ArticleDatabase : the journal of biological databases and curation2016
CD-REST: a system for extracting chemical-induced disease relation in literature.
Article in Database : the journal of biological databases and curation, 2016. 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.
- Enhancing biomedical relation extraction with directionality.Bioinformatics (Oxford, England) · 2025Article
- A framework for integrating biomedical knowledge in Wikidata with open biological and biomedical ontologies and MeSH keywords.Heliyon · 2024Article
- PubTator 3.0: an AI-powered literature resource for unlocking biomedical knowledge.Nucleic acids research · 2024Article
- A metric learning-based method for biomedical entity linking.Frontiers in research metrics and analytics · 2023Article
- Identification of Chemical-Disease Associations Through Integration of Molecular Fingerprint, Gene Ontology and Pathway Information.Interdisciplinary sciences, computational life sciences · 2022Article
- Biomedical relation extraction via knowledge-enhanced reading comprehension.BMC bioinformatics · 2022Article
- The precision medicine process for treating rare disease using the artificial intelligence tool mediKanren.Frontiers in artificial intelligence · 2022Article
- Relation Classification for Bleeding Events From Electronic Health Records Using Deep Learning Systems: An Empirical Study.JMIR medical informatics · 2021Article
- Document-Level Biomedical Relation Extraction Using Graph Convolutional Network and Multihead Attention: Algorithm Development and Validation.JMIR medical informatics · 2020Article
- Extraction of Information Related to Drug Safety Surveillance From Electronic Health Record Notes: Joint Modeling of Entities and Relations Using Knowledge-Aware Neural Attentive Models.JMIR medical informatics · 2020Article
- Broad-coverage biomedical relation extraction with SemRep.BMC bioinformatics · 2020Article
- Exploiting sequence labeling framework to extract document-level relations from biomedical texts.BMC bioinformatics · 2020Article
- Identifying relations of medications with adverse drug events using recurrent convolutional neural networks and gradient boosting.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Adverse drug reaction detection via a multihop self-attention mechanism.BMC bioinformatics · 2019Article
- Fine-Tuning Bidirectional Encoder Representations From Transformers (BERT)-Based Models on Large-Scale Electronic Health Record Notes: An Empirical Study.JMIR medical informatics · 2019Article
- Comparing drug safety of hepatitis C therapies using post-market data.BMC medical informatics and decision making · 2019Article
- Chemical-induced disease relation extraction via attention-based distant supervision.BMC bioinformatics · 2019Article
- An investigation of single-domain and multidomain medication and adverse drug event relation extraction from electronic health record notes using advanced deep learning models.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Knowledge-guided convolutional networks for chemical-disease relation extraction.BMC bioinformatics · 2019Article
- Hierarchical sequence labeling for extracting BEL statements from biomedical literature.BMC medical informatics and decision making · 2019Article
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Authors and funding
6 authors.
Funding
Abstract
Mining chemical-induced disease relations embedded in the vast biomedical literature could facilitate a wide range of computational biomedical applications, such as pharmacovigilance. The BioCreative V organized a Chemical Disease Relation (CDR) Track regarding chemical-induced disease relation extraction from biomedical literature in 2015. We participated in all subtasks of this challenge. In this article, we present our participation system Chemical Disease Relation Extraction SysTem (CD-REST), an end-to-end system for extracting chemical-induced disease relations in biomedical literature. CD-REST consists of two main components: (1) a chemical and disease named entity recognition and normalization module, which employs the Conditional Random Fields algorithm for entity recognition and a Vector Space Model-based approach for normalization; and (2) a relation extraction module that classifies both sentence-level and document-level candidate drug-disease pairs by support vector machines. Our system achieved the best performance on the chemical-induced disease relation extraction subtask in the BioCreative V CDR Track, demonstrating the effectiveness of our proposed machine learning-based approaches for automatic extraction of chemical-induced disease relations in biomedical literature. The CD-REST system provides web services using HTTP POST request. The web services can be accessed fromhttp://clinicalnlptool.com/cdr The online CD-REST demonstration system is available athttp://clinicalnlptool.com/cdr/cdr.html. Database URL:http://clinicalnlptool.com/cdr;http://clinicalnlptool.com/cdr/cdr.html.
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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.