ArticleDatabase : the journal of biological databases and curation2017
Chemical-induced disease relation extraction via convolutional neural network.
Article in Database : the journal of biological databases and curation, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed, 101 citations in OpenAlex.
- A comprehensive large scale biomedical knowledge graph for AI powered data driven biomedical research.bioRxiv : the preprint server for biology · 2025Article
- Surveying biomedical relation extraction: a critical examination of current datasets and the proposal of a new resource.Briefings in bioinformatics · 2024Review
- LitCovid ensemble learning for COVID-19 multi-label classification.Database : the journal of biological databases and curation · 2022Article
- A BERT-based ensemble learning approach for the BioCreative VII challenges: full-text chemical identification and multi-label classification in PubMed articles.Database : the journal of biological databases and curation · 2022Article
- Exploiting document graphs for inter sentence relation extraction.Journal of biomedical semantics · 2022Article
- An annotated dataset for extracting gene-melanoma relations from scientific literature.Journal of biomedical semantics · 2022Article
- CID-GCN: An Effective Graph Convolutional Networks for Chemical-Induced Disease Relation Extraction.Frontiers in genetics · 2021Article
- Document-Level Biomedical Relation Extraction Using Graph Convolutional Network and Multihead Attention: Algorithm Development and Validation.JMIR medical informatics · 2020Article
- Document-Level Biomedical Relation Extraction Leveraging Pretrained Self-Attention Structure and Entity Replacement: Algorithm and Pretreatment Method Validation Study.JMIR medical informatics · 2020Article
- Exploiting sequence labeling framework to extract document-level relations from biomedical texts.BMC bioinformatics · 2020Article
- Chemical-induced disease relation extraction via attention-based distant supervision.BMC bioinformatics · 2019Article
- Knowledge-guided convolutional networks for chemical-disease relation extraction.BMC bioinformatics · 2019Article
- A general approach for improving deep learning-based medical relation extraction using a pre-trained model and fine-tuning.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
- A document level neural model integrated domain knowledge for chemical-induced disease relations.BMC bioinformatics · 2018Article
- Chemical-induced disease extraction via recurrent piecewise convolutional neural networks.BMC medical informatics and decision making · 2018Article
- Opportunities and obstacles for deep learning in biology and medicine.Journal of the Royal Society, Interface · 2018Review
- Extracting chemical-protein relations with ensembles of SVM and deep learning models.Database : the journal of biological databases and curation · 2018Article
- LPTK: a linguistic pattern-aware dependency tree kernel approach for the BioCreative VI CHEMPROT task.Database : the journal of biological databases and curation · 2018Article
- Extracting chemical-protein relations using attention-based neural networks.Database : the journal of biological databases and curation · 2018Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This article describes our work on the BioCreative-V chemical-disease relation (CDR) extraction task, which employed a maximum entropy (ME) model and a convolutional neural network model for relation extraction at inter- and intra-sentence level, respectively. In our work, relation extraction between entity concepts in documents was simplified to relation extraction between entity mentions. We first constructed pairs of chemical and disease mentions as relation instances for training and testing stages, then we trained and applied the ME model and the convolutional neural network model for inter- and intra-sentence level, respectively. Finally, we merged the classification results from mention level to document level to acquire the final relations between chemical and disease concepts. The evaluation on the BioCreative-V CDR corpus shows the effectiveness of our proposed approach. Database URL: http://www.biocreative.org/resources/corpora/biocreative-v-cdr-corpus/.
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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.