Abstract
The subjects of literature are the direct expression of the author’s research results. Mining valuable knowledge helps to save time for the readers to understand the content and direction of the literature quickly. Therefore, the co-occurrence network of high-frequency words in the bioinformatics literature and its structural characteristics and evolution were analysed in this paper. First, 242,891 articles from 47 top bioinformatics periodicals were chosen as the object of the study. Second, the co-occurrence relationship among high-frequency words of these articles was analysed by word segmentation and high-frequency word selection. Then, a co-occurrence network of high-frequency words in bioinformatics literature was built. Finally, the conclusions were drawn by analysing its structural characteristics and evolution. The results showed that the co-occurrence network of high-frequency words in the bioinformatics literature was a small-world network with scale-free distribution, rich-club phenomenon and disassortative matching characteristics. At the same time, the high-frequency words used by authors changed little in 2–3 years but varied greatly in four years because of the influence of the state-of-the-art technology.
Highlights
Biotechnology science has led to biological information research in twenty-first century
Biological research results and findings are recorded in various forms of literature [1]
The results showed that any two high-frequency bioinformatics literature had the same level of average path length and higher level of clustering words of bioinformatics literature were connected at most by another high-frequency word
Summary
Biotechnology science has led to biological information research in twenty-first century. Biological information improves the level of biological intelligence with the help of information technology. A large number of related articles have been published in this field as it is one of the most concerned research areas. Biological research results and findings are recorded in various forms of literature [1]. How to quickly collect the literature and use knowledge discovery and data mining methods to identify future research hotspots has become an urgent issue in scientific investigations. Many countries carry a high cancer burden, and comprehensive cancer nursing has become increasingly complicated and difficult [1]. Segregating the vast number of existing articles will help to identify the cause and effect of cancer and achieve the goal of preventing cancer
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