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 Name: COMPUTATIONAL BIOLOGY AND CHEMISTRY
  • 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

Abstract

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.