Navigating the frontier of synthetic biology: an AI-driven analytics platform for exploring research trends and relationships

Felix Meier, Thom Dixon, Tom Williams, Ian Paulsen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

The field of synthetic biology has experienced rapid growth in recent years, leading to an overwhelming amount of literature that can make it difficult to comprehend the scope and trends of the discipline. In this study, we employ topic modeling to comprehensively map research topics within synthetic biology, revealing subtopics and their relationships, as well as trends over time. We utilize metadata to identify the most significant journals and countries in the field and discuss potential policy impact on the research output. In addition, we investigate co-authorship networks to analyze collaborations among authors, institutions, and countries. We believe that our findings could serve as a valuable resource for gaining a deeper understanding of synthetic biology and provide a foundation for analyzing other disciplines.

Original languageEnglish
Pages (from-to)3229-3241
Number of pages13
JournalACS Synthetic Biology
Volume12
Issue number11
Early online date30 Aug 2023
DOIs
Publication statusPublished - 17 Nov 2023

Keywords

  • metabolic engineering
  • natural language processing
  • network analysis
  • synthetic biology
  • topic modelling

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