Exploring the genetic basis of diseases through a heterogeneous bibliometric network: A methodology and case study

Wu, Mengjia; Zhang, Yi; Zhang, Guangquan; Lu, Jie

doi:10.1016/j.techfore.2020.120513

Cited by 12 publications

(10 citation statements)

References 66 publications

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“…Bio-entity network analysis is a method for literature-based discovery proposed by Wu et al (2021), which is used to discover and infer genetic knowledge for a specific disease. The method extracts bio-entity concepts from literature and constructs a heterogeneous bio-entity co-occurrence network.…”

Section: Bio-entity Network Analysismentioning

confidence: 99%

“…Traditional bibliometric approaches exploit statistical models to analyze bibliographic information such as author entities, keywords and citations and measure scientific activities. In the medical domain, such approaches have been successfully examined by multiple studies, such as profiling research landscapes (Thompson and Walker, 2015;Liao et al, 2018;Huang et al, 2019;Zhang L. et al, 2020) and discovering/ inferring knowledge associations (Hristovski et al, 2005;Porter et al, 2020;Wu et al, 2021). Nowadays, artificial intelligence (AI) and data science techniques have empowered current bibliometrics with novel capabilities of excavating implicit knowledge and inferring potential knowledge associations from bibliometric data, which we named intelligent bibliometrics (Zhang et al, 2020c).…”

Section: Introductionmentioning

confidence: 99%

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Profiling COVID-19 Genetic Research: A Data-Driven Study Utilizing Intelligent Bibliometrics

Zhang

Grosser³

et al. 2021

Front. Res. Metr. Anal.

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The COVID-19 pandemic constitutes an ongoing worldwide threat to human society and has caused massive impacts on global public health, the economy and the political landscape. The key to gaining control of the disease lies in understanding the genetics of SARS-CoV-2 and the disease spectrum that follows infection. This study leverages traditional and intelligent bibliometric methods to conduct a multi-dimensional analysis on 5,632 COVID-19 genetic research papers, revealing that 1) the key players include research institutions from the United States, China, Britain and Canada; 2) research topics predominantly focus on virus infection mechanisms, virus testing, gene expression related to the immune reactions and patient clinical manifestation; 3) studies originated from the comparison of SARS-CoV-2 to previous human coronaviruses, following which research directions diverge into the analysis of virus molecular structure and genetics, the human immune response, vaccine development and gene expression related to immune responses; and 4) genes that are frequently highlighted include ACE2, IL6, TMPRSS2, and TNF. Emerging genes to the COVID-19 consist of FURIN, CXCL10, OAS1, OAS2, OAS3, and ISG15. This study demonstrates that our suite of novel bibliometric tools could help biomedical researchers follow this rapidly growing field and provide substantial evidence for policymakers’ decision-making on science policy and public health administration.

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Section: Bio-entity Network Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Profiling COVID-19 Genetic Research: A Data-Driven Study Utilizing Intelligent Bibliometrics

Zhang

Grosser³

et al. 2021

Front. Res. Metr. Anal.

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“…Bibliometrics is the discipline of tracing scientific activities by quantitatively analyzing scientific literature and documents [36]. The incorporation of bibliometrics with emerging information technologies like AI (text mining and natural language processing, especially) and advanced computational models has introduced a new pathway for bibliometric studies [17,37]. We call it intelligent bibliometrics [38].…”

Section: Bibliometrics and Topic Analysismentioning

confidence: 99%

“…We call it intelligent bibliometrics [38]. Our pilot studies have demonstrated the effectiveness of these cross-disciplinary incorporations [17,21,39].…”

Section: Bibliometrics and Topic Analysismentioning

confidence: 99%

“…Bibliometrics is a set of statistical approaches that recognize the patterns in scientific activities by analyzing scientific documents, such as books, research articles, patents, etc. It has been used widely in science, technology, and innovation (ST&I) studies to identify research trends [15], predict emerging research topics [16], and discover explicit/implicit knowledge [17]. Unlike qualitative methods, bibliometric approaches rely on bibliographic indicators, such as keyword frequency, geographic distribution, and citation statistics, to generate derivate indices to quantitatively and measure the scientific behavior of research entities (e.g., researchers, institutions, and countries) among scientific disciplines and technological areas.…”

Section: Introductionmentioning

confidence: 99%

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Unraveling the capabilities that enable digital transformation: A data-driven methodology and the case of artificial intelligence

Çetindamar

Min

et al. 2021

Advanced Engineering Informatics

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Digital transformation (DT) is prevalent in businesses today. However, current studies to guide DT are mostly qualitative, resulting in a strong call for quantitative evidence of exactly what DT is and the capabilities needed to enable it successfully. With the aim of filling the gaps, this paper presents a novel bibliometric framework that unearths clues from scientific articles and patents. The framework incorporates the scientific evolutionary pathways and hierarchical topic tree to quantitatively identify the DT research topics' evolutionary patterns and hierarchies at play in DT research. Our results include a comprehensive definition of DT from the perspective of bibliometrics and a systematic categorization of the capabilities required to enable DT, distilled from over 10,179 academic papers on DT. To further yield practical insights on technological capabilities, the paper also includes a case study of 9,454 patents focusing on one of the emerging technologies -artificial intelligence (AI). We summarized the outcomes with a four-level AI capabilities model. The paper ends with a discussion on its contributions: presenting a quantitative account of the DT research, introducing a process based understanding of DT, offering a list of major capabilities enabling DT, and drawing the attention of managers to be aware of capabilities needed when undertaking their DT journey.

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BiblioEngine: An AI-Empowered Platform for Disease Genetic Knowledge Mining

Wu,

Zhang,

Lin

et al. 2023

Health Information Science

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Exploring the genetic basis of diseases through a heterogeneous bibliometric network: A methodology and case study

Cited by 12 publications

References 66 publications

Profiling COVID-19 Genetic Research: A Data-Driven Study Utilizing Intelligent Bibliometrics

Profiling COVID-19 Genetic Research: A Data-Driven Study Utilizing Intelligent Bibliometrics

Unraveling the capabilities that enable digital transformation: A data-driven methodology and the case of artificial intelligence

BiblioEngine: An AI-Empowered Platform for Disease Genetic Knowledge Mining

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