有89个国家和地区都进行了该方面的研究,其中欧洲地区的国家合作交流频繁;该领域中重要出版物有Stud Health Technol Inform、Plo S one等;目前研究热点主要聚焦为蛋白质等生物大分子网络作用的信息挖掘、数据挖掘在药物数据库及电子健康档案的应用、基因组序列数据挖掘在疾病预测中的应用、药物生物信息学的数据挖掘、生物医学大型数据库的数据挖掘、系统生物学的数据挖掘和医疗卫生服务中的数据挖掘等7个方面。

基于双向聚类方法的中医治疗中风病方剂配伍规律知识发现

目的深入探索方剂的制方要素,总结方药证治规律,找到对其中药理起关键作用的核心药/药组,为方剂配伍理论研究、药性理论研究、中医临床用药研究提供新方法和现代技术手段。方法从《中国方剂数据库》和《方剂现代应用数据库》中获取治疗中风病的方药文本数据源,经预处理、信息抽取后构建方药矩阵,引入双向聚类方法对方-药两方面数据同时聚类。结果预处理后得到有效记录648首,共计6913味药,生成了100×648"方-药"矩阵,最后得到4组聚类指标,高描述度代表方共8首,总结出针对不同证型配伍用药特点及各证型辨证论治的治则治法及各型常用药对药组。结论本方法适用于中医方剂数据库中方-药知识发现,可有效抽取所需方药信息,梳理出方药证治规律。

利用双聚类和突发检测算法探测学科前沿及知识基础的比较分析——以h指数研究领域为例

通过对双聚类算法和突发检测算法得到的研究前沿和知识基础的比较,总结其不同思路及优缺点,以便在今后的研究中扬长避短,更好的发挥各算法的作用。以h指数研究领域为例,分别对数据进行双聚类分析和突发检测分析,获取h指数研究领域知识基础和学科前沿。最后比较两种方法得到的结果并进行评价。在探测h指数研究领域知识基础方面,双聚类对高被引文献的聚类更为结构化和清晰。在识别h指数研究领域动态发展,突发检测算法表现出了优越性。本研究为以后学科前沿和知识基础的探测提供了有益参考,并提出观测科学活动特征的新角度。

精准医学研究热点的双向聚类计量分析

精准医学是医学研究的热点,和生物基因组学、基因治疗、个性化治疗以及转化医学有着紧密的联系,是在基础生物医学和临床医学两大领域上通过建立实际的转化研究和紧密的接轨机制而实现的新的医疗理念和模式。本文采用双向聚类的文献计量分析方法,对美国医学文摘Pubmed收录的相关精准医学方面的文献进行分析,并采用信息分析可视化方法揭示学科研究热点,以期从文献分析角度把握国际精准医学的研究模式及其主要研究内容,为国内开展精准医学研究提供借鉴。

双向聚类方法的文献计量分析

目的:拓展双向聚类方法在中医药领域数据分析中的应用,使数据分析研究人员掌握双向聚类方法的研究动态和潜在应用价值。方法:本文通过描述统计分析方法和文献同引网络分析方法等方法来研究双向聚类方法的发展趋势、热点研究领域和未来的发展方向。结果:双向聚类方法的研究热点主要集中在算法研究、生物信息学数据分析、信息检索和文本挖掘等方面。结论:结合双向聚类方法与中医药数据分析的特点,双向聚类方法能用来识别具有类似症状群的患者人群的特征,探索症状分类与患者特征的关系,探索药物配伍特点、实现精细化治疗,探索量效关系、实现动态和科学合理用药,探索组学研究与中医症候、中医治疗方案的关系等研究中。

转化医学信息学研究热点双向聚类计量分析

采用双向聚类文献计量分析方法,对PubMed收录的转化医学信息学相关文献进行分析,根据实际聚类效果将主题词聚为7类并生成可视化图,揭示学科研究热点,以期从文献分析角度把握国际转化医学信息学研究模式及其主要研究内容,为国内开展转化医学信息学研究提供借鉴。