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Large-scale event extraction from literature with multi-level gene normalization

(2013) PLOS ONE. 8(4).
Author
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Bioinformatics: from nucleotids to networks (N2N)
Abstract
Text mining for the life sciences aims to aid database curation, knowledge summarization and information retrieval through the automated processing of biomedical texts. To provide comprehensive coverage and enable full integration with existing biomolecular database records, it is crucial that text mining tools scale up to millions of articles and that their analyses can be unambiguously linked to information recorded in resources such as UniProt, KEGG, BioGRID and NCBI databases. In this study, we investigate how fully automated text mining of complex biomolecular events can be augmented with a normalization strategy that identifies biological concepts in text, mapping them to identifiers at varying levels of granularity, ranging from canonicalized symbols to unique gene and proteins and broad gene families. To this end, we have combined two state-of-the-art text mining components, previously evaluated on two community-wide challenges, and have extended and improved upon these methods by exploiting their complementary nature. Using these systems, we perform normalization and event extraction to create a large-scale resource that is publicly available, unique in semantic scope, and covers all 21.9 million PubMed abstracts and 460 thousand PubMed Central open access full-text articles. This dataset contains 40 million biomolecular events involving 76 million gene/protein mentions, linked to 122 thousand distinct genes from 5032 species across the full taxonomic tree. Detailed evaluations and analyses reveal promising results for application of this data in database and pathway curation efforts. The main software components used in this study are released under an open-source license. Further, the resulting dataset is freely accessible through a novel API, providing programmatic and customized access (http://www.evexdb.org/api/v001/). Finally, to allow for large-scale bioinformatic analyses, the entire resource is available for bulk download from http://evexdb.org/download/, under the Creative Commons -Attribution - Share Alike (CC BY-SA) license.
Keywords
NETWORKS, PROTEINS, INFORMATION, BIOLOGY, ANNOTATION, SHARED TASK 2011, DATABASE, BIOCREATIVE III, GENOMES, ENSEMBL

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Citation

Please use this url to cite or link to this publication:

Chicago
Van Landeghem, Sofie, Jari Bjorne, Chih-Hsuan Wei, Kai Hakala, Sampo Pyysalo, Sophia Ananiadou, Hung-Yu Kao, et al. 2013. “Large-scale Event Extraction from Literature with Multi-level Gene Normalization.” Plos One 8 (4).
APA
Van Landeghem, S., Bjorne, J., Wei, C.-H., Hakala, K., Pyysalo, S., Ananiadou, S., Kao, H.-Y., et al. (2013). Large-scale event extraction from literature with multi-level gene normalization. PLOS ONE, 8(4).
Vancouver
1.
Van Landeghem S, Bjorne J, Wei C-H, Hakala K, Pyysalo S, Ananiadou S, et al. Large-scale event extraction from literature with multi-level gene normalization. PLOS ONE. 2013;8(4).
MLA
Van Landeghem, Sofie et al. “Large-scale Event Extraction from Literature with Multi-level Gene Normalization.” PLOS ONE 8.4 (2013): n. pag. Print.
@article{3239212,
  abstract     = {Text mining for the life sciences aims to aid database curation, knowledge summarization and information retrieval through the automated processing of biomedical texts. To provide comprehensive coverage and enable full integration with existing biomolecular database records, it is crucial that text mining tools scale up to millions of articles and that their analyses can be unambiguously linked to information recorded in resources such as UniProt, KEGG, BioGRID and NCBI databases. In this study, we investigate how fully automated text mining of complex biomolecular events can be augmented with a normalization strategy that identifies biological concepts in text, mapping them to identifiers at varying levels of granularity, ranging from canonicalized symbols to unique gene and proteins and broad gene families. To this end, we have combined two state-of-the-art text mining components, previously evaluated on two community-wide challenges, and have extended and improved upon these methods by exploiting their complementary nature. Using these systems, we perform normalization and event extraction to create a large-scale resource that is publicly available, unique in semantic scope, and covers all 21.9 million PubMed abstracts and 460 thousand PubMed Central open access full-text articles. This dataset contains 40 million biomolecular events involving 76 million gene/protein mentions, linked to 122 thousand distinct genes from 5032 species across the full taxonomic tree. Detailed evaluations and analyses reveal promising results for application of this data in database and pathway curation efforts. The main software components used in this study are released under an open-source license. Further, the resulting dataset is freely accessible through a novel API, providing programmatic and customized access (http://www.evexdb.org/api/v001/). Finally, to allow for large-scale bioinformatic analyses, the entire resource is available for bulk download from http://evexdb.org/download/, under the Creative Commons -Attribution - Share Alike (CC BY-SA) license.},
  articleno    = {e55814},
  author       = {Van Landeghem, Sofie and Bjorne, Jari and Wei, Chih-Hsuan and Hakala, Kai and Pyysalo, Sampo and Ananiadou, Sophia and Kao, Hung-Yu and Lu, Zhiyong and Salakoski, Tapio and Van de Peer, Yves and Ginter, Filip},
  issn         = {1932-6203},
  journal      = {PLOS ONE},
  keywords     = {NETWORKS,PROTEINS,INFORMATION,BIOLOGY,ANNOTATION,SHARED TASK 2011,DATABASE,BIOCREATIVE III,GENOMES,ENSEMBL},
  language     = {eng},
  number       = {4},
  pages        = {12},
  title        = {Large-scale event extraction from literature with multi-level gene normalization},
  url          = {http://dx.doi.org/10.1371/journal.pone.0055814},
  volume       = {8},
  year         = {2013},
}

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