Fuzzy k-Nearest Neighbor Method for Protein Secondary Structure Prediction and Its Parallel Implementation

  • Seung-Yeon Kim
  • Jaehyun Sim
  • Julian Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4115)


Fuzzy k-nearest neighbor method is a generalization of nearest neighbor method, the simplest algorithm for pattern classification. One of the important areas for application of the pattern classification is the protein secondary structure prediction, an important topic in the field of bioinformatics. In this work, we develop a parallel algorithm for protein secondary structure prediction, based on the fuzzy k-nearest neighbor method, that uses evolutionary profile obtained from PSI-BLAST (Position Specific Iterative Basic Local Sequence Alignment Tool) as the feature vectors.


Feature Vector Secondary Structure Prediction Solvent Accessibility Protein Structure Prediction Reference Dataset 
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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© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Seung-Yeon Kim
    • 1
  • Jaehyun Sim
    • 2
  • Julian Lee
    • 3
  1. 1.Computer Aided Molecular Design Research CenterSoongsil UniversitySeoulKorea
  2. 2.School of DentistrySeoul National UniversitySeoulKorea
  3. 3.Department of Bioinformatics and Life ScienceSoongsil UniversitySeoulKorea

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