SciLinker: a large-scale text mining framework for mapping associations among biological entities
SciLinker represents a novel text mining approach that extracts and quantifies associations between biomedical entities through co-occurrence analysis and relationship extraction from PubMed abstracts. Its modular design enables expansion to additional entities and text corpora, making it a versatile tool for transforming unstructured biomedical data into actionable insights for drug discovery.
Authors
- Khader Shameer (ORCID: https://orcid.org/0000-0002-9982-1911)
- Franck Rapaport (ORCID: https://orcid.org/0000-0001-6553-2110)
- Dongyu Liu (ORCID: https://orcid.org/0009-0008-2997-5967)
- C. G. Ames (ORCID: https://orcid.org/0000-0002-2126-4246)
Institutions
- Target (United States) (US)
- Sanofi (United States) (US)
Publication Details
- Journal
- Frontiers in Artificial Intelligence
- Published
- 2025-03-19
- DOI
- https://doi.org/10.3389/frai.2025.1528562
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 0.00