Visualizing the knowledge structure and evolution of bioinformatics

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.

Trends in research publication topics related to eco enzymes with bibliometric analysis

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Resistin as a potential diagnostic biomarker for sepsis: insights from DIA and ELISA analyses

“…Utilizing specialized software, the interconnections and interactions between various proteins in the network can be described, enabling the prediction of potential core biomarkers. This integrated approach, encompassing GO, KEGG, and PPI analyses, enhances our ability to unravel the intricate relationships and functionalities within molecular systems [ 8 – 10 ].…”