ArticleBMC bioinformatics2010
Moara: a Java library for extracting and normalizing gene and protein mentions.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- An interactive retrieval system for clinical trial studies with context-dependent protocol elements.PloS one · 2020Article
- A method for named entity normalization in biomedical articles: application to diseases and plants.BMC bioinformatics · 2017Article
- An analysis of disease-gene relationship from Medline abstracts by DigSee.Scientific reports · 2017Article
- PALM-IST: Pathway Assembly from Literature Mining--an Information Search Tool.Scientific reports · 2015Article
- OncoSearch: cancer gene search engine with literature evidence.Nucleic acids research · 2014Article
- Molecular profiling of thyroid cancer subtypes using large-scale text mining.BMC medical genomics · 2014Article
- DigSee: Disease gene search engine with evidence sentences (version cancer).Nucleic acids research · 2013Article
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- Integrating various resources for gene name normalization.PloS one · 2012Article
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- GeneTUKit: a software for document-level gene normalization.Bioinformatics (Oxford, England) · 2011Article
- Toward an automatic method for extracting cancer- and other disease-related point mutations from the biomedical literature.Bioinformatics (Oxford, England) · 2011Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGene/protein recognition and normalization are important preliminary steps for many biological text mining tasks, such as information retrieval, protein-protein interactions, and extraction of semantic information, among others. Despite dedication to these problems and effective solutions being reported, easily integrated tools to perform these tasks are not readily available.
resultsThis study proposes a versatile and trainable Java library that implements gene/protein tagger and normalization steps based on machine learning approaches. The system has been trained for several model organisms and corpora but can be expanded to support new organisms and documents.
conclusionsMoara is a flexible, trainable and open-source system that is not specifically orientated to any organism and therefore does not requires specific tuning in the algorithms or dictionaries utilized. Moara can be used as a stand-alone application or can be incorporated in the workflow of a more general text mining system.
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Registered trials
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