ArticleJournal of cheminformatics2016
Improving chemical disease relation extraction with rich features and weakly labeled data.
Article in Journal of cheminformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 88 citations in OpenAlex.
- Knowledge discovery of diseases symptoms and rehabilitation measures in Q&A communities.Scientific reports · 2025Article
- PubTator 3.0: an AI-powered literature resource for unlocking biomedical knowledge.Nucleic acids research · 2024Article
- Biomedical relation extraction with knowledge base-refined weak supervision.Database : the journal of biological databases and curation · 2023Article
- Disease- and Drug-Related Knowledge Extraction for Health Management from Online Health Communities Based on BERT-BiGRU-ATT.International journal of environmental research and public health · 2022Article
- Identification of Chemical-Disease Associations Through Integration of Molecular Fingerprint, Gene Ontology and Pathway Information.Interdisciplinary sciences, computational life sciences · 2022Article
- A sequence labeling framework for extracting drug-protein relations from biomedical literature.Database : the journal of biological databases and curation · 2022Article
- Exploiting document graphs for inter sentence relation extraction.Journal of biomedical semantics · 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
- CID-GCN: An Effective Graph Convolutional Networks for Chemical-Induced Disease Relation Extraction.Frontiers in genetics · 2021Article
- Broad-coverage biomedical relation extraction with SemRep.BMC bioinformatics · 2020Article
- Quality Matters: Biocuration Experts on the Impact of Duplication and Other Data Quality Issues in Biological Databases.Genomics, proteomics & bioinformatics · 2020Article
- Using a Large Margin Context-Aware Convolutional Neural Network to Automatically Extract Disease-Disease Association from Literature: Comparative Analytic Study.JMIR medical informatics · 2019Article
- Exploring semi-supervised variational autoencoders for biomedical relation extraction.Methods (San Diego, Calif.) · 2019Article
- 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
- Hierarchical sequence labeling for extracting BEL statements from biomedical literature.BMC medical informatics and decision making · 2019Article
- Combining relation extraction with function detection for BEL statement extraction.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
- Opportunities and obstacles for deep learning in biology and medicine.Journal of the Royal Society, Interface · 2018Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
backgroundDue to the importance of identifying relations between chemicals and diseases for new drug discovery and improving chemical safety, there has been a growing interest in developing automatic relation extraction systems for capturing these relations from the rich and rapid-growing biomedical literature. In this work we aim to build on current advances in named entity recognition and a recent BioCreative effort to further improve the state of the art in biomedical relation extraction, in particular for the chemical-induced disease (CID) relations.
resultsWe propose a rich-feature approach with Support Vector Machine to aid in the extraction of CIDs from PubMed articles. Our feature vector includes novel statistical features, linguistic knowledge, and domain resources. We also incorporate the output of a rule-based system as features, thus combining the advantages of rule- and machine learning-based systems. Furthermore, we augment our approach with automatically generated labeled text from an existing knowledge base to improve performance without additional cost for corpus construction. To evaluate our system, we perform experiments on the human-annotated BioCreative V benchmarking dataset and compare with previous results. When trained using only BioCreative V training and development sets, our system achieves an F-score of 57.51 %, which already compares favorably to previous methods. Our system performance was further improved to 61.01 % in F-score when augmented with additional automatically generated weakly labeled data.
conclusionsOur text-mining approach demonstrates state-of-the-art performance in disease-chemical relation extraction. More importantly, this work exemplifies the use of (freely available) curated document-level annotations in existing biomedical databases, which are largely overlooked in text-mining system development.
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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.