ArticleComputational and mathematical methods in medicine2015
A Relation Extraction Framework for Biomedical Text Using Hybrid Feature Set.
Article in Computational and mathematical methods in medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Integrating graph convolutional networks with large language models for structured biomedical material knowledge representation.Regenerative biomaterials · 2025Article
- MeSH2Matrix: combining MeSH keywords and machine learning for biomedical relation classification based on PubMed.Journal of biomedical semantics · 2024Article
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- Review: A Roadmap to Use Nonstructured Data to Discover Multitarget Cancer Therapies.JCO clinical cancer informatics · 2023Review
- Application of Deep Learning in Generating Structured Radiology Reports: A Transformer-Based Technique.Journal of digital imaging · 2023Article
- Automated extraction of genes associated with antibiotic resistance from the biomedical literature.Database : the journal of biological databases and curation · 2022Article
- Applying citizen science to gene, drug and disease relationship extraction from biomedical abstracts.Bioinformatics (Oxford, England) · 2020Article
- Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives.Human genetics · 2019Review
- A Computable Phenotype for Acute Respiratory Distress Syndrome Using Natural Language Processing and Machine Learning.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- Integrating unified medical language system and association mining techniques into relevance feedback for biomedical literature search.BMC bioinformatics · 2016Article
- A Shortest Dependency Path Based Convolutional Neural Network for Protein-Protein Relation Extraction.BioMed research international · 2016Article
Corrections and comments
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Authors and funding
3 authors.
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Abstract
The information extraction from unstructured text segments is a complex task. Although manual information extraction often produces the best results, it is harder to manage biomedical data extraction manually because of the exponential increase in data size. Thus, there is a need for automatic tools and techniques for information extraction in biomedical text mining. Relation extraction is a significant area under biomedical information extraction that has gained much importance in the last two decades. A lot of work has been done on biomedical relation extraction focusing on rule-based and machine learning techniques. In the last decade, the focus has changed to hybrid approaches showing better results. This research presents a hybrid feature set for classification of relations between biomedical entities. The main contribution of this research is done in the semantic feature set where verb phrases are ranked using Unified Medical Language System (UMLS) and a ranking algorithm. Support Vector Machine and Naïve Bayes, the two effective machine learning techniques, are used to classify these relations. Our approach has been validated on the standard biomedical text corpus obtained from MEDLINE 2001. Conclusively, it can be articulated that our framework outperforms all state-of-the-art approaches used for relation extraction on the same corpus.
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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.