This page has only limited features, please log in for full access.
A better understanding of the clinical characteristics of coronavirus disease 2019 (COVID-19) is urgently required to address this health crisis. Numerous researchers and pharmaceutical companies are working on developing vaccines and treatments; however, a clear solution has yet to be found. The current study proposes the use of artificial intelligence methods to comprehend biomedical knowledge and infer the characteristics of COVID-19. A biomedical knowledge base was established via FastText, a word embedding technique, using PubMed literature from the past decade. Subsequently, a new knowledge base was created using recently published COVID-19 articles. Using this newly constructed knowledge base from the word embedding model, a list of anti-infective drugs and proteins of either human or coronavirus origin were inferred to be related, because they are located close to COVID-19 on the knowledge base. This study attempted to form a method to quickly infer related information about COVID-19 using the existing knowledge base, before sufficient knowledge about COVID-19 is accumulated. With COVID-19 not completely overcome, machine learning-based research in the PubMed literature will provide a broad guideline for researchers and pharmaceutical companies working on treatments for COVID-19.
Heyoung Yang; Eunsoo Sohn. Expanding Our Understanding of COVID-19 from Biomedical Literature Using Word Embedding. International Journal of Environmental Research and Public Health 2021, 18, 3005 .
AMA StyleHeyoung Yang, Eunsoo Sohn. Expanding Our Understanding of COVID-19 from Biomedical Literature Using Word Embedding. International Journal of Environmental Research and Public Health. 2021; 18 (6):3005.
Chicago/Turabian StyleHeyoung Yang; Eunsoo Sohn. 2021. "Expanding Our Understanding of COVID-19 from Biomedical Literature Using Word Embedding." International Journal of Environmental Research and Public Health 18, no. 6: 3005.
The Fourth Industrial Revolution caused by innovative technologies is an irresistible megatrend, and many companies, institutions, and major countries are making efforts to participate. The World Economic Forum took the lead in discussing the Fourth Industrial Revolution, adding the issue to its 2016 agenda, and found that many governments, including that of Korea, were concerned about how to support their nation’s participation in the Fourth Industrial Revolution and were pursuing programs to support such efforts. In this study, we describe one of those programs, the Korean government’s Flagship Project Support Program (FPSP), which supports latecomers in creating open platforms and creating new business ideas in innovative technological industries. The program helps businesses overcome entry barriers to existing business ecosystems established by big technological players in growing fields such as smart cars, the Internet of Things (IoT), virtual reality (VR), etc. The purpose of this study is to determine whether latecomers and small- and medium-sized companies that are experiencing difficulties in their own innovation can succeed in innovation through the Korean government’s FPSP. This study performed a comprehensive and qualitative analysis based on the Logic Model Framework consisting of an investigation of business ecosystems before and after the FPSP, assessment of outcomes, and evaluation of the effectiveness of the FPSP. This study shows that open platforms resulting from the FPSP successfully innovated business models in Korea. Our study, therefore, has implications for other governments seeking to play a role in supporting the Fourth Industrial Revolution.
Heyoung Yang; Su Youn Kim; Seongmin Yim. A Case Study of the Korean Government’s Preparation for the Fourth Industrial Revolution: Public Program to Support Business Model Innovation. Journal of Open Innovation: Technology, Market, and Complexity 2019, 5, 35 .
AMA StyleHeyoung Yang, Su Youn Kim, Seongmin Yim. A Case Study of the Korean Government’s Preparation for the Fourth Industrial Revolution: Public Program to Support Business Model Innovation. Journal of Open Innovation: Technology, Market, and Complexity. 2019; 5 (2):35.
Chicago/Turabian StyleHeyoung Yang; Su Youn Kim; Seongmin Yim. 2019. "A Case Study of the Korean Government’s Preparation for the Fourth Industrial Revolution: Public Program to Support Business Model Innovation." Journal of Open Innovation: Technology, Market, and Complexity 5, no. 2: 35.
Motivation: PubMed is a primary source of biomedical information comprising search tool function and the biomedical literature from MEDLINE which is the US National Library of Medicine premier bibliographic database, life science journals and online books. Complimentary tools to PubMed have been developed to help the users search for literature and acquire knowledge. However, these tools are insufficient to overcome the difficulties of the users due to the proliferation of biomedical literature. A new method is needed for searching the knowledge in biomedical field. Methods: A new method is proposed in this study for visualizing the recent research trends based on the retrieved documents corresponding to a search query given by the user. The Medical Subject Headings (MeSH) are used as the primary analytical element. MeSH terms are extracted from the literature and the correlations between them are calculated. A MeSH network, called MeSH Net, is generated as the final result based on the Pathfinder Network algorithm. Results: A case study for the verification of proposed method was carried out on a research area defined by the search query (immunotherapy and cancer and “tumor microenvironment”). The MeSH Net generated by the method is in good agreement with the actual research activities in the research area (immunotherapy). Conclusion: A prototype application generating MeSH Net was developed. The application, which could be used as a “guide map for travelers”, allows the users to quickly and easily acquire the knowledge of research trends. Combination of PubMed and MeSH Net is expected to be an effective complementary system for the researchers in biomedical field experiencing difficulties with search and information analysis.
Heyoung Yang; Hyuck Jai Lee. Research Trend Visualization by MeSH Terms from PubMed. International Journal of Environmental Research and Public Health 2018, 15, 1113 .
AMA StyleHeyoung Yang, Hyuck Jai Lee. Research Trend Visualization by MeSH Terms from PubMed. International Journal of Environmental Research and Public Health. 2018; 15 (6):1113.
Chicago/Turabian StyleHeyoung Yang; Hyuck Jai Lee. 2018. "Research Trend Visualization by MeSH Terms from PubMed." International Journal of Environmental Research and Public Health 15, no. 6: 1113.
Increasing costs, risks, and productivity problems in the pharmaceutical industry are important recent issues in the biomedical field. Open innovation is proposed as a solution to these issues. However, little statistical analysis related to collaboration in the pharmaceutical industry has been conducted so far. Meanwhile, not many cases have analyzed the clinical trials database, even though it is the information source with the widest coverage for the pharmaceutical industry. The purpose of this study is to test the clinical trials information as a probe for observing the status of the collaboration network and open innovation in the pharmaceutical industry. This study applied the social network analysis method to clinical trials data from 1980 to 2016 in ClinicalTrials.gov. Data were divided into four time periods—1980s, 1990s, 2000s, and 2010s—and the collaboration network was constructed for each time period. The characteristic of each network was investigated. The types of agencies participating in the clinical trials were classified as a university, national institute, company, or other, and the major players in the collaboration networks were identified. This study showed some phenomena related to the pharmaceutical industry that could provide clues to policymakers about open innovation. If follow-up studies were conducted, the utilization of the clinical trial database could be further expanded, which is expected to help open innovation in the pharmaceutical industry.
Heyoung Yang; Hyuck Jai Lee. Long-Term Collaboration Network Based on ClinicalTrials.gov Database in the Pharmaceutical Industry. Sustainability 2018, 10, 322 .
AMA StyleHeyoung Yang, Hyuck Jai Lee. Long-Term Collaboration Network Based on ClinicalTrials.gov Database in the Pharmaceutical Industry. Sustainability. 2018; 10 (2):322.
Chicago/Turabian StyleHeyoung Yang; Hyuck Jai Lee. 2018. "Long-Term Collaboration Network Based on ClinicalTrials.gov Database in the Pharmaceutical Industry." Sustainability 10, no. 2: 322.