[Visualization analysis on treatment of coronavirus based on knowledge graph].
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,014 | 0,012 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».