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

ISSN: 1747-0862

Journal of Molecular and Genetic Medicine

  • Research Article   
  • J Mol Genet Med 2018, Vol 12(1): 319
  • DOI: 10.4172/1747-0862.1000319

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

Chien TW1, Chang Y2, Chow JC3 and Chou W4,5*
1Department of Research, Chi-Mei Medical Center, Tainan, Taiwan
2School of Medicine, National Taiwan University, Taiwan
3Department of Paediatrics, Chi Mei Medical Center, Tainan, Taiwan
4Department of Physical Medicine and Rehabilitation, Chi Mei Medical Center, Tainan, Taiwan
5Department of Recreation and Health-Care Management and Institute of Recreation Industry Management, Chia Nan University of Pharmacy, Tainan, Taiwan
*Corresponding Author: Chou W, Department of Physical Medicine and Rehabilitation, Chi Mei Medical Center, Tainan, Taiwan, Tel: 90 474 225 11 67, Email: [email protected]

Received Date: Dec 20, 2017 / Accepted Date: Jan 18, 2018 / Published Date: Jan 23, 2018

Abstract

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.

Keywords: Authorship collaboration; Social network analysis; Google maps; Medline

Introduction

Many papers have been saved in Medline library. International collaboration in science has increased rapidly in recent decades [1]. The dominant nations in Science come from U.S. and Europe [2,3]. Whether the field of molecular and genetic research is also dominated by U.S. and Europe remains unclear.

The co-author relation is similar to the comorbid co-occurred with one another in medicine. Molecular and genetic terms are often combined together in literature. Many studies have made efforts to explore the association of two or more relevant entities such as molecular epidemiological analysis used for revealing a high level of genetic diversity [4] and molecular and genetic determinants together existed in glioma cell invasion [5]. The pattern of international coauthor collaboration in molecular and genetic research is worthwhile to study. It is rather hard using traditional statistics to observe the association of two or more symptoms co-occurred at one moment till social network analysis (SNA) [6] has been launched to explore the pattern of elements in a system.

Google maps offers a global view of geospatial visualization for our interesting objects dispersed on a map [7,8]. However, only four papers were found in Medline library using keyword google map (Title) on December 12, 2017. Many papers [9-11] have researched on co-author collaboration in academics, but failed to incorporate SNA with Google maps in their studies. We are thus interested in investigating whether it is possible to show the pattern of author collaboration in molecular and genetic research using Google maps.

Our aims are to investigate patterns of international co-author collaborations in molecular and genetic research by collecting data from Medline and to visualize results with following representations: (1) nation and journal distribution; (2) main keywords frequently presented in papers; (3) the eminent author in the field of molecular and genetic research.

Methods

Data sources

We programed Microsoft Excel VBA (visual basic for applications) modules for extracting abstracts and their corresponding coauthor names as well as keywords on December 12, 2017 from Medline library. Only those abstracts entitled with molecular and genetic topics and labelled with Journal Article were included. Others like those labelled with Published Erratum, Editorial or those without author nation were excluded from this study. A total of 6,732 eligible abstracts were obtained from Medline since 1983. Only 5,088 papers are labeled with 1st author nation in Medline.

Data arrangement to fit SNA requirement

We analyzed all eligible papers with complete data consisting of author countries and journal names. Prior to visualize representations using SNA, we organized data in compliance with the SNA format and guidelines of Pajek software [12]. Microsoft Excel VBA was used to deal with data fitting to the SNA requirement.

Graphical Representations to Report

Author nations and their relations

Two cross tables (i.e. columns for publication years and rows for the 1st author nations as well as journals) were generated for showing the distribution of nations and the most number of journals publishing papers of molecular and genetic research. The bigger bubble means the more number of the nodes (i.e., nations, or authors). The wider line indicates the stronger relations between two nodes. Community clusters are filled with different colors in bubbles.

Keywords and authors to present the feature of molecular and genetic research

Keywords in abstract are defined by authors. Research domain can be highlighted by the relation between any pair of two keywords using SNA. The representation for the bubble and line is interpreted similar to the previous section.

Statistical Tools and Data Analyses

Google Maps [13] and SNA Pajek software [12] were used to display visualized representations for papers published in the field of molecular and genetic research. Author-made Excel VBA modules were used to organize research data.

Cluster coefficient represents the density of a network as below= equation whereas n=the number of nodes in a network and m=the number of other connected nodes with a specific ego node. A significant lever (>1.96) is defined by t-value as the formula[=cc*√[(n-2)/(1-cc2)].

In contrast, E-I index is defined by the formula of equation where EL=hte number of external friendshio links and IL= the number of internal friendship links [14]. The negative E-I index means a coherence cluster in existence. Similarly, the higher CC indicates many members are other linked members’ friends. Density is defined as the ratio of the linked members over all possible linked members.

Results

Author nations and their relations

A total of 5,088 eligible papers with complete author nations since 1983 are shown in Table 1. We can see that the most number of nations are from U.S. (1622,31.88%), China (361, 7.10%), and Japan (356, 7.00%). The trend in the number of publications for countries is present in the column of growth in Table 1. We can see Asia with the highest growth rate (0.91) The diagram (shown by SNA and Google maps) in Figure 1 displays author collaboration among nations. The highest productive nations are from Europe (36.24%) and North America (35.22). Any nation collaborated with other nations are shown with a blue line. Interested authors are recommend clicking the bubble of interest to see details on a website at reference (Table 1 and Figure 1) [15].

molecular-genetic-medicine-International-coauthor

Figure 1: International coauthor collaboration in molecular and genetic research.

Nation 1983-2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Total % Growth
Africa 7 3 3 1 2 5 4 2 3 3 5 38 0.75 0.39
Tunisia 2 - 1 1 1 2 - - 1 2 - 10 0.20 0.09
Nigeria 1 - - - - 2 3 - - - - 6 0.12 -
Egypt 1 - - - 1 - - - - - 3 5 0.10 -
Kenya 1 1 - - - - - 2 - - 1 5 0.10 0.46
Others 2 2 2 0 0 1 1 0 2 1 1 12 0.24 0.37
Asia 440 38 43 63 71 75 78 65 85 94 91 1143 22.46 0.91
China 90 16 13 19 28 25 32 33 37 31 37 361 7.10 0.17
Japan 222 7 12 21 19 17 9 7 10 18 14 356 7.00 0.06
India 28 4 7 8 11 17 15 10 12 17 11 140 2.75 0.91
Taiwan 24 1 3 7 1 6 4 3 2 3 5 59 1.16 -0.03
Others 76 10 8 8 12 10 18 12 24 25 24 227 4.46 5.40
EUROPE 1123 61 66 78 69 73 64 64 63 97 82 1844 36.24 0.50
U.K 232 10 10 9 13 2 6 5 10 16 7 320 6.29 -0.17
Germany 213 10 9 12 7 11 6 8 9 7 9 301 5.92 0.14
France 130 7 6 7 7 6 4 10 4 10 6 197 3.87 0.41
Italy 96 2 5 11 8 14 8 11 5 7 11 180 3.54 -0.42
Netherlands 79 2 7 3 5 6 8 5 5 8 4 132 2.59 -0.11
Others 373 30 29 36 29 34 32 25 30 49 45 714 14.03 1.98
N. AMERICA 1245 54 63 41 53 61 56 62 59 51 46 1792 35.22 -0.11
U.S 1135 51 59 38 46 54 49 52 52 46 40 1622 31.88 0.32
Canada 97 3 3 3 5 6 6 5 6 2 5 142 2.79 0.52
Mexico 7 - 1   2 - 1 4 1 3 - 19 0.37 -
Cuba 3 - - - - - - - - - - 3 0.06 -
Others 3 - - - - 1 - 1 - - 1 6 0.12 0.03
OCEANIA 75 3 5 6 8 3 2 7 7 7 5 128 2.52 0.29
Australia 64 3 4 6 7 2 2 5 5 6 4 108 2.12 0.13
New Zealand 11 - - - 1 1 - - 2 - - 15 0.29 0.17
Palestine 0 - 1 - - - - 1 - 1 1 4 0.08 0.16
Myanmar 0 - - - - - - 1 - - - 1 0.02 0.43
S. AMERICA 49 6 4 12 8 6 10 11 15 14 8 143 2.81 0.59
Brazil 20 6 2 4 3 4 8 7 9 9 7 79 1.55 0.17
Argentina 8 - 2 3 1 - 2 2 2 2 1 23 0.45 0.72
Chile 11 - - 1 2 - - 1 4 1   20 0.39 0.27
Colombia 5 - - 3 - 1 - - - - - 9 0.18 -0.30
Others 5 - - 1 2 1 - 1 - 2 - 12 0.24 0.32
Total 2939 165 184 201 211 223 214 211 232 266 237 5088 100.00 0.89

Table 1: Nation distribution based on the 1st author in molecular and genetic research.

Journals and the trend

A total of 6,732 eligible abstracts were analyzed regarding title with either molecular or genetic keyword. The most number of journals in production is Genetika (103, 1.53%). The trend for a journal is shown in the column of correlation in Table 2. BoTh journals of PLoS One (0.84) and Genet Mol Res (0.85) earn the highest growth in past years. We can see other journals are increasing or decreasing in papers regarding molecular or genetic research (Table 2).

Journal 1983-2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Total % Growth
Genetika 54 9 6 3 8 5 7 3 5 1 - 103 1.53 -0.74
Genetics 89 - 1 3 - - - - - - 1 94 1.40 -0.25
PLoS One 0 1 2 7 5 9 13 15 14 12 11 89 1.32 0.84
Theor Appl Genet 48 1 3 2 3 2 1 3 2 3 4 72 1.07 0.49
J Bacteriol 56 - - - - 1 - - - - - 64 0.95 -0.06
Proc Natl Acad Sci U S A 35 2 1 1 3 2 - 1 - 3 - 60 0.89 -0.27
Genet Mol Res 1   2 4 3 6 3 5 10 9 7 50 0.74 0.85
Am J Surg Pathol 22 7 5 1 2 1 1 3 2 2 2 48 0.71 -0.54
Infect Genet Evol 3 - 2 3 4 7 7 - 5 9 3 43 0.64 0.45
Mol Ecol 26 2 5 2 1 1 1 - - - 1 39 0.58 -0.70
Others 334 22 27 26 29 34 33 30 38 39 29 662 9.83 0.72
Total 3933 226 222 254 264 281 264 301 290 305 265 6732 100.00 0.78

Table 2: Journal distribution for papers in molecular and genetic research.

Keywords to present the feature of research domain

Two clusters of RT-PCR and genetic association earn the highest cluster coefficient (Table 3) [16]. We can see that the two bigger bubbles are of RT-PCR and genetic association in respective clusters (Table 3).

Name CC Density Weighted EI Count of Nodes degree Weighted t-value
RT-PCR 0.89 0.02 0.04 -1.00 60 44 68 14.87
Genetic association 0.88 0.04 0.05 -0.91 23 11 12 8.49
Microsatellites 0.83 0.09 0.09 -0.71 27 30 30 7.44
Fish 0.81 0.01 0.01 -0.57 49 9 12 9.47
Genetics 0.80 0.15 0.30 -1.00 37 97 201 7.89
Genetic analysis 0.76 0.03 0.03 -0.70 44 26 27 7.58
Genotyping 0.71 0.05 0.05 -0.62 60 83 83 7.68
Mutations 0.70 0.05 0.05 -0.99 58 83 87 7.34
Mucopolysaccharidosis type iva 0.68 0.03 0.04 -0.61 48 33 43 6.29
Mutation 0.65 0.05 0.06 -0.86 64 101 113 6.73
Barhanin, J (Italy) 0.91 1.00 3 -1 12 66 198 6.94
Cao, Shinuo (Japan) 0.78 1.00 3 -1 9 36 108 3.3
Aghokeng, Avelin F (Estonia) 0.57 1.00 3 -1 21 210 630 3.02
Blankenberg, Stefan (Netherlands) 0.49 1.00 3 -1 22 231 693 2.51
Ivanova-Smolenskaia, I A (UK) 0.48 0.53 2.24 -1 10 24 101 1.55
Blatt, Ilan (Israel) 0.47 0.93 3.77 -1 23 236 955 2.44
Najera, Rafael (Cuba) 0.46 1.00 3.02 -1 10 45 136 1.47
Chen, Chih-Ping (Taiwan) 0.34 0.45 2.18 -1 18 69 333 1.45
Cheng, Liang (US) 0.33 0.51 2.45 -1 11 28 135 1.05
Michal, Michal (Czech Republic) 0.32 0.39 1.66 -1 28 149 626 1.72

CC: Cluster Coefficient; Density Centrality: The Number of Connected Relation/All Possible Connected Relation (=n(n-1)/2, where n: the Number of Nodes (or Entities); Weighted Centrality includes Repeated Connection Relations; Count of Nodes: the Number of Members in The Network; Degree: the Number of Connected Relation; Weighted: all Repeated Connection Relations; t-value: Student t Statistics.

Table 3: Indicators of SNA for the keyword clusters in a global network.

Eminent authors selected by SNA

The eminent with the highest cluster coefficient is J Barhanin form Italy shown in Table 3 (bottom) or click it on the reference [17]. We can see that the top 10 with a higher cluster coefficient are present in (Figure 2).

molecular-genetic-medicine-molecular-genetic

Figure 2: Eminent authors in clusters of molecular and genetic research.

Discussion

This study used SNA techniques to report 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 form Italy.

What this adds to what was known

An apocryphal story is often told to discover the co-occurrence about beer and diaper sales [18-20]. It is hard to see all possible pairs of our observed entities at one short moment. In literature, no such examples but studies [9,10] were illustrated to inspect co-author collaboration using SNA. We demonstrated SNA incorporated with Google maps to display valuable information to readers, which is rare seen in previous papers.

Clusters can be compared with each other using Google maps. We can see that many links connecting nations, indicating a collaboration pattern to the previous study [11]. The results in this study show a huge international co-author collaboration in molecular and genetic research which is consistent with the previous studies that investigated scientific collaboration of Iranian Psychology and Psychiatry Researchers [21,22].

Two papers [23,24] incorporated MeSH (Medical subject heading) with social network analysis to explore knowledge in journal topics. However, no any incorporated SNA with Google maps to show research results like we did in the current study. The way we illustrated here in Figures is novel and promising in academics, especially in the field of molecular and genetic research.

What it implies and what should be changed?

Scientific publication is one of the objective measurements to evaluate the achievements of a medical research [25]. Using SNA and Google Maps is appropriate to report journal features or author research domains in future. Several algorithms have been developed in computer science and have applied SNA to researches. If we further investigate whether author domains or paper keywords are most fitting the scope of a journal, the centrality measures [9] is recommend to readers. It means that the core research domain can be analyzed using the centrality measure [11,23] produced in social network analysis.

Strengths of this study

The way we used with SNA and Google Maps is unique, which is rare seen in previous papers. Another strength (or feature) is regarding Google Maps provided to interested readers who can practice it on their own ways by clicking the links in references [15-17]. The nation distribution in Figure 1 is easy to know the feature of molecular and genetic research. One picture is worth ten thousand words. We expect following studies that can report more information using SNA and Google Maps to readers.

Limitations and future study

The interpretation and generalization this study should be cautious. First, the data were downloaded from Pubmed. Any attempt to generalize the findings should be subject to the similar background or the journal with similar topic and scope.

Second, data were extracted from Pubmed. We also put a lot of efforts on every linkage, the original downloaded data including some errors in symbols such as period, comma or others in author address that might result in some bias.

Third, there are many computer algorithms in social network analysis. We only applied on way to show data. Any changes made in algorithm used for exploration will display different layout of pattern.

Fourth, the social network analysis is not limited in Pajeck software we used in this study, Others such as Ucinet [26] and Gephi [27] are suggested to readers for use in future.

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.

References

Citation: Chien TW, Chang Y, Chow JC, Chou W (2018) Using Visual Representations to Present the Pattern of International Co-Author Collaboration in the Field of Molecular and Genetic Research. J Mol Genet Med 12: 319. Doi: 10.4172/1747-0862.1000319

Copyright: © 2018 Chien TW, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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