Abstract

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.

Highlights

  • The size of the literature in biomedical domain grows exponentially [1]

  • As of 2017, PubMed comprises more than 27 million publications in the broad and up-to-date sources such as biomedical literature from MEDLINE, life science journals, and online books according to the website

  • We studied a methodology to visualize a social network composed of emerging medical keywords in PubMed literature, and introduced an application using this method

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Summary

Introduction

The size of the literature in biomedical domain grows exponentially [1]. Some of the reasons for the growth are the ease of the Internet access, breakdown of interdisciplinary boundaries due to genome-scale instruments, and increasingly interdisciplinary nature of research and development [1,2,3].Professionals such as researchers and educators experience difficulties keeping abreast of the literature in their research areas because of the massive amount of literature [4].PubMed, provided by the National Center for Biotechnology Information (NCBI), has served as the primary information source based on search tool and the literature in biomedical domain.As of 2017, PubMed comprises more than 27 million publications in the broad and up-to-date sources such as biomedical literature from MEDLINE, life science journals, and online books according to the website (https://www.ncbi.nlm.nih.gov/pubmed/). Some of the reasons for the growth are the ease of the Internet access, breakdown of interdisciplinary boundaries due to genome-scale instruments, and increasingly interdisciplinary nature of research and development [1,2,3]. PubMed, provided by the National Center for Biotechnology Information (NCBI), has served as the primary information source based on search tool and the literature in biomedical domain. As of 2017, PubMed comprises more than 27 million publications in the broad and up-to-date sources such as biomedical literature from MEDLINE, life science journals, and online books according to the website (https://www.ncbi.nlm.nih.gov/pubmed/). The total number of publications indexed by PubMed is continuously on the rise; the annual average growth rate was reported to be 4% [2]

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