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Investigating the integrated landscape of the intellectual topology of bioinformatics

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Abstract

We aim at identifying (1) whether and how various data sources influence mapping an intellectual structure of the field of bioinformatics, and (2) the landscape of bioinformatics by integrating those sources. To this end, we conduct a comprehensive bibliometric analysis by harvesting bibliographic information from DBLP, PubMed Central, and Web of Science. We then measure and compare topological characteristics of networks generated using these sources. The results show a dichotomous pattern dominated by PubMed Central and WoS. In addition, a few influential scientists in the field of bioinformatics receive very high citations from their colleagues, which is a driving force to bloom the field. These few scientists are connected to a much larger research community. Most of the researchers are intellectually linked within a few steps, in spite of the domain’s interdisciplinary characteristics. Particularly, influential authors consist of a small world. We also identify that there is not a coherent body of discipline in bioinformatics since the field is still under development. Finally, the journals and conferences indexed by each source cover different research topics, and PubMed Central is more inclusive than DBLP as an indexing database.

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References

  • Abraham, A., Hassanien, A., & Snasel, V. (2010). Computational social network analysis: Trends, tools and research advances. New York: Springer.

    Book  Google Scholar 

  • Ahlgren, P., Jarneving, B., & Rousseau, R. (2003). Requirements for a cocitation similarity measure, with special reference to Pearson’s correlation coefficient. JASIST, 54(6), 550–560.

    Article  Google Scholar 

  • Barnett, G. A. (Ed.). (2011). Encyclopedia of social networks. Thousand Oaks, CA: Sage.

    Google Scholar 

  • Beaver, D., & Rosen, R. (1978). Studies in scientific collaboration. Part I. The professional origins of scientific co-authorship. Scientometrics, 1, 65–84.

    Article  Google Scholar 

  • Beaver, D., & Rosen, R. (1979). Studies in scientific collaboration. Part II. Scientific co-authorship, research productivity and visibility in the French Elite. Scientometrics, 1, 133–149.

    Article  Google Scholar 

  • Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 10, 10008.

    Article  Google Scholar 

  • Börner, K., Penumarthy, S., Meiss, M., & Ke, W. (2006). Mapping the diffusion of scholarly knowledge among major U.S. research institutions. Scientometrics, 68(3), 415–426.

  • Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems, 30(1–7), 107–117.

    Article  Google Scholar 

  • Catala-Lopez, F., et al. (2012). Coauthorship and institutional collaborations on cost-effectiveness analyses: A systematic network analysis. PLoS One, 7(5), e38012.

    Article  Google Scholar 

  • Chen, F., Chen, Z., Wang, X., & Yuan, Z. (2008). The average path length of scale free networks. Communications in Nonlinear Science and Numerical Simulation, 13(7), 1405–1410.

    Article  MathSciNet  MATH  Google Scholar 

  • Chung, F. R. K. (1984). Diameters of communications networks. Mathematics of Information Processing, AMS Short Course Lecture Notes, 1–18.

  • Clarke, B. L. (1964). Multiple authorship trends in scientific papers. Science, 143, 822–824.

    Article  Google Scholar 

  • Clarke, B. L. (1967). Communication patterns of biomedical scientists. Federation Proceedings, 26, 1288–1292.

    Google Scholar 

  • Coleman, T. F., & Moré, J. J. (1983). Estimation of Sparse Jacobian matrices and graph coloring problems. SIAM Journal on Numerical Analysis, 20(1), 187–209.

    Article  MathSciNet  MATH  Google Scholar 

  • Day, M., Ong, C., & Hsu, W. (2010). An analysis of research on information reuse and integration (2003–2008). ITSSA, 6(2), 146–157.

    Google Scholar 

  • Ding, Y., Yan, E., Frazho, A., & Caverlee, J. (2009). PageRank for ranking authors in co-citation networks. JASIST, 60(11), 2229–2243.

    Article  Google Scholar 

  • Erma, N., & Todorovski, L. (2010). Co-authorship network analysis in the E-government research field. In Proceedings of EGOV ‘10.

  • Glänzel, W., Janssens, F., & Thijs, B. (2009). A comparative analysis of publication activity and citation impact based on the core literature in bioinformatics. Scientometrics, 79(1), 109–129.

    Article  Google Scholar 

  • Glänzel, W., & Schubert, A. (2004). Analyzing scientific networks through co-authorship. Handbook of Quantitative Science and Technology Research, 257–276.

  • Hany, Y., Zhouz, B., Peiz, J., & Jiay, Y. (2009). Understanding importance of collaborations in co-authorship networks: A supportiveness analysis approach. In Proceedings of SDM ‘09.

  • He, B., Tang, J., Ding, Y., Wang, H., Sun, Y., et al. (2011). Mining relational paths in integrated biomedical data. PLoS One, 6(12), e27506.

    Article  Google Scholar 

  • Heffner, A. G. (1981). Funded research, multiple authorship, and subauthorship collaboration in four disciplines. Scientometrics, 3, 5–12.

    Article  Google Scholar 

  • Hou, H., Kretschmer, H., & Liu, Z. (2006). The structure of scientific collaboration networks in scientometrics. In Proceedings of COLLECT’06.

  • Huang, H., Andrews, J., & Tang, J. (2012). Citation characterization and impact normalization in bioinformatics journals. JASIST, 63(3), 490–497.

    Article  Google Scholar 

  • Huang, T., & Huang, M. L. (2006). Analysis and visualization of co-authorship networks for understanding academic collaboration and knowledge domain of individual researchers. In Proceedings of IEEE CGIV ‘06 (pp. 18–23).

  • Ioannidis, J. P. A. (2008). Measuring co-authorship and networking-adjusted scientific impact. PLoS One, 3(7), e2778.

    Article  Google Scholar 

  • Janssens, F., Glänzel, W., & De Moor, B. (2007). Dynamic hybrid clustering of bioinformatics by incorporating text mining and citation analysis. In Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 360–369).

  • Kim, H. & Barnett, G. A. (2008). Social network analysis using author co-citation data. In Proceedings of the 14th AMCIS, 172.

  • Kolchinsky, A., Abi-Haidar, A., Kaur, J., Hamed, A. A., & Rocha, L. M. (2010). Classification of protein–protein interaction full-text documents using text and citation network features. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 7(3), 400–411.

    Article  Google Scholar 

  • Kulkarni, A. V., Aziz, B., Shams, I., & Busse, J. W. (2009). Comparisons of citations in Web of Science, Scopus, and Google Scholar for articles published in general medical journals. JAMA, 302(10), 1092–1096.

    Article  Google Scholar 

  • Kumar, R., Novak, J., & Tomkins, A. (2010). Structure and evolution of online social networks. NY: Springer.

    Google Scholar 

  • Leydesdorff, L. (2005). Similarity measures, author co-citation analysis, and information theory. JASIST, 57(7), 769–772.

    Article  Google Scholar 

  • Leydesdorff, L., & Vaughan, L. (2006). Co-occurrence matrices and their applications in information science: Extending ACA to the web environment. JASIST, 57(12), 1616–1627.

    Article  Google Scholar 

  • Luscombe, N. M., Greenbaum, D., & Gerstein, M. (2001). What is bioinformatics? A proposed definition and overview of the field. Methods of Information in Medicine, 40(4), 346–358.

    Google Scholar 

  • Luukkonen, T., Persson, O., & Silvertsen, G. (1992). Understanding patterns of international scientific collaboration. Science, Technology and Human Values, 17, 101–126.

    Article  Google Scholar 

  • Luukkonen, T., Tijssen, R. J. W., Persson, O., & Silvertsen, G. (1993). The measurement of international scientific collaboration. Scientometrics, 28, 15–36.

    Article  Google Scholar 

  • Manoharan, A., Kanagavel, B., Muthuchidambaram, A., & Kumaravel, J. P. S. (2011). Bioinformatics research—An informetric view. International Conference on Information Communication and Management, 2011, 199–204.

    Google Scholar 

  • Marques-Pita, M., & Rocha, L. M. (2013). Canalization and control in automata networks: Body segmentation in Drosophila melanogaster. PLoS One, 8(3), e55946.

    Article  Google Scholar 

  • Morel, C. M., Serruya, S. J., Penna, G. O., & Guimaraes, R. (2009). Co-authorship network analysis: A powerful tool for strategic planning of research, development and capacity building programs on neglected diseases. PLoS, 3(8), e501.

    Google Scholar 

  • Newman, M. E. J. (2001). The structure of scientific collaboration networks. PNAS, 98(2), 404–409.

    Article  MATH  Google Scholar 

  • Newman, M. E. J. (2004). Co-authorship networks and patterns of scientific collaboration. PNAS, 101, 5200–5205.

    Article  Google Scholar 

  • Nooy, D. W., Mrvar, A., & Batagelj, V. (2005). Exploratory social network analysis with Pajek. New York: Cambridge University Press.

    Book  Google Scholar 

  • Patra, D., Mishra, A. K. (2006). Chemical and biochemical fluorescence sensors encyclopedia of sensors. In C. A. Grimes, E. C. Dickey & M. V. Pishko (Eds.) (Vol. 2, pp. 139–156). CA, USA: American Scientific Publishers.

  • Perez-Iratxeta, C., Andrade-Navarro, M. A., & Wren, J. D. (2007). Evolving research trends in bioinformatics. Brief Bioinform, 8(2), 88–95.

    Article  Google Scholar 

  • Perry, C. A., & Rice, R. E. (1998). Scholarly communication in developmental dyslexia: Influence of network structure on change in a hybrid problem Area. JASIST, 49, 151–168.

    Article  Google Scholar 

  • Persson, O. (2001). All author citations vs first author citations. Scientometrics, 50, 339–344.

    Article  Google Scholar 

  • Price, D., & Beaver, D. (1966). Collaboration in an invisible college. American Psychologist, 21, 1011–1018.

    Article  Google Scholar 

  • Rousseau, R., & Zuccala, A. (2004). A classification of author cocitations: Definitions and search strategies. JASIST, 55, 513–529.

    Article  Google Scholar 

  • Sade, D. S. (1989). Sociometrics of Macaca Mulatta III: N-path centrality in grooming networks. Social Networks, 11, 273–292.

    Article  MathSciNet  Google Scholar 

  • Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between publications. JASIST, 24, 265–269.

    Article  Google Scholar 

  • Smith, M. (1958). The trend toward multiple authorship in psychology. American Psychologist, 13, 596–599.

    Article  Google Scholar 

  • Song, M., Kim, S. Y., Zhang, G., Ding, Y., & Chamber, T. (2013a). Productivity and influence in bioinformatics: A bibliometric analysis using PubMed Central. JASIST, 65(2), 352–371.

    Google Scholar 

  • Song, M., Yang, C. C., & Tang, X. (2013b). Detecting evolution of bioinformatics with a content and co-authorship analysis. Springerplus, 2(1), 186.

    Article  MathSciNet  Google Scholar 

  • The DBLP Website. Retrieved August 18, 2013 from http://www.informatik.uni-trier.de/~ley/db/.

  • The PubMed Central Website. Retrieved August 18, 2013 from http://www.ncbi.nlm.nih.gov/pmc/.

  • The Web of Science. Retrieved August 18, 2013 from http://thomsonreuters.com/web-of-science/.

  • Velden, T., Haque, A., & Lagoze, C. (2009). A new approach to analyzing patterns of collaboration in co-authorship networks—Mesoscopic analysis and interpretation. In Proceedings of ISSI ‘09 (pp. 14–17).

  • Wasserman, S., & Katherine, F. (1994). Social network analysis: Methods and applications. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Watts, D. J., & Strogatz, S. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393(6684), 440–442.

    Article  Google Scholar 

  • White, H. D. (2003a). Author cocitation analysis and Pearson’s R. JASIST, 54(13), 1250–1259.

    Article  Google Scholar 

  • White, H. D. (2003b). Pathfinder network and author cocitation analysis: A remapping of paradigmatic information scientists. JASIST, 54(5), 423–434.

    Article  Google Scholar 

  • White, H. D., & Griffith, B. C. (1981). Author cocitation: A literature measure of intellectual structure. JASIST, 32, 163–172.

    Article  Google Scholar 

  • White, H. D., Wellman, B., & Nazer, N. (2004). Does citation reflect social structure? JASIST, 55(2), 111–126.

    Article  Google Scholar 

  • Zhao, D. (2006). Towards all-author co-citation analysis. Information Processing and Management, 42, 1578–1591.

    Article  Google Scholar 

  • Zizi, M., & Beaudouin-Lafon, M. (1994). Accessing hyperdocuments through interactive dynamic map. In Proceedings of the 1994 ACM European conference on Hypermedia technology (pp. 126–135).

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Acknowledgments

This research was supported by the Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Ministry of Science, ICT & Future Planning (Grant No. 2013M3A9C4078138).

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Correspondence to Min Song.

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Kim, M.C., Jeong, Y.K. & Song, M. Investigating the integrated landscape of the intellectual topology of bioinformatics. Scientometrics 101, 309–335 (2014). https://doi.org/10.1007/s11192-014-1417-1

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