[Visualization analysis on treatment of coronavirus based on knowledge graph].
Bibliographic record
Abstract
OBJECTIVE: To discuss the research progress in the field of coronavirus (CoVs) treatment based on the visualization analysis of knowledge graph. METHODS: The related literatures in the field of CoVs treatment were retrieved from the establishment of Web of Science core collection database to February 15th, 2020, and the literature analysis tool of Web of Science database was used to count the annual trend of published literatures. The VOSviewer software was used to analyze the relationship among countries, institutions, authors, clustering and density of subject words. The HistCite software was used to screen important documents and to draw the evolution process of hot spots. The CiteSpace software was used to analyze the breakout points of subject words, so as to find the front and hot spots in this field. RESULTS: A total of 1 747 data were retrieved, with the exception of 17 duplicate data, and 1 730 data were retained for visualization analysis. In terms of literature volume, the literatures on CoVs therapy rose after 2003 and 2012, and the number of published literatures had remained high since 2014. In terms of countries, the main countries that carried out the research on the treatment of CoVs were the United States (n = 613), China (n = 582), Germany (n = 122), Canada (n = 99), etc., and the cooperation among countries was close. In terms of institutions, the number of papers issued by Chinese Academy of Sciences in the field of CoVs treatment ranked first (n = 82), followed by University of Hong Kong of China (n = 74) and Chinese University of Hong Kong of China (n = 58), followed by National Institute of Allergy and Infectious Diseases (n = 37), and the cooperation among various institutions was close. In terms of literature authors, there were two high-yielding authors in the United States [Ralph S. Baric (n = 21) and Kuochen Chou (n = 17)], two Chinese authors [Yuen Kwok-yung (n = 17) and Jiang Shibo (n = 16)] and one Dutch author [Eric J. Snijder (n = 17)]. In terms of the cluster analysis of authors, the authors were closely related in reverse genetics, respiratory infection, receptor binding domain, etc., and the 15 top-cited papers came mainly from China, the United States, Netherlands and other countries, and the literature content represented the frontiers and hot spots in different periods. The treatment hot spots focused on preventing virus adsorption, inhibiting the virus gene nucleic acid replication, transcription and translation. The main subject words were divided into three main categories, namely, CoVs epidemiology, basic research and drug development, in which basic research and drug development were strongly correlated. In the subject words breakthrough analysis, there were time-related breakthrough points in 1991, 1996 and 2002, and the "diagnosis" and "sequence" were continuous hot spots. CONCLUSIONS: Through the visualization analysis of knowledge graph, the development trend and hot spots of CoVs therapy research could be well observed. In this study, the degree of attention in the field of CoVs treatment showed periodic changes, related to the outbreak of new CoVs, and the country, institutions and the author were closely related. The treatment hot spots focused on preventing virus adsorption, inhibiting the virus gene nucleic acid replication, transcription and translation in order to develop new targets of drug.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".