Efficiency analysis of KNN and minimum distance-based classifiers in enzyme family prediction

Nasibov E., Kandemir Çavaş Ç.

COMPUTATIONAL BIOLOGY AND CHEMISTRY, vol.33, no.6, pp.461-464, 2009 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 33 Issue: 6
  • Publication Date: 2009
  • Doi Number: 10.1016/j.compbiolchem.2009.09.002
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.461-464
  • Keywords: Amino acid composition, Enzyme class, K-nearest neighbor, Minimum-distance classifier, AMINO-ACID-COMPOSITION, SUBCELLULAR LOCATION PREDICTION, PROTEIN-STRUCTURE, LOCALIZATION
  • Dokuz Eylül University Affiliated: Yes


Nearly all enzymes are proteins. They are the biological catalysts that accelerate the function of cellular reactions. Because of different characteristics of reaction tasks, they split into six classes: oxidoreductases (EC-1), transferases (EC-2), hydrolases (EC-3), lyases (EC-4), isomerases (EC-5), ligases (EC-6). Prediction of enzyme classes is of great importance in identifying which enzyme class is a member of a protein. Since the enzyme sequences increase day by day, contrary to experimental analysis in prediction of enzyme classes for a newly found enzyme sequence, providing from data mining techniques becomes very useful and time-saving.