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      Event extraction across multiple levels of biological organization

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          Abstract

          Motivation: Event extraction using expressive structured representations has been a significant focus of recent efforts in biomedical information extraction. However, event extraction resources and methods have so far focused almost exclusively on molecular-level entities and processes, limiting their applicability.

          Results: We extend the event extraction approach to biomedical information extraction to encompass all levels of biological organization from the molecular to the whole organism. We present the ontological foundations, target types and guidelines for entity and event annotation and introduce the new multi-level event extraction (MLEE) corpus, manually annotated using a structured representation for event extraction. We further adapt and evaluate named entity and event extraction methods for the new task, demonstrating that both can be achieved with performance broadly comparable with that for established molecular entity and event extraction tasks.

          Availability: The resources and methods introduced in this study are available from http://nactem.ac.uk/MLEE/.

          Contact: pyysalos@ 123456cs.man.ac.uk

          Supplementary information: Supplementary data are available at Bioinformatics online.

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          Most cited references37

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            Entrez Gene: gene-centered information at NCBI

            Entrez Gene (www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=gene) is NCBI's database for gene-specific information. It does not include all known or predicted genes; instead Entrez Gene focuses on the genomes that have been completely sequenced, that have an active research community to contribute gene-specific information, or that are scheduled for intense sequence analysis. The content of Entrez Gene represents the result of curation and automated integration of data from NCBI's Reference Sequence project (RefSeq), from collaborating model organism databases, and from many other databases available from NCBI. Records are assigned unique, stable and tracked integers as identifiers. The content (nomenclature, map location, gene products and their attributes, markers, phenotypes, and links to citations, sequences, variation details, maps, expression, homologs, protein domains and external databases) is updated as new information becomes available. Entrez Gene is a step forward from NCBI's LocusLink, with both a major increase in taxonomic scope and improved access through the many tools associated with NCBI Entrez.
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              Modeling biomedical experimental processes with OBI

              Background Experimental descriptions are typically stored as free text without using standardized terminology, creating challenges in comparison, reproduction and analysis. These difficulties impose limitations on data exchange and information retrieval. Results The Ontology for Biomedical Investigations (OBI), developed as a global, cross-community effort, provides a resource that represents biomedical investigations in an explicit and integrative framework. Here we detail three real-world applications of OBI, provide detailed modeling information and explain how to use OBI. Conclusion We demonstrate how OBI can be applied to different biomedical investigations to both facilitate interpretation of the experimental process and increase the computational processing and integration within the Semantic Web. The logical definitions of the entities involved allow computers to unambiguously understand and integrate different biological experimental processes and their relevant components. Availability OBI is available at http://purl.obolibrary.org/obo/obi/2009-11-02/obi.owl
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                Author and article information

                Journal
                Bioinformatics
                Bioinformatics
                bioinformatics
                bioinfo
                Bioinformatics
                Oxford University Press
                1367-4803
                1367-4811
                15 September 2012
                3 September 2012
                3 September 2012
                : 28
                : 18 , ECCB 2012 PROCEEDINGS PAPERS COMMITTEE SEPTEMBER 9 TO SEPTEMBER 12, 2012, CONFERENCE CENTER BASEL, SWITZERLAND
                : i575-i581
                Affiliations
                1 National Centre for Text Mining and School of Computer Science, University of Manchester, Manchester, UK
                2Department of Computer Science, University of Tokyo, Tokyo, Japan
                3Microsoft Research Asia, Beijing, China
                Author notes
                * To whom correspondence should be addressed.
                Article
                bts407
                10.1093/bioinformatics/bts407
                3436834
                22962484
                cf06a10d-8906-4b48-80f0-da7e0cb0f52f
                © The Author(s) (2012). Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Page count
                Pages: 7
                Categories
                Original Papers
                Databases, Ontologies, and Text Mining

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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