颜林林 (2022-10-02 15:26):
#paper doi:10.1186/s12859-022-04948-9 BMC Bioinformatics, 2022, Visualizing the knowledge structure and evolution of bioinformatics. 这篇文章用了一些生物信息学中常用的数据分析方法和可视化方法,来研究生物信息学学科本身。对过去几十年所发表的相关论文摘要文本的分析,展示了一些研究模式变迁过程(如从纯理论性的模型计算到堆机器学习模型)以及相应的知识结构的变化过程。思路上很新颖,正文中以UMAP点图展示知识结构的方式也很有创意。
IF:2.900Q1 BMC bioinformatics, 2022-Sep-30. DOI: 10.1186/s12859-022-04948-9 PMID: 36180852
Visualizing the knowledge structure and evolution of bioinformatics
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Abstract:
BACKGROUND: Bioinformatics has gained much attention as a fast growing interdisciplinary field. Several attempts have been conducted to explore the field of bioinformatics by bibliometric analysis, however, such works did not elucidate the role of visualization in analysis, nor focus on the relationship between sub-topics of bioinformatics.RESULTS: First, the hotspot of bioinformatics has moderately shifted from traditional molecular biology to omics research, and the computational method has also shifted from mathematical model to data mining and machine learning. Second, DNA-related topics are bridge topics in bioinformatics research. These topics gradually connect various sub-topics that are relatively independent at first. Third, only a small part of topics we have obtained involves a number of computational methods, and the other topics focus more on biological aspects. Fourth, the proportion of computing-related topics hit a trough in the 1980s. During this period, the use of traditional calculation methods such as mathematical model declined in a large proportion while the new calculation methods such as machine learning have not been applied in a large scale. This proportion began to increase gradually after the 1990s. Fifth, although the proportion of computing-related topics is only slightly higher than the original, the connection between other topics and computing-related topics has become closer, which means the support of computational methods is becoming increasingly important for the research of bioinformatics.CONCLUSIONS: The results of our analysis imply that research on bioinformatics is becoming more diversified and the ranking of computational methods in bioinformatics research is also gradually improving.
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