1
|
Wu M, Zhang Y, Markley M, Cassidy C, Newman N, Porter A. COVID-19 knowledge deconstruction and retrieval: an intelligent bibliometric solution. Scientometrics 2023:1-31. [PMID: 37360228 PMCID: PMC10230150 DOI: 10.1007/s11192-023-04747-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Accepted: 05/16/2023] [Indexed: 06/28/2023]
Abstract
COVID-19 has been an unprecedented challenge that disruptively reshaped societies and brought a massive amount of novel knowledge to the scientific community. However, as this knowledge flood continues surging, researchers have been disadvantaged by not having access to a platform that can quickly synthesize emerging information and link the new knowledge to the latent knowledge foundation. Aiming to fill this gap, we propose a research framework and develop a dashboard that can assist scientists in identifying, retrieving, and understanding COVID-19 knowledge from the ocean of scholarly articles. Incorporating principal component decomposition (PCD), a knowledge mode-based search approach, and hierarchical topic tree (HTT) analysis, the proposed framework profiles the COVID-19 research landscape, retrieves topic-specific latent knowledge foundation, and visualizes knowledge structures. The regularly updated dashboard presents our research results. Addressing 127,971 COVID-19 research papers from PubMed, the PCD topic analysis identifies 35 research hotspots, along with their inner correlations and fluctuating trends. The HTT result segments the global knowledge landscape of COVID-19 into clinical and public health branches and reveals the deeper exploration of those studies. To supplement this analysis, we additionally built a knowledge model from research papers on the topic of vaccination and fetched 92,286 pre-Covid publications as the latent knowledge foundation for reference. The HTT analysis results on the retrieved papers show multiple relevant biomedical disciplines and four future research topics: monoclonal antibody treatments, vaccinations in diabetic patients, vaccine immunity effectiveness and durability, and vaccination-related allergic sensitization.
Collapse
Affiliation(s)
- Mengjia Wu
- Australian Artificial Intelligence Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia
| | - Yi Zhang
- Australian Artificial Intelligence Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia
| | | | | | | | - Alan Porter
- Search Technology, Inc., Norcross, USA
- Science, Technology & Innovation Policy, Georgia Institute of Technology, Atlanta, USA
| |
Collapse
|
2
|
Trends and hotspots for European Journal of Medicinal Chemistry: A bibliometric study. Eur J Med Chem 2023; 247:115041. [PMID: 36566715 DOI: 10.1016/j.ejmech.2022.115041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 12/08/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022]
Abstract
European Journal of Medicinal Chemistry (EJMC) has been around for a long time and has gained broad interest from the various individuals working in the field. However, there is no bibliometric analysis on the publications of EJMC to thoroughly assess the scientific output and current status systematically. Therefore, the study was conducted to analyze the various publications of EJMC from 1987 to 2022 to improve their quality. A total of 13,386 papers were retrieved, with the number of publications increasing yearly. Based on the multiple indicators of bibliometrics, the highest impact countries, institutions, authors and representative literature were identified, and visualization networks were constructed using VOSviewer. Keyword co-occurrence analysis reveals a gradual shift from phenotypic drug discovery to target-based drug discovery in the EJMC theme change. Moreover, further discussion of the keyword clustering results is provided to support researchers in defining the scope of their research topics and planning their research directions. At this stage, there is a greater focus on developing antitumor and oxidative stress-related drugs than on the earlier anti-infective activities. In future studies, the main research directions are tumor multidrug resistance, oxidative stress, and dual inhibitors.
Collapse
|
3
|
Li X, Tang X, Lu W. Tracking biomedical articles along the translational continuum: a measure based on biomedical knowledge representation. Scientometrics 2023; 128:1295-1319. [PMID: 36570779 PMCID: PMC9758472 DOI: 10.1007/s11192-022-04607-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 11/28/2022] [Indexed: 12/23/2022]
Abstract
Keeping track of translational research is essential to evaluating the performance of programs on translational medicine. Despite several indicators in previous studies, a consensus measure is still needed to represent the translational features of biomedical research at the article level. In this study, we first trained semantic representations of biomedical entities and documents (i.e., bio-entity2vec and bio-doc2vec) based on over 30 million PubMed articles. With these vectors, we then developed a new measure called Translational Progression (TP) for tracking biomedical articles along the translational continuum. We validated the effectiveness of TP from two perspectives (Clinical trial phase identification and ACH classification), which showed excellent consistency between TP and other indicators. Meanwhile, TP has several advantages. First, it can track the degree of translation of biomedical research dynamically and in real-time. Second, it is straightforward to interpret and operationalize. Third, it doesn't require labor-intensive MeSH labeling and it is suitable for big scholarly data as well as papers that are not indexed in PubMed. In addition, we examined the translational progressions of biomedical research from three dimensions (including overall distribution, time, and research topic), which revealed three significant findings. The proposed measure in this study could be used by policymakers to monitor biomedical research with high translational potential in real-time and make better decisions. It can also be adopted and improved for other domains, such as physics or computer science, to assess the application value of scientific discoveries.
Collapse
Affiliation(s)
- Xin Li
- School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030 Hubei China
| | - Xuli Tang
- School of Information Management, Central China Normal University, Wuhan, 430079 Hubei China
| | - Wei Lu
- School of Information Management, Wuhan University, Wuhan, 430072 Hubei China
| |
Collapse
|
4
|
Li X, Tang X, Cheng Q. Predicting the clinical citation count of biomedical papers using multilayer perceptron neural network. J Informetr 2022. [DOI: 10.1016/j.joi.2022.101333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
5
|
Yu Q, Wang Q, Zhang Y, Chen C, Ryu H, Park N, Baek JE, Li K, Wu Y, Li D, Xu J, Liu M, Yang JJ, Zhang C, Lu C, Zhang P, Li X, Chen B, Ebeid IA, Fensel J, Min C, Zhai Y, Song M, Ding Y, Bu Y. Reply to issues about entitymetrics and paper-entity citation network. Scientometrics 2022. [DOI: 10.1007/s11192-022-04311-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
6
|
Sun F, Li Y, Sheng G, Yao X. Issues about entitymetrics and paper-entity citation network. Scientometrics 2022; 127:2123-2125. [PMID: 35250118 PMCID: PMC8882066 DOI: 10.1007/s11192-022-04316-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 02/14/2022] [Indexed: 11/29/2022]
Affiliation(s)
- Fengjun Sun
- Dalian Neusoft University of Information, Dalian, 116023 People’s Republic of China
| | - Yingqiu Li
- Dalian Neusoft University of Information, Dalian, 116023 People’s Republic of China
| | - Guojun Sheng
- Dalian Neusoft University of Information, Dalian, 116023 People’s Republic of China
| | - Xiaolin Yao
- Dalian Neusoft University of Information, Dalian, 116023 People’s Republic of China
| |
Collapse
|
7
|
Wang S, Mao J, Cao Y, Li G. Integrated knowledge content in an interdisciplinary field: identification, classification, and application. Scientometrics 2022. [DOI: 10.1007/s11192-022-04282-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
|
8
|
Liu M, Bu Y, Chen C, Xu J, Li D, Leng Y, Freeman RB, Meyer ET, Yoon W, Sung M, Jeong M, Lee J, Kang J, Min C, Song M, Zhai Y, Ding Y. Pandemics are catalysts of scientific novelty: Evidence from
COVID
‐19. J Assoc Inf Sci Technol 2021; 73:1065-1078. [PMID: 35441082 PMCID: PMC9011856 DOI: 10.1002/asi.24612] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 11/07/2021] [Accepted: 12/08/2021] [Indexed: 11/24/2022]
Abstract
Scientific novelty drives the efforts to invent new vaccines and solutions during the pandemic. First‐time collaboration and international collaboration are two pivotal channels to expand teams' search activities for a broader scope of resources required to address the global challenge, which might facilitate the generation of novel ideas. Our analysis of 98,981 coronavirus papers suggests that scientific novelty measured by the BioBERT model that is pretrained on 29 million PubMed articles, and first‐time collaboration increased after the outbreak of COVID‐19, and international collaboration witnessed a sudden decrease. During COVID‐19, papers with more first‐time collaboration were found to be more novel and international collaboration did not hamper novelty as it had done in the normal periods. The findings suggest the necessity of reaching out for distant resources and the importance of maintaining a collaborative scientific community beyond nationalism during a pandemic.
Collapse
Affiliation(s)
- Meijun Liu
- Institute for Global Public Policy Fudan University Shanghai China
| | - Yi Bu
- Department of Information Management Peking University Beijing China
| | - Chongyan Chen
- School of Information University of Texas at Austin Austin Texas
| | - Jian Xu
- School of Information Management Sun Yat‐sen University Guangzhou China
| | - Daifeng Li
- School of Information Management Sun Yat‐sen University Guangzhou China
| | - Yan Leng
- McCombs School of Business University of Texas at Austin Austin Texas
| | - Richard B. Freeman
- Department of Economics Harvard University Cambridge Massachusetts
- National Bureau of Economic Research (NBER) Cambridge Massachusetts
| | - Eric T. Meyer
- School of Information University of Texas at Austin Austin Texas
| | - Wonjin Yoon
- Department of Computer Science and Engineering Korea University Seoul South Korea
| | - Mujeen Sung
- Department of Computer Science and Engineering Korea University Seoul South Korea
| | - Minbyul Jeong
- Department of Computer Science and Engineering Korea University Seoul South Korea
| | - Jinhyuk Lee
- Department of Computer Science and Engineering Korea University Seoul South Korea
| | - Jaewoo Kang
- Department of Computer Science and Engineering Korea University Seoul South Korea
- Interdisciplinary Graduate Program in Bioinformatics Korea University Seoul South Korea
| | - Chao Min
- School of Information Management Nanjing University Nanjing China
| | - Min Song
- Department of Library and Information Science Yonsei University Seoul South Korea
| | - Yujia Zhai
- Department of Information Resource Management, School of Management Tianjin Normal University Tianjin China
- School of Information Management Wuhan University Wuhan China
| | - Ying Ding
- School of Information University of Texas at Austin Austin Texas
- Dell Medical School University of Texas at Austin Austin Texas
| |
Collapse
|
9
|
On the inequality of citation counts of all publications of individual authors. J Informetr 2021. [DOI: 10.1016/j.joi.2021.101203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
10
|
Li X, Tang X. Characterizing interdisciplinarity in drug research: A translational science perspective. J Informetr 2021. [DOI: 10.1016/j.joi.2021.101216] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
|