Fuzzy Classification of Secretory Signals in Proteins Encoded by the Plasmodium falciparum Genome

  • Erica Logan
  • Richard Hall
  • Nectarios Klonis
  • Susanna Herd
  • Leann Tilley
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3213)


Over five thousand types of protein are produced by the malaria parasite Plasmodium falciparum. Each protein contains the address of a specific destination to which it will be trafficked by various cellular translocation mechanisms. This address may be encoded in a secretory signal, physically represented as an amino acid subsequence forming a motif or pattern. The different signal sequences are classified according to where they occur with respect to the entire amino acid sequence. Biologists are interested in computational techniques that can automatically classify the large amount of data in the Plasmodium falciparum genome, since they have inferred from ongoing experimentation that a correlation exists between particular signals and significant cellular locations. We describe the development of a web-accessible fuzzy classifier of secretory signals in proteins. The application of this classifier to the entire P. falciparum genome immediately produced some biologically-interesting predictions that are briefly discussed.


Plasmodium Falciparum Secretory Signal Fuzzy Classification Fuzzy Classifier Falciparum Genome 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Erica Logan
    • 1
    • 2
  • Richard Hall
    • 2
  • Nectarios Klonis
    • 1
  • Susanna Herd
    • 1
  • Leann Tilley
    • 1
  1. 1.Department of BiochemistryLa Trobe UniversityMelbourneAustralia
  2. 2.Department of Computer Science and EngineeringLa Trobe UniversityMelbourneAustralia

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