利用文献计量学新方法探索生物学研究热点

IF 3.1 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry Pub Date : 2025-09-09 DOI:10.1016/j.compbiolchem.2025.108680

Shan Chen , Junsha Wang , Xinyu Huang , Kailin Chen , Limei Fu , Yuanzhao Ding

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引用次数: 0

摘要

生物学研究是一个重要的研究领域，深刻地影响着人类生活的方方面面。本研究的目的是利用一种创新的文献计量分析方法来了解当前生物学的研究热点和未来趋势。这种新颖的文献计量分析方法，基于R编程语言，提供了一种与传统VOSviewer完全不同的方法，提供了更深入的分析。基于文献计量分析结果，本文还提出了未来可能的发展方向，即将大数据与机器学习相结合。通过将现有数据整合到大型数据库中，然后训练模型，这种方法可以为未来提供深刻的见解和准确的预测。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Exploring biological research hotspots through a novel bibliometric approach

Biological research is a crucial field of study, profoundly impacting every aspect of human life. The objective of this study is to utilize an innovative bibliometric analysis method to understand current research hotspots and future trends in biology. This novel bibliometric analysis method, based on the R programming language, offers a completely different approach than traditional VOSviewer, providing a more in-depth analysis. Based on the bibliometric analysis results, this paper also proposes potential future developments, namely, integrating big data with machine learning. By integrating existing data into large databases and then training models, this approach can provide deep insights and accurate predictions for the future.

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来源期刊

Computational Biology and Chemistry 生物-计算机：跨学科应用

CiteScore

6.10

自引率

3.20%

发文量

142

审稿时长

24 days

期刊介绍： Computational Biology and Chemistry publishes original research papers and review articles in all areas of computational life sciences. High quality research contributions with a major computational component in the areas of nucleic acid and protein sequence research, molecular evolution, molecular genetics (functional genomics and proteomics), theory and practice of either biology-specific or chemical-biology-specific modeling, and structural biology of nucleic acids and proteins are particularly welcome. Exceptionally high quality research work in bioinformatics, systems biology, ecology, computational pharmacology, metabolism, biomedical engineering, epidemiology, and statistical genetics will also be considered. Given their inherent uncertainty, protein modeling and molecular docking studies should be thoroughly validated. In the absence of experimental results for validation, the use of molecular dynamics simulations along with detailed free energy calculations, for example, should be used as complementary techniques to support the major conclusions. Submissions of premature modeling exercises without additional biological insights will not be considered. Review articles will generally be commissioned by the editors and should not be submitted to the journal without explicit invitation. However prospective authors are welcome to send a brief (one to three pages) synopsis, which will be evaluated by the editors.