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Knowledge Networks of Biological and Medical Data: An Exhaustive and Flexible Solution to Model Life Science Domains

(Systems Paper)
  • Sascha Losko
  • Karsten Wenger
  • Wenzel Kalus
  • Andrea Ramge
  • Jens Wiehler
  • Klaus Heumann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4075)

Abstract

The huge amount of unstructured information generated by academic and industrial research groups must be easily available to facilitate scientific projects. In particular, information that is conveyed by unstructured or semi-structured text represents a vast resource for the scientific community. Systems capable of mining these textual data sets are the only option to unveil the information hidden in free text on a large scale. The BioLT Literature Mining Tool allows exhaustive extraction of information from text resources. Using advanced tagger/parser mechanisms and topic-specific dictionaries, the BioLT tool delivers structured relationships. Beyond information hidden in free text, other resources in biological and medical research are relevant, including experimental data from “-omics” platforms, phenotype information and clinical data. The BioXM Knowledge Management Environment efficiently models such complex research environments. This platform enables scientists to create knowledge networks with flexible workflows for handling experimental information and metadata, including annotation or ontologies. Information from public databases can be incorporated using the embedded BioRS Integration and Retrieval System. Users can navigate and modify the information networks. Thus, research projects can be modeled and extended dynamically.

Keywords

Gastrointestinal Stromal Tumor Progressive Supranuclear Palsy Knowledge Network Systemic Mastocytosis Paralytic Shellfish Poison 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Lazebnik, Y.: Can a biologist fix a radio?–Or, what I learned while studying apoptosis. Cancer Cell 2(3), 179–182 (2002)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Searls, D.B.: Data integration: challenges for drug discovery. Nat. Rev. Drug Discov. 4(1), 45–58 (2005)CrossRefGoogle Scholar
  3. 3.
    Etzold, T., Ulyanov, A., Argos, P.: SRS: information retrieval system for molecular biology data banks. Methods. Enzymol. 266, 114–128 (1996)CrossRefGoogle Scholar
  4. 4.
    Vogelstein, B., Kinzler, K.W.: Cancer genes and the pathways they control. Nat. Med. 10(8), 789–799 (2004)CrossRefGoogle Scholar
  5. 5.
    Hartel, F.W., et al.: Modeling a description logic vocabulary for cancer research. J. Biomed. Inform. 38(2), 114–129 (2005)CrossRefGoogle Scholar
  6. 6.
    Ruepp, A., et al.: The FunCat, a functional annotation scheme for systematic classification of proteins from whole genomes. Nucleic Acids Res. 32(18), 5539–5545 (2004)CrossRefGoogle Scholar
  7. 7.
    Ashburner, M., et al.: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25(1), 25–29 (2000)Google Scholar
  8. 8.
    Karp, P.D., et al.: An evidence ontology for use in pathway/genome databases. In: Pac. Symp. Biocomput., pp. 190–201 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sascha Losko
    • 1
  • Karsten Wenger
    • 1
  • Wenzel Kalus
    • 1
  • Andrea Ramge
    • 1
  • Jens Wiehler
    • 1
  • Klaus Heumann
    • 1
  1. 1.Biomax Informatics AGMartinsriedGermany

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