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

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.