ArticleDatabase : the journal of biological databases and curation2018
Extracting chemical-protein relations with ensembles of SVM and deep learning models.
Article in Database : the journal of biological databases and curation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- LitTx: A New Treatment Relation Extraction Dataset.LREC ... International Conference on Language Resources & Evaluation : [proceedings]. International Conference on Language Resources & Evaluation · 2026Article
- Integrating text mining and knowledge graph to enhance biopharmaceutical process optimization.PloS one · 2026Article
- Review
- A framework for integrating biomedical knowledge in Wikidata with open biological and biomedical ontologies and MeSH keywords.Heliyon · 2024Article
- Optimizing protein sequence classification: integrating deep learning models with Bayesian optimization for enhanced biological analysis.BMC medical informatics and decision making · 2024Article
- Document-level biomedical relation extraction via hierarchical tree graph and relation segmentation module.Bioinformatics (Oxford, England) · 2024Article
- Prompt Tuning in Biomedical Relation Extraction.Journal of healthcare informatics research · 2024Article
- Retrieval augmented scientific claim verification.JAMIA open · 2024Article
- Extracting Drug-Protein Relation from Literature Using Ensembles of Biomedical Transformers.Studies in health technology and informatics · 2024Article
- BioEGRE: a linguistic topology enhanced method for biomedical relation extraction based on BioELECTRA and graph pointer neural network.BMC bioinformatics · 2023Article
- Overview of DrugProt task at BioCreative VII: data and methods for large-scale text mining and knowledge graph generation of heterogenous chemical-protein relations.Database : the journal of biological databases and curation · 2023Article
- Article
- End-to-EndProceedings. IEEE International Conference on Healthcare Informatics · 2023Article
- End-to-End Models for Chemical-Protein Interaction Extraction: Better Tokenization and Span-Based Pipeline Strategies.Proceedings. IEEE International Conference on Healthcare Informatics · 2023Article
- Entity relation extraction in the medical domain: based on data augmentation.Annals of translational medicine · 2022Article
- A sequence labeling framework for extracting drug-protein relations from biomedical literature.Database : the journal of biological databases and curation · 2022Article
- An annotated dataset for extracting gene-melanoma relations from scientific literature.Journal of biomedical semantics · 2022Article
- DTI-BERT: Identifying Drug-Target Interactions in Cellular Networking Based on BERT and Deep Learning Method.Frontiers in genetics · 2022Article
- Deep semi-supervised learning ensemble framework for classifying co-mentions of human proteins and phenotypes.BMC bioinformatics · 2021Article
- The risk of racial bias while tracking influenza-related content on social media using machine learning.Journal of the American Medical Informatics Association : JAMIA · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Mining relations between chemicals and proteins from the biomedical literature is an increasingly important task. The CHEMPROT track at BioCreative VI aims to promote the development and evaluation of systems that can automatically detect the chemical-protein relations in running text (PubMed abstracts). This work describes our CHEMPROT track entry, which is an ensemble of three systems, including a support vector machine, a convolutional neural network, and a recurrent neural network. Their output is combined using majority voting or stacking for final predictions. Our CHEMPROT system obtained 0.7266 in precision and 0.5735 in recall for an F-score of 0.6410 during the challenge, demonstrating the effectiveness of machine learning-based approaches for automatic relation extraction from biomedical literature and achieving the highest performance in the task during the 2017 challenge.Database URL: http://www.biocreative.org/tasks/biocreative-vi/track-5/.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.