Investigating the integrated landscape of the intellectual topology of bioinformatics

Kim, Meen Chul; Jeong, Yoo Kyung

doi:10.1007/s11192-014-1417-1

Cited by 9 publications

(5 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Medical researchers have made increasing use of computer techniques to analyze large datasets of publications and citations in order to characterize and map the structure of scientific fields and their development over time (Jones et al 2011;Armfield et al 2014;Chen et al 2014a, b;Jiang et al 2015), even to model the dissemination of scientific ideas and identify characteristics of publication patterns that suggest whether an idea or innovation has reached a crucial phase transition (Yao et al 2014a, b;Winnink and Tijssen 2014;Kim et al 2014;Chida et al 2014;Cheng and Zhao 2014;Gøeg et al 2014;Ye et al 2014). Analysis of citation data helps define the field at issue by highlighting various areas of research and its future directions (Small 1981).…”

Section: Introductionmentioning

confidence: 99%

Visualizing the structure and the evolving of digital medicine: a scientometrics review

Fang

2015

Scientometrics

View full text Add to dashboard Cite

This article aims to analyze and visualize the structure and the emerging trend of digital medicine, a new medical pattern of twenty-first century. Our study objectively explores the document co-citation clusters of 6060 bibliographic records to identify the origin of digital medicine and the hot research specialty of domain. Pivotal point articles, prominent authors, active disciplines and institution have been identified by network analysis theory. CiteSpace was used to visualize the perspective of digital medicine domain. As an interdiscipline which integrated computer science, information engineering with medicine, digital medicine originally and mainly on digital medical imaging technology research for accuracy and speedy clinical diagnosis and therapy. Of 6060 relevant records reviewed, 1719 (28 %) are on radiology, 902 (15 %) are on engineering, 539 (9 %) are on computer science. The largest co-citation cluster is on digital tomosynthesis. The earliest cluster is on medical imaging segmentation and registration. Post-processing imaging technology, detector, phase contrast, reversible watermarking, input, model 3D reconstruct, real-time dynamic imaging, dosimetry have been the hot research topics. The recently cluster is on internet health information. Harvard University of USA is the prominent institution. The coverage of digital medicine research is widely from clinic to laboratory. Recent year, domain research front is thematically on teleradiology, telemedicine and hospital information management system.

show abstract

Section: Introductionmentioning

confidence: 99%

Visualizing the structure and the evolving of digital medicine: a scientometrics review

Fang

2015

Scientometrics

View full text Add to dashboard Cite

show abstract

“…Based on a network of multiple entities such as publication statistics, cited references, keywords and dual-map overlays, Kim et al (2016) explored the intellectual frontier of information and data visualization research. Kim et al (2014) examined the integrated intellectual topology of bioinformatics using co-authorship networks and co-citation networks of related bibliographic information. Lee et al (2015) attempted to jointly apply association rules, link prediction and topic model methods to predict the technology convergence patterns.…”

Section: Topic Analysis Researchmentioning

confidence: 99%

Topic analysis of academic disciplines based on prolific and authoritative researchers

Yang

Wang

2021

LHT

View full text Add to dashboard Cite

PurposeThe paper aims to explore whether topic analysis (identification of the core contents, trends and topic distribution in the target field) can be performed using a more low-cost and easily applicable method that relies on a small dataset, and how we can obtain this small dataset based on the features of the publications.Design/methodology/approachThe paper proposes a topic analysis method based on prolific and authoritative researchers (PARs). First, the authors identify PARs in a specific discipline by considering the number of publications and citations of authors. Based on the research publications of PARs (small dataset), the authors then construct a keyword co-occurrence network and perform a topic analysis. Finally, the authors compare the method with the traditional method.FindingsThe authors found that using a small dataset (only 6.47% of the complete dataset in our experiment) for topic analysis yields relatively high-quality and reliable results. The comparison analysis reveals that the proposed method is quite similar to the results of traditional large dataset analysis in terms of publication time distribution, research areas, core keywords and keyword network density.Research limitations/implicationsExpert opinions are needed in determining the parameters of PARs identification algorithm. The proposed method may neglect the publications of junior researchers and its biases should be discussed.Practical implicationsThis paper gives a practical way on how to implement disciplinary analysis based on a small dataset, and how to identify this dataset by proposing a PARs-based topic analysis method. The proposed method presents a useful view of the data based on PARs that can produce results comparable to traditional method, and thus will improve the effectiveness and cost of interdisciplinary topic analysis.Originality/valueThis paper proposes a PARs-based topic analysis method and verifies that topic analysis can be performed using a small dataset.

show abstract

“…The diffusion of knowledge is also investigated to understand the intellectual growth in much broader contexts. We employ a scientometrics review [2][3][4][5][6] using a set of quantitative and visual analytics. Compared to the conventional reviews, this approach offers the following advantages: 1) a more diverse range of bibliographic entities can be analyzed; 2) this type of domain analysis can be conducted as frequently as needed without prior experience in a target domain; and 3) citation analysis used in this work provides topically relevant, influential references which, otherwise, can be chosen less objectively.…”

Section: Introductionmentioning

confidence: 99%

Mapping scientific landscapes in UMLS research: a scientometric review

Kim

Nam

Wang

et al. 2020

Journal of the American Medical Informatics Association

Self Cite

View full text Add to dashboard Cite

Objective The Unified Medical Language System (UMLS) is 1 of the most successful, collaborative efforts of terminology resource development in biomedicine. The present study aims to 1) survey historical footprints, emerging technologies, and the existing challenges in the use of UMLS resources and tools, and 2) present potential future directions. Materials and Methods We collected 10 469 bibliographic records published between 1986 and 2019, using a Web of Science database. graph analysis, data visualization, and text mining to analyze domain-level citations, subject categories, keyword co-occurrence and bursts, document co-citation networks, and landmark papers. Results The findings show that the development of UMLS resources and tools have been led by interdisciplinary collaboration among medicine, biology, and computer science. Efforts encompassing multiple disciplines, such as medical informatics, biochemical sciences, and genetics, were the driving forces behind the domain’s growth. The following topics were found to be the dominant research themes from the early phases to mid-phases: 1) development and extension of ontologies and 2) enhancing the integrity and accessibility of these resources. Knowledge discovery using machine learning and natural language processing and applications in broader contexts such as drug safety surveillance have recently been receiving increasing attention. Discussion Our analysis confirms that while reaching its scientific maturity, UMLS research aims to boundary-span to more variety in the biomedical context. We also made some recommendations for editorship and authorship in the domain. Conclusion The present study provides a systematic approach to map the intellectual growth of science, as well as a self-explanatory bibliometric profile of the published UMLS literature. It also suggests potential future directions. Using the findings of this study, the scientific community can better align the studies within the emerging agenda and current challenges.

show abstract

Investigating the integrated landscape of the intellectual topology of bioinformatics

Cited by 9 publications

References 47 publications

Visualizing the structure and the evolving of digital medicine: a scientometrics review

Visualizing the structure and the evolving of digital medicine: a scientometrics review

Topic analysis of academic disciplines based on prolific and authoritative researchers

Mapping scientific landscapes in UMLS research: a scientometric review

Contact Info

Product

Resources

About