分子与遗传医学

Using Visual Representations to Present the Pattern of International Co-Author Collaboration in the Field of Molecular and Genetic Research

Abstract

Chien TW, Chang Y, Chow JC and Chou W

Objective: The pattern of international co-author collaboration in molecular and genetic research remains unclear. We collected data from Medline and report the results with graphical presentations using Google maps and social network analysis (SNA).
Methods: Downloading 6,732 abstracts on December 13, 2017 from the Medline library with keywords of Molecular (Title) AND Genetic (Title), we reported following features: (1) nation and journal distribution; (2) main keywords frequently presented in papers; (3) the eminent author and key indicators in SNA. We programmed Microsoft Excel VBA to organize data. Google Maps and SNA Pajek were used for displaying results in molecular and genetic research.
Results: We found that (1) the most number of nations are from U.S. (1622,31.88%), China (361, 7.10%), and Japan (356, 7.00%);(2) the most number of journals is Genetika (103, 1.53%); (3) two clusters of RT-PCR and genetic association earn the highest cluster coefficient; (4) the eminent with the highest cluster coefficient is J Barhanin from Italy.
Conclusion: Social network analysis provides wide and deep insight with the relationships among entities of interest. The results drawn by Google maps can be offered to readers for future submission to journals.