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Volume 6 Supplement 1

A critical assessment of text mining methods in molecular biology

Reports

Edited by Christian Blaschke, Lynette Hirschman, Alfonso Valencia, Alexander Yeh

A critical assessment of text mining methods in molecular biology. Go to conference site.

Granada, SpainMarch 28-31, 2004

  1. The goal of the first BioCreAtIvE challenge (Critical Assessment of Information Extraction in Biology) was to provide a set of common evaluation tasks to assess the state of the art for text mining applied to ...

    Authors: Lynette Hirschman, Alexander Yeh, Christian Blaschke and Alfonso Valencia
    Citation: BMC Bioinformatics 2005 6(Suppl 1):S1
  2. The biological research literature is a major repository of knowledge. As the amount of literature increases, it will get harder to find the information of interest on a particular topic. There has been an inc...

    Authors: Alexander Yeh, Alexander Morgan, Marc Colosimo and Lynette Hirschman
    Citation: BMC Bioinformatics 2005 6(Suppl 1):S2
  3. Named entity recognition (NER) is an important first step for text mining the biomedical literature. Evaluating the performance of biomedical NER systems is impossible without a standardized test corpus. The a...

    Authors: Lorraine Tanabe, Natalie Xie, Lynne H Thom, Wayne Matten and W John Wilbur
    Citation: BMC Bioinformatics 2005 6(Suppl 1):S3
  4. Our approach to Task 1A was inspired by Tanabe and Wilbur's ABGene system [1, 2]. Like Tanabe and Wilbur, we approached the problem as one of part-of-speech tagging, adding a GENE tag to the standard tag set. Whe...

    Authors: Shuhei Kinoshita, K Bretonnel Cohen, Philip V Ogren and Lawrence Hunter
    Citation: BMC Bioinformatics 2005 6(Suppl 1):S4
  5. Good automatic information extraction tools offer hope for automatic processing of the exploding biomedical literature, and successful named entity recognition is a key component for such tools.

    Authors: Jenny Finkel, Shipra Dingare, Christopher D Manning, Malvina Nissim, Beatrice Alex and Claire Grover
    Citation: BMC Bioinformatics 2005 6(Suppl 1):S5
  6. Automated information extraction from biomedical literature is important because a vast amount of biomedical literature has been published. Recognition of the biomedical named entities is the first step in inf...

    Authors: Tomohiro Mitsumori, Sevrani Fation, Masaki Murata, Kouichi Doi and Hirohumi Doi
    Citation: BMC Bioinformatics 2005 6(Suppl 1):S8
  7. In task 1A of the BioCreAtIvE evaluation, systems had to be devised that recognize words and phrases forming gene or protein names in natural language sentences. We approach this problem by building a word cla...

    Authors: Jörg Hakenberg, Steffen Bickel, Conrad Plake, Ulf Brefeld, Hagen Zahn, Lukas Faulstich, Ulf Leser and Tobias Scheffer
    Citation: BMC Bioinformatics 2005 6(Suppl 1):S9
  8. Our goal in BioCreAtIve has been to assess the state of the art in text mining, with emphasis on applications that reflect real biological applications, e.g., the curation process for model organism databases....

    Authors: Lynette Hirschman, Marc Colosimo, Alexander Morgan and Alexander Yeh
    Citation: BMC Bioinformatics 2005 6(Suppl 1):S11
  9. We prepared and evaluated training and test materials for an assessment of text mining methods in molecular biology. The goal of the assessment was to evaluate the ability of automated systems to generate a li...

    Authors: Marc E Colosimo, Alexander A Morgan, Alexander S Yeh, Jeffrey B Colombe and Lynette Hirschman
    Citation: BMC Bioinformatics 2005 6(Suppl 1):S12
  10. Document gene normalization is the problem of creating a list of unique identifiers for genes that are mentioned within a document. Automating this process has many potential applications in both information e...

    Authors: Jeremiah Crim, Ryan McDonald and Fernando Pereira
    Citation: BMC Bioinformatics 2005 6(Suppl 1):S13
  11. Identification of gene and protein names in biomedical text is a challenging task as the corresponding nomenclature has evolved over time. This has led to multiple synonyms for individual genes and proteins, a...

    Authors: Daniel Hanisch, Katrin Fundel, Heinz-Theodor Mevissen, Ralf Zimmer and Juliane Fluck
    Citation: BMC Bioinformatics 2005 6(Suppl 1):S14
  12. Significant parts of biological knowledge are available only as unstructured text in articles of biomedical journals. By automatically identifying gene and gene product (protein) names and mapping these to uni...

    Authors: Katrin Fundel, Daniel Güttler, Ralf Zimmer and Joannis Apostolakis
    Citation: BMC Bioinformatics 2005 6(Suppl 1):S15
  13. Molecular Biology accumulated substantial amounts of data concerning functions of genes and proteins. Information relating to functional descriptions is generally extracted manually from textual data and store...

    Authors: Christian Blaschke, Eduardo Andres Leon, Martin Krallinger and Alfonso Valencia
    Citation: BMC Bioinformatics 2005 6(Suppl 1):S16
  14. The Gene Ontology Annotation (GOA) database http://​www.​ebi.​ac.​uk/​GOA aims to provide high-quality supplementary GO annotation to proteins in the UniProt Know...

    Authors: Evelyn B Camon, Daniel G Barrell, Emily C Dimmer, Vivian Lee, Michele Magrane, John Maslen, David Binns and Rolf Apweiler
    Citation: BMC Bioinformatics 2005 6(Suppl 1):S17
  15. We participated in the BioCreAtIvE Task 2, which addressed the annotation of proteins into the Gene Ontology (GO) based on the text of a given document and the selection of evidence text from the document just...

    Authors: Karin Verspoor, Judith Cohn, Cliff Joslyn, Sue Mniszewski, Andreas Rechtsteiner, Luis M Rocha and Tiago Simas
    Citation: BMC Bioinformatics 2005 6(Suppl 1):S20
  16. The development of text mining systems that annotate biological entities with their properties using scientific literature is an important recent research topic. These systems need first to recognize the biolo...

    Authors: Francisco M Couto, Mário J Silva and Pedro M Coutinho
    Citation: BMC Bioinformatics 2005 6(Suppl 1):S21
  17. In the context of the BioCreative competition, where training data were very sparse, we investigated two complementary tasks: 1) given a Swiss-Prot triplet, containing a protein, a GO (Gene Ontology) term and ...

    Authors: Frédéric Ehrler, Antoine Geissbühler, Antonio Jimeno and Patrick Ruch
    Citation: BMC Bioinformatics 2005 6(Suppl 1):S23

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