ArticleGenome biology2008
Concept recognition for extracting protein interaction relations from biomedical text.
Article in Genome biology, 2008. 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
22 citing papers in PubMed.
- PhenoTagger: a hybrid method for phenotype concept recognition using human phenotype ontology.Bioinformatics (Oxford, England) · 2021Article
- Leveraging syntax to better capture the semantics of elliptical coordinated compound noun phrases.Journal of biomedical informatics · 2017Article
- Mapping Phenotypic Information in Heterogeneous Textual Sources to a Domain-Specific Terminological Resource.PloS one · 2016Article
- Methodological Issues in Predicting Pediatric Epilepsy Surgery Candidates Through Natural Language Processing and Machine Learning.Biomedical informatics insights · 2016Article
- Automatically Detecting Failures in Natural Language Processing Tools for Online Community Text.Journal of medical Internet research · 2015Article
- GNormPlus: An Integrative Approach for Tagging Genes, Gene Families, and Protein Domains.BioMed research international · 2015Article
- BC4GO: a full-text corpus for the BioCreative IV GO task.Database : the journal of biological databases and curation · 2014Article
- Chapter 16: text mining for translational bioinformatics.PLoS computational biology · 2013Article
- Assessment of software testing and quality assurance in natural language processing applications and a linguistically inspired approach to improving it.Trustworthy eternal systems via evolving software, data and knowledge : second international workshop, EternalS 2012, Montpellier, France, August 28, 2012, revised selected papers. EternalS (Workshop) (2nd : 2012 : Montpellier, France) · 2013Article
- Article
- Dynamic programming re-ranking for PPI interactor and pair extraction in full-text articles.BMC bioinformatics · 2011Article
- Parenthetically speaking: classifying the contents of parentheses for text mining.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2011Article
- The structural and content aspects of abstracts versus bodies of full text journal articles are different.BMC bioinformatics · 2010Article
- Improving accuracy for identifying related PubMed queries by an integrated approach.Journal of biomedical informatics · 2009Article
- A realistic assessment of methods for extracting gene/protein interactions from free text.BMC bioinformatics · 2009Article
- Biomedical discovery acceleration, with applications to craniofacial development.PLoS computational biology · 2009Article
- Overview of the protein-protein interaction annotation extraction task of BioCreative II.Genome biology · 2008Article
- Introducing meta-services for biomedical information extraction.Genome biology · 2008Article
- Linking genes to literature: text mining, information extraction, and retrieval applications for biology.Genome biology · 2008Review
- SimConcept: A Hybrid Approach for Simplifying Composite Named Entities in Biomedicine.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and BiomedicineArticle
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
backgroundReliable information extraction applications have been a long sought goal of the biomedical text mining community, a goal that if reached would provide valuable tools to benchside biologists in their increasingly difficult task of assimilating the knowledge contained in the biomedical literature. We present an integrated approach to concept recognition in biomedical text. Concept recognition provides key information that has been largely missing from previous biomedical information extraction efforts, namely direct links to well defined knowledge resources that explicitly cement the concept's semantics. The BioCreative II tasks discussed in this special issue have provided a unique opportunity to demonstrate the effectiveness of concept recognition in the field of biomedical language processing.
resultsThrough the modular construction of a protein interaction relation extraction system, we present several use cases of concept recognition in biomedical text, and relate these use cases to potential uses by the benchside biologist.
conclusionCurrent information extraction technologies are approaching performance standards at which concept recognition can begin to deliver high quality data to the benchside biologist. Our system is available as part of the BioCreative Meta-Server project and on the internet http://bionlp.sourceforge.net.
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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.