Cited references and Medical Subject Headings (MeSH) as two different knowledge representations: clustering and mappings at the paper level

Leydesdorff, Loet; Comins, Jordan A.; Sorensen, Aaron A.; Bornmann, Lutz; Hellsten, Iina

doi:10.1007/s11192-016-2119-7

Cited by 40 publications

(29 citation statements)

References 32 publications

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“…Limited evidence exists specifically regarding hyperoxia in trauma patients. Based on our review of the literature via a systematic Medical Subject Headings search, prior studies are limited to mostly traumatic brain injury (TBI) patients and do not account for cumulative oxygen exposure ( 15 ).…”

mentioning

confidence: 99%

Association Between Hyperoxia, Supplemental Oxygen, and Mortality in Critically Injured Patients

Douin

Anderson

Dylla

et al. 2021

Critical Care Explorations

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mentioning

confidence: 99%

Association Between Hyperoxia, Supplemental Oxygen, and Mortality in Critically Injured Patients

Douin

Anderson

Dylla

et al. 2021

Critical Care Explorations

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“…The selection of terms with a minimum of ten occurrences is attributed to the fact that the network visualization becomes clear and avoids overlapping of labels. Furthermore, selecting ten or more than ten terms help to perform core set analysis (Leydesdorff et al , 2016) with an ease of interpretation. For example, Van Eck and Waltman (2017) selected minimum of ten terms for cluster creation with the reason that “when the number of objects to be clustered is relatively limited, analyzing and interpreting the results obtained from a clustering technique usually does not cause any significant difficulties.” The top 10 keywords with the highest frequency are; “Cov” (694), “Coronavirus” (601), “Virus” (346), “Hospital” (254), “Impact” (218), “Approach” (175), “Outcome” (172), “Pneumonia” (169), “Development” (137) and “Challenge” (127).…”

Section: Discussionmentioning

confidence: 99%

“…network visualization becomes clear and avoids overlapping of labels. Furthermore, selecting ten or more than ten terms help to perform core set analysis (Leydesdorff et al, 2016) with an ease of interpretation. For example, Van Eck and Waltman (2017) selected minimum of ten terms for cluster creation with the reason that "when the number of objects to be clustered is relatively limited, analyzing and interpreting the results obtained from a clustering technique usually does not cause any significant difficulties."…”

Section: Funding Of Covid-19 Researchmentioning

confidence: 99%

Measuring the funding landscape of COVID-19 research

Shueb

Gul

Nisa³

et al. 2021

LHT

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PurposeThe purpose of the study is to map the funding status of COVID-19 research. The various aspects, such as funding ratio, geographical distribution of funded articles, journals publishing funded research and institutions that sponsor the COVID-19 research are studied. To visualize the country collaboration network and research trends/hotspots in the field of COVID-19 funded research, keyword analysis is also performed. The open-access (OA) status of the funded research on COVID-19 is also discussed.Design/methodology/approachThe leading indexing and abstracting database, i.e. Web of Science (WoS), was used to retrieve the funded articles published on the topic COVID-19. The scientometric approach, more particularly “funding acknowledgment analysis (FAA),” was used to study the research funding.FindingsA total of 5,546 publications of varied nature have been published on COVID-19, of which 1,760 are funded, thus indicating a funding ratio of 32%. China is the leading producer of funded research (760, 43.182%) on COVID-19 followed by the USA (482, 27.386%), England (179, 10.17%), Italy (119, 6.761%), Germany (107, 6.08%) and Canada (107, 6.08%). China is also in lead in terms of the funding ratio (60.94%). However, the funding ratio of the USA (31.54%) is at 11th rank behind Canada (40.68%), Germany (34.18%) and England (35.87%). The USA occupies a central position in the collaboration network having the highest score of articles with other countries (n = 489), with the USA–China collaboration ranking first (n = 123). National Natural Science Foundation of China (NSFC) is the largest source of funding for COVID-19 research, supporting 342 (19.432%) publications, followed by the United States Department of Health Human Services (DHHS) and National Institute of Health (NIH), USA with 211 (11.989%) and 200 (11.364%) publications, respectively. However, China's National Key Research and Development Program achieves the highest citation impact (80.24) for its funded publications. Journal of Medical Virology, Science of the Total Environment and EuroSurveillance are the three most prolific journals publishing 63 (3.58%), 35 (1.989%) and 32 (1.818%), respectively, of the sponsored research articles on the COVID-19. A total of 3,138 institutions produce funded articles with Huazhong University of Science Technology and Wuhan University from China at the forefront publishing 92 (5.227%) and 83 (4.716%) publications, respectively. The funded research on COVID-19 is largely available in OA mode (1,674, 95.11%) and mainly through the Green and Bronze routes. The keyword clustering reveals that the articles mainly focus on the impact, structure and clinical characteristics of the virus.Research limitations/implicationsThe study's main limitation is that the results are based on the publications indexed by WoS, which has limited coverage compared to other databases. Moreover, all the funding agencies do not require or authors miss to acknowledge funding sources in their publications, which ultimately undermines the number of funded publications. The research publications on COVID-19 are also proliferating; thus, the study's findings shall be valid for a minimum period.Practical implicationsThe funding of research on the COVID-19 is highly essential to accelerate innovative research and help countries fight against the global pandemic. The study's findings reflect the efforts made by nations and institutions to remove the financial and accessibility hurdles. It not only underscores the lead of the USA in the research on COVID-19, but also shows China as a forerunner in sponsoring the research, thus, helping to know the contribution of nations toward understanding the dynamics of pandemic and controlling it. The study will help healthcare practitioners and policymakers recognize the areas that remain the focus of sponsored research on COVID-19 and other left-out areas that need to be taken up and thus may help in policy formulation. It further highlights the impact of prolific funding agencies so that efforts may be initiated to increase the impact and thereby the returns of investment. The study can help to map the scientific structure of COVID-19 through the lens of funded research and recognize core inclinations of its development. Overall, a comprehensive analysis has been performed to present the detailed characteristics of sponsored research on emerging area of COVID-19, and it is informative, useful and one of its kind on the theme.Originality/valueThe study explores the funding support of research on COVID-19 and its other aspects, along with the mode of availability.

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“…As the names of drugs and diseases in different datasets often vary in their vocabulary, this required consideration and adjustment for standardization. For example, the drug names in the indication dataset, side effect dataset, and contraindication dataset were from DrugBank ( Wishart et al, 2007 ), ATC ( Miller and Britt, 1995 ), and RxNorm ( Nelson et al, 2011 ), respectively, while the corresponding disease names in these three datasets were from OMIM ( Hamosh et al, 2005 ), UMLS ( Bodenreider, 2004 ), and MeSH ( Leydesdorff et al, 2016 ), respectively. To unify the drug and disease names, we mapped their names to Unified Medical Language System (UMLS) ontology.…”

Section: Methodsmentioning

confidence: 99%

DDIT: An Online Predictor for Multiple Clinical Phenotypic Drug-Disease Associations

Qin

Chen

et al. 2022

Front. Pharmacol.

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Background: Drug repurposing provides an effective method for high-speed, low-risk drug development. Clinical phenotype-based screening exceeded target-based approaches in discovering first-in-class small-molecule drugs. However, most of these approaches predict only binary phenotypic associations between drugs and diseases; the types of drug and diseases have not been well exploited. Principally, the clinical phenotypes of a known drug can be divided into indications (Is), side effects (SEs), and contraindications (CIs). Incorporating these different clinical phenotypes of drug–disease associations (DDAs) can improve the prediction accuracy of the DDAs.Methods: We develop Drug Disease Interaction Type (DDIT), a user-friendly online predictor that supports drug repositioning by submitting known Is, SEs, and CIs for a target drug of interest. The dataset for Is, SEs, and CIs was extracted from PREDICT, SIDER, and MED-RT, respectively. To unify the names of the drugs and diseases, we mapped their names to the Unified Medical Language System (UMLS) ontology using Rest API. We then integrated multiple clinical phenotypes into a conditional restricted Boltzmann machine (RBM) enabling the identification of different phenotypes of drug–disease associations, including the prediction of as yet unknown DDAs in the input.Results: By 10-fold cross-validation, we demonstrate that DDIT can effectively capture the latent features of the drug–disease association network and represents over 0.217 and over 0.072 improvement in AUC and AUPR, respectively, for predicting the clinical phenotypes of DDAs compared with the classic K-nearest neighbors method (KNN, including drug-based KNN and disease-based KNN), Random Forest, and XGBoost. By conducting leave-one-drug-class-out cross-validation, the AUC and AUPR of DDIT demonstrated an improvement of 0.135 in AUC and 0.075 in AUPR compared to any of the other four methods. Within the top 10 predicted indications, side effects, and contraindications, 7/10, 9/10, and 9/10 hit known drug–disease associations. Overall, DDIT is a useful tool for predicting multiple clinical phenotypic types of drug–disease associations.

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Cited references and Medical Subject Headings (MeSH) as two different knowledge representations: clustering and mappings at the paper level

Cited by 40 publications

References 32 publications

Association Between Hyperoxia, Supplemental Oxygen, and Mortality in Critically Injured Patients

Association Between Hyperoxia, Supplemental Oxygen, and Mortality in Critically Injured Patients

Measuring the funding landscape of COVID-19 research

DDIT: An Online Predictor for Multiple Clinical Phenotypic Drug-Disease Associations

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