TopicTracker – An advanced software pipeline for text mining on PubMed data: Bridging the gap between off-the-shelf tools and code based approaches
TopicTracker represents a significant advancement in academic research tools for text mining. While it has its limitations, primarily tied to its alignment with PubMed, its benefits far outweigh the constraints. As the landscape of research continues to expand, tools like TopicTracker may be instrumental in guiding scholars in their pursuit of knowledge, ensuring they navigate the large amount of literature with clarity and precision.
Authors
- Giovanni Spitale (ORCID: https://orcid.org/0000-0002-6812-0979)
- Federico Germani (ORCID: https://orcid.org/0000-0002-5604-0437)
- Nikola Biller‐Andorno (ORCID: https://orcid.org/0000-0001-7661-1324)
Institutions
- University of Zurich (CH)
- Institute for Biomedical Engineering (CH)
Publication Details
- Journal
- Heliyon
- Published
- 2024-08-15
- DOI
- https://doi.org/10.1016/j.heliyon.2024.e36351
- Citations
- 3
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 0.70