HiSP: A Probabilistic Data Mining Technique for Protein Classification

  • Luiz Merschmann
  • Alexandre Plastino
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3992)


In this work, we propose a new computational technique to solve the protein classification problem. The goal is to predict the functional family of novel protein sequences based on their motif composition. In order to improve the results obtained with other known approaches, we propose a new data mining technique for protein classification based on Bayes’ theorem, called Highest Subset Probability (HiSP). To evaluate our proposal, datasets extracted from Prosite, a curated protein family database, are used as experimental datasets. The computational results have shown that the proposed method outperforms other known methods for all tested datasets and looks very promising for problems with characteristics similar to the problem addressed here.


Data Mining Training Dataset Test Dataset Data Mining Technique Data Mining Method 
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 2006

Authors and Affiliations

  • Luiz Merschmann
    • 1
  • Alexandre Plastino
    • 1
  1. 1.Departamento de Ciência da ComputaçãoUniversidade Federal FluminenseNiteróiBrazil

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