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Analyzing topic evolution in bioinformatics: investigation of dynamics of the field with conference data in DBLP

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Listed:
  • Min Song

    (Yonsei University)

  • Go Eun Heo

    (Yonsei University)

  • Su Yeon Kim

    (Yonsei University)

Abstract

In this paper we analyze topic evolution over time within bioinformatics to uncover the underlying dynamics of that field, focusing on the recent developments in the 2000s. We select 33 bioinformatics related conferences indexed in DBLP from 2000 to 2011. The major reason for choosing DBLP as the data source instead of PubMed is that DBLP retains most bioinformatics related conferences, and to study dynamics of the field, conference papers are more suitable than journal papers. We divide a period of a dozen years into four periods: period 1 (2000–2002), period 2 (2003–2005), period 3 (2006–2008) and period 4 (2009–2011). To conduct topic evolution analysis, we employ three major procedures, and for each procedure, we develop the following novel technique: the Markov Random Field-based topic clustering, automatic cluster labeling, and topic similarity based on Within-Period Cluster Similarity and Between-Period Cluster Similarity. The experimental results show that there are distinct topic transition patterns between different time periods. From period 1 to period 3, new topics seem to have emerged and expanded, whereas from period 3 to period 4, topics are merged and display more rigorous interaction with each other. This trend is confirmed by the collaboration pattern over time.

Suggested Citation

  • Min Song & Go Eun Heo & Su Yeon Kim, 2014. "Analyzing topic evolution in bioinformatics: investigation of dynamics of the field with conference data in DBLP," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 397-428, October.
  • Handle: RePEc:spr:scient:v:101:y:2014:i:1:d:10.1007_s11192-014-1246-2
    DOI: 10.1007/s11192-014-1246-2
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    5. Qian, Yue & Liu, Yu & Sheng, Quan Z., 2020. "Understanding hierarchical structural evolution in a scientific discipline: A case study of artificial intelligence," Journal of Informetrics, Elsevier, vol. 14(3).
    6. Sjögårde, Peter & Ahlgren, Per, 2018. "Granularity of algorithmically constructed publication-level classifications of research publications: Identification of topics," Journal of Informetrics, Elsevier, vol. 12(1), pages 133-152.
    7. Kai Hu & Kunlun Qi & Siluo Yang & Shengyu Shen & Xiaoqiang Cheng & Huayi Wu & Jie Zheng & Stephen McClure & Tianxing Yu, 2018. "Identifying the “Ghost City” of domain topics in a keyword semantic space combining citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 1141-1157, March.
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