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

Biclustering data analysis: a comprehensive survey

Biclustering, the simultaneous clustering of rows and columns of a data matrix, has proved its effectiveness in bioinformatics due to its capacity to produce local instead of global models, evolving from a key technique used in gene expression data analysis into one of the most used approaches for pattern discovery and identification of biological modules, used in both descriptive and predictive learning tasks. This survey presents a comprehensive overview of biclustering. It proposes an updated taxonomy for its fundamental components (bicluster, biclustering solution, biclustering algorithms, and evaluation measures) and applications. We unify scattered concepts in the literature with new definitions to accommodate the diversity of data types (such as tabular, network, and time series data) and the specificities of biological and biomedical data domains. We further propose a pipeline for biclustering data analysis and discuss practical aspects of incorporating biclustering in real-world applications. We highlight prominent application domains, particularly in bioinformatics, and identify typical biclusters to illustrate the analysis output. Moreover, we discuss important aspects to consider when choosing, applying, and evaluating a biclustering algorithm. We also relate biclustering with other data mining tasks (clustering, pattern mining, classification, triclustering, N-way clustering, and graph mining). Thus, it provides theoretical and practical guidance on biclustering data analysis, demonstrating its potential to uncover actionable insights from complex datasets.

Knowledge mapping of programmed cell death in osteonecrosis of femoral head: a bibliometric analysis (2000-2022)

BACKGROUND

Osteonecrosis of the femoral head (ONFH) is a common, refractory and disabling disease of orthopedic department, which is one of the common causes of hip pain and dysfunction. Recent studies have shown that much progress has been made in the research of programmed cell death (PCD) in ONFH. However, there is no bibliometric analysis in this research field. This study aims to provide a comprehensive overview of the knowledge structure and research hot spots of PCD in ONFH through bibliometrics.

METHOD

The literature search related to ONFH and PCD was conducted on the Web of Science Core Collection (WoSCC) database from 2002 to 2021. The VOSviewers, "bibliometrix" R package and CiteSpace were used to conduct this bibliometric analysis.

RESULTS

In total, 346 articles from 27 countries led by China and USA and Japan were included. The number of publications related to PCD in ONFH is increasing year by year. Shanghai Jiao Tong University, Xi An Jiao Tong University, Wuhan University and Huazhong University of Science and Technology are the main research institutions. Molecular Medicine Reports is the most popular journal in the field of PCD in ONFH, and Clinical Orthopaedics and Related Research is the most cocited journal. These publications come from 1882 authors among which Peng Hao, Sun Wei, Zhang Chang-Qing, Zhang Jian and Wang Kun-zheng had published the most papers and Ronald S Weinstein was cocited most often. Apoptosis, osteonecrosis, osteonecrosis of the femoral head, glucocorticoid and femoral head appeared are the main topics the field of PCD in ONFH. Autophagy was most likely to be the current research hot spot for PCD in ONFH.

CONCLUSION

This is the first bibliometric study that comprehensively summarizes the research trends and developments of PCD in ONFH. This information identified recent research frontiers and hot directions, which will provide a reference for scholars studying PCD in ONFH.

Research hotspots and trends for axon regeneration (2000-2021): a bibliometric study and systematic review

BACKGROUND

Axons play an essential role in the connection of the nervous system with the rest of the body. Axon lesions often lead to permanent impairment of motor and cognitive functions and the interaction with the outside world. Studies focusing on axon regeneration have become a research field with considerable interest. The purpose of this study is to obtain an overall perspective of the research field of axonal regeneration and to assist the researchers and the funding agencies to better know the areas of greatest research opportunities.

METHODS

We conducted a bibliometric analysis and Latent Dirichlet Allocation (LDA) analysis of the global literature on axon regeneration based on the Web of Science (WoS) over the recent 22 years, to address the research hotspots, publication trends, and understudied areas.

RESULTS

A total of 21,018 articles were included, which in the recent two decades has increased by 125%. Among the top 12 hotspots, the annual productions rapidly increased in some topics, including axonal regeneration signaling pathway, axon guidance cues, neural circuits and functional recovery, nerve conduits, and cells transplant. Comparatively, the number of studies on axon regeneration inhibitors decreased. As for the topics focusing on nerve graft and transplantation, the annual number of papers tended to be relatively stable. Nevertheless, the underlying mechanisms of axon regrowth have not been completely uncovered. A lack of notable research on the epigenetic programs and noncoding RNAs regulation was observed. The significance of cell-type-specific data has been highlighted but with limited research working on that. Functional recovery from neuropathies also needs further studies.

CONCLUSION

The last two decades witnessed tremendous progress in the field of axon regeneration. There are still a lot of challenges to be tackled in translating these technologies into clinical practice.

A bibliometric research based on hotspots and frontier trends of denosumab

Denosumab is a monoclonal antibody that targets and inhibits the osteoclast activating factor receptor activator for nuclear factor-κB ligand (RANKL). It has been widely used in the treatment of osteoporosis, giant cell tumors of bone, and in the prevention of malignant skeletal-related events (SREs). We collected the research results and related MeSH terms of denosumab from 2011 to 2021 through the Web of Science and PubMed, respectively. The literature was visualized and analyzed by CiteSpace and bibliometric online analysis platforms. The MeSH terms were biclustered using the Bibliographic Co-Occurrence Analysis System (BICOMB) and graph clustering toolkit (gCLUTO). The results show that the number of denosumab-related annual publications had increased from 51 to 215, with the United States leading and Amgen Inc. being the most influential in the past 10 years. Articles published in the Journal of Bone and Mineral Research had the highest total citations. Three scholars from Shinshu University in Matsumoto, Yukio Nakamura, Takako Suzuki, and Hiroyuki Kato, joined the field relatively late but produced the most. The clinical comparison and combination of denosumab with other drugs in the treatment of osteoporosis was the most significant focus of research. Drug withdrawal rebound and management strategies have gained more attention and controversy recently. MeSH analysis revealed eight major categories of research hotspots. Among them, exploring the multiple roles of the RANK-RANKL-OPG system in tumor progression, metastasis, and other diseases is the potential direction of future mechanism research. It is a valuable surgical topic to optimize the perioperative drug administration strategy for internal spinal fixation and orthopedic prosthesis implantation. Taken together, the advantages of denosumab were broad and cost-effective. However, there were still problems such as osteonecrosis of the jaw, severe hypocalcemia, a high recurrence rate of giant cells in the treatment of bone and individual sarcoidosis, and atypical femoral fractures, which need to be adequately solved.

Global Research Status and Trends in Hair Follicle Stem Cells: a Bibliometric Analysis

BACKGROUND

Hair follicle stem cells (HFSCs) are derived from the bulge region and are important autologous stem cell sources. Bibliometric is a statistical method that quantitatively analyses the research papers concerned about one special topic. This study aims to estimate the research status and trends of HFSCs worldwide by bibliometric analyses.

METHODS

Data were obtained from the Web of Science by searching keywords related to HFSCs. Publication distributions stratified by countries/regions, institutions, journals, and authors were systematically assessed. The frequency of keywords was assessed, and bibliometric mapping was employed to describe the development of HFSC research.

RESULTS

A total of 458 publications that met our screening criteria were included in this study, consisting of 423 (92.4%) articles and 35 (7.6%) reviews. The United States of America (USA) ranked first in the number of publications at 146 (31.9%), followed by China at 130 (28.4%), which is consistent with the rank of the H-index. Author keywords were classified into three clusters, namely, basic study, applied study, and biomarker; average publication time of keywords in applied study cluster is later than basic study cluster. The keywords "bulge", "nestin", and "skin" are the top three most frequent keywords in basic studies; "differentiation", "proliferation", and "alopecia" are the top three most frequent keywords in applied studies. With respect to the latest research hotspots, "apoptosis" and "tissue engineering" are relatively new keywords.

CONCLUSIONS

The USA and China were the most productive countries for research on HFSCs. The focus of keywords gradually shifted from basic study to applied study. Research on the differentiation/proliferation of HFSCs and the role of HFSCs in alopecia have been recent research focuses. Apoptosis and tissue engineering are recommended as promising research hotspots. Our study provides profound insights into the research history, current status, and future trend of HFSCs.

Mapping theme trends and knowledge structure of Sjögren's syndrome (SS), a bibliometric analysis from 2010 to 2021

BACKGROUND

Sjögren's syndrome is an autoimmune disease with a complicated pathophysiology, and treatment strategies are in desperate need of improvement. In this research, we provide the literature topic trends and knowledge structure connected to Sjögren's syndrome from the perspective of bibliometrics for further research.

METHODS

We acquired 3753 publications from the PubMed database and 3880 catalogs from the Web of Science Core Collection, with a time limit of 2010 to 2021 and the search term as "Sjögren's syndrome." Using the VOSviewer and bibliographic item co-occurrence matrix generator, we conducted cocited analysis, extracted major MeSH terms, generated matrices and performed biclustering analysis, visual matrix, drawn strategy diagram, and social network analysis.

RESULTS

Through the clustering of the major MeSH terms, a total of five categories and 10 research topics were identified. Cocitation analysis suggests that diagnosis and clinical manifestations have been the main basis over the past 12 years and also are the mature content in the first quadrant of the strategy map. Ultrasonography, immunosuppressive agents and therapy, mechanism and therapeutic targets, and diagnostic autoantibodies are examples of the third quadrant, which wields great potential to progress. Genetic predisposition and psychology, in the fourth quadrant, need to be developed additionally. The subject keywords near the network's edge, such as "Salivary Glands, Minor/pathology," "Immunosuppressive Agents/therapeutic use," and "Autoantibodies/blood," are all growing research hotspots with much space, according to social network analysis.

CONCLUSION

Through bibliometric analysis of the literature related to Sjögren's syndrome in the past 12 years, we obtained the knowledge basis, undeveloped topics, and emerging hotspots, which can suggest recommendations for future research. Key Points • We disclosed the topic trends and knowledge structure of Sjögren's syndrome literature over the past 12 years. • Clinical manifestations and diagnosis of Sjögren syndrome are included in mature and comprehensive studies, whereas ultrasonography, immunosuppressants and treatments, mechanisms and therapeutic targets, and diagnostic autoantibodies are studies with high potential for advancement. Additional research in genetics and psychology is needed. • We identified potential future research hotspots, including "Salivary Glands, Minor/pathology," "Immunosuppressive Agents/therapeutic use," and "Autoantibodies/blood."

Investigating Research Hotspots and Publication Trends of Spinal Stenosis: A Bibliometric Analysis During 2000-2018

Spinal stenosis is a common disease affecting the elderly that is present in a various forms. Its high incidence forces researchers to pay more attention and offer countermeasures. We used the Web of Science Core collection and PubMed database to obtain 5,606 scientific studies concerning spinal stenosis, and the number of publications maintained a roughly increasing trend from 108 in 2000 to 512 in 2018, only declining in 2011. Bibliometric analysis was conducted using the online analysis software CiteSpace and Bibliographic Item Co-Occurrence Matrix Builder (BICOMB). The United States maintains academic leadership in this field. The journal SPINE was the most authoritative, with 695 articles and an average of 12.73 citations. The exported major MeSH terms were further biclustered with gCLUTO according to co-word analysis to reveal research hotspots, including etiology, pathogenesis, clinical manifestation, conservative treatment, operative indication, internal implantation, and postoperative complications. After combination, the main topics focused on pathogenesis and surgical treatment. Narrowing causes flavum ligamentum hypertrophy, and posterior longitudinal ligament ossification is widely accepted. Additionally, minimally invasive surgery and internal implantation fixation are more valid in the clinic. Refining pathological classification and optimizing surgical methods and instrument properties will be important future research directions for spinal stenosis.

The Sustainable Development of Financial Topic Detection and Trend Prediction by Data Mining

Blockchain technology is the most cutting-edge technology in the field of financial technology, which has attracted extensive attention from governments, financial institutions and investors of various countries. Blockchain and finance, as an interdisciplinary, cross-technology and cross-field topic, has certain limitations in both theory and application. Based on the bibliometrics data of Web of Science, this paper conducts data mining on 759 papers related to blockchain technology in the financial field by means of co-word analysis, bi-clustering algorithm and strategic coordinate analysis, so as to explore hot topics in this field and predict the future development trend. The experimental results found ten research topics in the field of blockchain combined with finance, including blockchain crowdfunding, Fintech, encryption currency, consensus mechanism, the Internet of Things, digital financial, medical insurance, supply chain finance, intelligent contract and financial innovation. Among them, blockchain crowdfunding, Fintech, encryption currency and supply chain finance are the key research directions in this research field. Finally, this paper also analyzes the opportunities and risks of blockchain development in the financial field and puts forward targeted suggestions for the government and financial institutions.

Knowledge Mapping of Dietary Factors of Metabolic Syndrome Research: Hotspots, Knowledge Structure, and Theme Trends

Background: The global incidence of metabolic syndrome (MetS) is continuously increasing, making it a potential worldwide public health concern. Research on dietary factors related to MetS has attracted considerable attention in the recent decades. However, the research hotspots, knowledge structure, and theme trends for the dietary factors associated with MetS remain unknown, and have not yet been systematically mapped. This study aimed to review the research status of diet as a risk factor for MetS through bibliometric methods. Bibliometric analysis was conducted using the Web of Science database. Research hotspots were identified using biclustering analysis with the gCLUTO software, and knowledge structure was explored via social network analysis using the Ucinet software. Theme trends were investigated using evolutionary analysis with the SciMAT software. In total, 1,305 papers were analyzed. The research output on the dietary factors associated with MetS increased steadily. The research scope was gradually expanding and diverse. Overall, eight research hot spots, four key dietary nodes, and four motor themes on the dietary factors associated with MetS were identified. Fatty acids, dietary fiber, and polyphenols have been the focus of research in this field over the years. Evolutionary analysis showed that fish oil and vitamin C were well-developed research foci recently. Prebiotics was recognized as an emerging theme with certain developmental potential. These findings provide a better understanding of the research status of the dietary factors associated with MetS and a reference for future investigations.

Collaboration Network and Trends of Global Coronavirus Disease Research: A Scientometric Analysis

As a global pandemic threatens health and livelihoods, finding effective treatments has become a vital issue that requires worldwide collaboration. This study examines research collaboration and network profiles through a case study of coronavirus diseases, including both the extinct severe acute respiratory syndrome coronavirus (SARS-CoV) and the emerging species (SARS-CoV-2). A scientometric process was designed to apply quantitative tools and a qualitative approach employing technological expertise to accomplish a three-level collaboration analysis. The text mining software, VantagePoint, was used to analyze research articles from the Web of Science database to identify the key national, organizational, and individual players in the coronavirus research field combined with indicators, namely, the breadth and depth of collaboration. The results show that China and the United States are at the center of coronavirus research networks at all three levels, including many endeavors involving single or joint entities. This study demonstrates how governments, public sectors, and private sectors, such as the pharmaceutical industry, can use scientometric analysis to gain insight into the holistic research trends and networks of players in this field, leading to the formulation of strategies to strengthen research and development programs. Furthermore, this approach can be utilized as a visualization and decision support tool for further policy planning, identification and execution of collaboration, and research exchange opportunities. This scientometric process should be directly applicable to other fields.

Bufalin: A Systematic Review of Research Hotspots and Antitumor Mechanisms by Text Mining and Bioinformatics

Bufalin is an anticancer drug extract from traditional Chinese medicine. Several articles about bufalin have been published. However, the literature on bufalin has not yet been systematically studied. This study aimed to identify the study status and knowledge structures of bufalin and to summarize the antitumor mechanism. Data were retrieved and downloaded from the PubMed database. The softwares of BICOMB, gCLUTO, Ucinet 6.0, and NetDraw2.084 were used to analyze these publications. The bufalin related genes were recognized and tagged by ABNER software. Then these BF-related genes were performed by Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis, and protein-protein interaction (PPI) network analysis. A total of 474 papers met the search criteria from 2000 to 2019. By biclustering clustering analysis, the 50 high-frequency main MeSH terms/subheadings were classified into 5 clusters. The clusters of drug therapy and the mechanism of bufalin were hotspot topics. A total of 50 genes were identified as BF-related genes. PPI network analysis showed that inducing apoptosis was the main effect of bufalin, and apoptosis-related gene Caspase 3 was the most reported by people. Bufalin could inhibit the proliferation, invasion, and metastasis of cancer cells through multiple signaling pathways, such as PI3K/AKT, Hedgehog, MAPK/JNK, Wnt/[Formula: see text]-catenin, TGF-[Formula: see text]/Smad, Integrin signaling pathway, and NF-KB signaling pathway via KEGG analysis. Through the quantitative analysis of bufalin literature, we revealed the research status and hot spots in this field and provided some guidance for further research.

Intellectual Structure and Evolutionary Trends of Precision Medicine Research: Coword Analysis

BACKGROUND

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.

OBJECTIVE

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.

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.

RESULTS

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.

CONCLUSIONS

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.