Abstract Purpose This study explores the underlying research topics regarding CRISPR based on the LDA model and figures out trends in knowledge transfer from science to technology in this area over the latest 10 years. Design/methodology/approach We collected publications on CRISPR between 2011 and 2020 from the Web of Science, and traced all the patents citing them from lens.org. 15,904 articles and 18,985 patents in total are downloaded and analyzed. The LDA model was applied to identify underlying research topics in related research. In addition, some indicators were introduced to measure the knowledge transfer from research topics of scientific publications to IPC-4 classes of patents. Findings The emerging research topics on CRISPR were identified and their evolution over time displayed. Furthermore, a big picture of knowledge transition from research topics to technological classes of patents was presented. We found that for all topics on CRISPR, the average first transition year, the ratio of articles cited by patents, the NPR transition rate are respectively 1.08, 15.57%, and 1.19, extremely shorter and more intensive than those of general fields. Moreover, the transition patterns are different among research topics. Research limitations Our research is limited to publications retrieved from the Web of Science and their citing patents indexed in lens.org. A limitation inherent with LDA analysis is in the manual interpretation and labeling of “topics”. Practical implications Our study provides good references for policy-makers on allocating scientific resources and regulating financial budgets to face challenges related to the transformative technology of CRISPR. Originality/value The LDA model here is applied to topic identification in the area of transformative researches for the first time, as exemplified on CRISPR. Additionally, the dataset of all citing patents in this area helps to provide a full picture to detect the knowledge transition between S&T.

中文翻译：

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基于LDA模型的CRISPR研究前沿进展与知识转移

摘要 目的 本研究基于 LDA 模型探索有关 CRISPR 的潜在研究主题，并找出该领域最近 10 年从科学到技术的知识转移趋势。设计/方法/途径 我们从 Web of Science 收集了 2011 年至 2020 年间关于 CRISPR 的出版物，并从 lens.org 追踪了所有引用它们的专利。共有15,904篇文章和18,985项专利被下载和分析。LDA模型用于识别相关研究中的潜在研究主题。此外，还引入了一些指标来衡量从科学出版物的研究主题到 IPC-4 专利类别的知识转移。研究结果 确定了有关 CRISPR 的新兴研究主题，并展示了它们随时间的演变。此外，还展示了从研究主题到专利技术类别的知识转变的大图。我们发现，对于 CRISPR 上的所有主题，平均第一个转换年、专利引用的文章比例、NPR 转换率分别为 1.08、15.57% 和 1.19，比一般领域的转换时间更短、更密集。此外，研究主题之间的过渡模式也不同。研究限制 我们的研究仅限于从 Web of Science 检索到的出版物及其在 lens.org 中索引的施引专利。LDA 分析固有的局限性在于“主题”的手动解释和标记。实际意义 我们的研究为政策制定者分配科学资源和调节财政预算以应对与 CRISPR 变革技术相关的挑战提供了很好的参考。原创性/价值 这里的 LDA 模型首次应用于变革性研究领域的主题识别，例如 CRISPR。此外，该领域所有施引专利的数据集有助于提供全貌，以检测科技之间的知识转移。实际意义 我们的研究为政策制定者分配科学资源和调节财政预算以应对与 CRISPR 变革技术相关的挑战提供了很好的参考。原创性/价值 这里的 LDA 模型首次应用于变革性研究领域的主题识别，例如 CRISPR。此外，该领域所有施引专利的数据集有助于提供全貌，以检测科技之间的知识转移。实际意义 我们的研究为政策制定者分配科学资源和调节财政预算以应对与 CRISPR 变革技术相关的挑战提供了很好的参考。原创性/价值 这里的 LDA 模型首次应用于变革性研究领域的主题识别，例如 CRISPR。此外，该领域所有施引专利的数据集有助于提供全貌，以检测科技之间的知识转移。