Biomedical relation extraction method based on ensemble learning and attention mechanism
SARE offers a promising solution for improving the accuracy and efficiency of relation extraction tasks in biomedical research, facilitating advancements in biomedical informatics. The results suggest that combining ensemble learning with attention mechanisms is effective for extracting complex relationships from biomedical texts. Our code and data are publicly available at: https://github.com/GS233/Biomedical .
Authors
- Zuo‐Lin Xiang (ORCID: https://orcid.org/0000-0002-5725-842X)
- Li’an Zhu (ORCID: https://orcid.org/0000-0001-9657-9341)
- Haoyang Wang (ORCID: https://orcid.org/0000-0003-2187-9205)
- Yaxun Jia (ORCID: https://orcid.org/0000-0001-5808-4997)
- Zhu Yuan (ORCID: https://orcid.org/0000-0001-5950-4121)
Institutions
- Shanghai East Hospital (CN)
- Criminal Investigation Police University of China (CN)
- Beijing Information Science & Technology University (CN)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2024-10-18
- DOI
- https://doi.org/10.1186/s12859-024-05951-y
- Citations
- 9
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 2.10
Funders
- National Natural Science Foundation of China
- Tongji University
- Shanghai Pudong New Area Health Commission