ArticleDatabase : the journal of biological databases and curation2018
Extracting chemical-protein relations using attention-based neural networks.
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 15 papers.
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Who cites it
15 citing papers in PubMed.
- A comprehensive large scale biomedical knowledge graph for AI powered data driven biomedical research.bioRxiv : the preprint server for biology · 2025Article
- A framework for integrating biomedical knowledge in Wikidata with open biological and biomedical ontologies and MeSH keywords.Heliyon · 2024Article
- Prompt Tuning in Biomedical Relation Extraction.Journal of healthcare informatics research · 2024Article
- End-to-End Models for Chemical-Protein Interaction Extraction: Better Tokenization and Span-Based Pipeline Strategies.Proceedings. IEEE International Conference on Healthcare Informatics · 2023Article
- Relation Extraction in Biomedical Texts Based on Multi-Head Attention Model With Syntactic Dependency Feature: Modeling Study.JMIR medical informatics · 2022Article
- Expanding a database-derived biomedical knowledge graph via multi-relation extraction from biomedical abstracts.BioData mining · 2022Article
- A Syntactic Information-Based Classification Model for Medical Literature: Algorithm Development and Validation Study.JMIR medical informatics · 2022Article
- Deep semi-supervised learning ensemble framework for classifying co-mentions of human proteins and phenotypes.BMC bioinformatics · 2021Article
- Extraction of causal relations based on SBEL and BERT model.Database : the journal of biological databases and curation · 2021Article
- Delineation of ischemic lesion from brain MRI using attention gated fully convolutional network.Biomedical engineering letters · 2021Article
- CGINet: graph convolutional network-based model for identifying chemical-gene interaction in an integrated multi-relational graph.BMC bioinformatics · 2020Article
- A Graph Convolutional Network-Based Method for Chemical-Protein Interaction Extraction: Algorithm Development.JMIR medical informatics · 2020Article
- Protein Interaction Network Reconstruction Through Ensemble Deep Learning With Attention Mechanism.Frontiers in bioengineering and biotechnology · 2020Article
- Chemical-protein interaction extraction via contextualized word representations and multihead attention.Database : the journal of biological databases and curation · 2019Article
- Extraction of chemical-protein interactions from the literature using neural networks and narrow instance representation.Database : the journal of biological databases and curation · 2019Article
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7 authors.
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Abstract
Relation extraction is an important task in the field of natural language processing. In this paper, we describe our approach for the BioCreative VI Task 5: text mining chemical-protein interactions. We investigate multiple deep neural network (DNN) models, including convolutional neural networks, recurrent neural networks (RNNs) and attention-based (ATT-) RNNs (ATT-RNNs) to extract chemical-protein relations. Our experimental results indicate that ATT-RNN models outperform the same models without using attention and the ATT-gated recurrent unit (ATT-GRU) achieves the best performing micro average F1 score of 0.527 on the test set among the tested DNNs. In addition, the result of word-level attention weights also shows that attention mechanism is effective on selecting the most important trigger words when trained with semantic relation labels without the need of semantic parsing and feature engineering. The source code of this work is available at https://github.com/ohnlp/att-chemprot.
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