Skip to main content
SpringerLink
Log in

GREAT: Gene Regulation EvAluation Tool

  • Conference paper
Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living (IWANN 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5518))

Included in the following conference series:

  • 2638 Accesses

Abstract

Our understanding of biological systems is highly dependent on the study of the mechanisms that regulate genetic expression. In this paper we present a tool to evaluate scientific papers that potentially describe Saccharomyces cerevisiae gene regulations, following the identification of transcription factors in abstracts using text mining techniques. GREAT evaluates the probability of a given gene-transcription factor pair corresponding to a gene regulation based on data retrieved from public biological databases.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Rebholz-Schuhmmann, D., Kirsch, H., Couto, F.: Facts From Text - Is Text Mining Ready to Deliver? PLoS. Biol. 3, e65 (2005)

    Google Scholar 

  2. Teixeira, M.C., et al.: The YEASTRACT Database: a Tool for the Analysis of Transcription Regulatory Associations in Saccharomyces cerevisiae. Nucl. Acids Res. 34, D446–D451 (2006)

    Article  Google Scholar 

  3. Sayers, E.W., et al.: Database Resources of the National Center For Biotechnology Information. Nucl. Acids Res. 37, D5–D15 (2009)

    Article  Google Scholar 

  4. Ashburner, M., et al.: Gene Ontology: Tool for the Unification of Biology. Nature Genetics 25, 25–29 (2000)

    Article  Google Scholar 

  5. Barrel, D., et al.: The GOA Database in 2009 - An Integrated Gene Ontology Annotation Resource. Nucl. Acids Res. 37, D396–D403 (2009)

    Article  Google Scholar 

  6. McCallum, A.K.: Bow: A Toolkit For Statistical Language Modelling, Text Retrieval, Classification and Clustering (1996), http://www.cs.cmu.edu/~mccallum/bow

  7. Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A.: Classification and Regression Trees. Chapman & Hall, USA (1984)

    MATH  Google Scholar 

  8. Lord, P., Stevens, R., Brass, A., Goble, C.: Investigating Semantic Similarity Measures Across the Gene Ontology: The Relationship Between Sequence and Annotation. Bioinformatics 19, 1275–1283 (2003)

    Article  Google Scholar 

  9. Pesquita, C., et al.: Metrics for GO Based Protein Semantic Similarity: a Systematic Evaluation. BMC Bioinformatics (suppl. 5), S4 (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Informatics, Faculty of Sciences, University of Lisbon, Edifício C6, Piso 3, Campo Grande, 1749-016, Lisbon, Portugal

    Catia Machado, Hugo Bastos & Francisco Couto

Authors
  1. Catia Machado
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hugo Bastos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Francisco Couto
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Graduate School of Engineering, Osaka Prefecture University, Osaka, Japan

    Sigeru Omatu

  2. Department of Informatics / CCTC, University of Minho, Braga, Portugal

    Miguel P. Rocha

  3. MAmI Research Lab, University of Castilla-La Mancha,, Ciudad Real, Spain

    José Bravo

  4. Department of Informatics, University of Vigo, Ourense, Spain

    Florentino Fernández

  5. Grupo de Investigación GICAP, Área de Lenguajes Higher Polytechnic School, Universidad de Burgos, Burgos, Spain

    Emilio Corchado

  6. Higher Polytechnic School, University of Burgos, Burgos, Spain

    Andrés Bustillo

  7. Department of Informatics, University of Salamanca, Salamanca, Spain

    Juan M. Corchado

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Machado, C., Bastos, H., Couto, F. (2009). GREAT: Gene Regulation EvAluation Tool. In: Omatu, S., et al. Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living. IWANN 2009. Lecture Notes in Computer Science, vol 5518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02481-8_142

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-02481-8_142

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02480-1

  • Online ISBN: 978-3-642-02481-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation