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Detecting evolution of bioinformatics with a content and co-authorship analysis

Overview of attention for article published in SpringerPlus, April 2013
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23 Mendeley
Detecting evolution of bioinformatics with a content and co-authorship analysis
Published in
SpringerPlus, April 2013
DOI 10.1186/2193-1801-2-186
Pubmed ID

Min Song, Christopher C Yang, Xuning Tang


Bioinformatics is an interdisciplinary research field that applies advanced computational techniques to biological data. Bibliometrics analysis has recently been adopted to understand the knowledge structure of a research field by citation pattern. In this paper, we explore the knowledge structure of Bioinformatics from the perspective of a core open access Bioinformatics journal, BMC Bioinformatics with trend analysis, the content and co-authorship network similarity, and principal component analysis. Publications in four core journals including Bioinformatics - Oxford Journal and four conferences in Bioinformatics were harvested from DBLP. After converting publications into TF-IDF term vectors, we calculate the content similarity, and we also calculate the social network similarity based on the co-authorship network by utilizing the overlap measure between two co-authorship networks. Key terms is extracted and analyzed with PCA, visualization of the co-authorship network is conducted. The experimental results show that Bioinformatics is fast-growing, dynamic and diversified. The content analysis shows that there is an increasing overlap among Bioinformatics journals in terms of topics and more research groups participate in researching Bioinformatics according to the co-authorship network similarity.

Mendeley readers

The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 4%
United States 1 4%
Unknown 21 91%

Demographic breakdown

Readers by professional status Count As %
Librarian 4 17%
Student > Ph. D. Student 4 17%
Student > Master 3 13%
Other 2 9%
Lecturer 2 9%
Other 5 22%
Unknown 3 13%
Readers by discipline Count As %
Computer Science 6 26%
Social Sciences 5 22%
Biochemistry, Genetics and Molecular Biology 2 9%
Arts and Humanities 2 9%
Agricultural and Biological Sciences 2 9%
Other 3 13%
Unknown 3 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 April 2013.
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