A meta-contrastive learning approach for clinical drug-drug interaction extraction from biomedical literature.
Uložené v:
| Názov: | A meta-contrastive learning approach for clinical drug-drug interaction extraction from biomedical literature. |
|---|---|
| Autori: | Jia Y; Department of Radiation Oncology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China., Yuan Z; Department of Information Management, The National Police University for Criminal Justice, Baoding, China., Zhu L; Department of Radiation Oncology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China., Xiang ZL; Department of Radiation Oncology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China.; Department of Radiation Oncology, Shanghai East Hospital Ji'an Hospital, Jian, China. |
| Zdroj: | PLoS computational biology [PLoS Comput Biol] 2025 Dec 05; Vol. 21 (12), pp. e1013722. Date of Electronic Publication: 2025 Dec 05 (Print Publication: 2025). |
| Spôsob vydávania: | Journal Article |
| Jazyk: | English |
| Informácie o časopise: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101238922 Publication Model: eCollection Cited Medium: Internet ISSN: 1553-7358 (Electronic) Linking ISSN: 1553734X NLM ISO Abbreviation: PLoS Comput Biol Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: San Francisco, CA : Public Library of Science, [2005]- |
| Výrazy zo slovníka MeSH: | Machine Learning* , Data Mining*/methods, Drug Interactions ; Humans ; Computational Biology/methods ; Pharmacovigilance ; Drug-Related Side Effects and Adverse Reactions ; Algorithms ; Databases, Factual |
| Abstrakt: | Drug-drug interactions (DDIs) are a significant source of adverse drug events and pose critical challenges to patient safety and clinical decision-making. Extracting DDIs from biomedical literature plays an essential role in pharmacovigilance, yet remains difficult due to data sparsity and high annotation costs. This study presents BioMCL-DDI, a novel few-shot learning framework that integrates meta-learning with contrastive embedding strategies to enable efficient DDI extraction under limited supervision. BioMCL-DDI jointly optimizes prototype-based classification and supervised contrastive representation learning within a unified architecture. The model captures both intra-class compactness and inter-class separability, enhancing its generalization in sparse biomedical settings. We evaluate BioMCL-DDI on three benchmark datasets: DDI-2013, DrugBank, and the more recent TAC 2018 DDI Extraction corpus. The model achieves F1 scores of 87.80% on DDI-2013, 86.00% on DrugBank, and 74.85%/74.82% on the two official test sets of TAC 2018, consistently outperforming competitive baselines. Our model significantly outperforms state-of-the-art baselines in low-resource scenarios. BioMCL-DDI provides a scalable and effective solution for DDI extraction from biomedical texts, with strong potential for integration into clinical decision support systems and biomedical knowledge bases. All our code and data have been publicly released at: https://github.com/Hero-Legend/BioMCL-DDI. (Copyright: © 2025 Jia et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.) |
| Entry Date(s): | Date Created: 20251205 Date Completed: 20251205 Latest Revision: 20251205 |
| Update Code: | 20251206 |
| DOI: | 10.1371/journal.pcbi.1013722 |
| PMID: | 41348931 |
| Databáza: | MEDLINE |
Full Text 
Full Text Finder 
Nájsť tento článok vo Web of Science Buďte prvý, kto okomentuje tento záznam!