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

Nasibov E., Kandemir Çavaş Ç.

COMPUTATIONAL BIOLOGY AND CHEMISTRY, cilt.33, sa.6, ss.461-464, 2009 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 33 Sayı: 6
  • Basım Tarihi: 2009
  • Doi Numarası: 10.1016/j.compbiolchem.2009.09.002
  • Dergi Adı: COMPUTATIONAL BIOLOGY AND CHEMISTRY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.461-464
  • Anahtar Kelimeler: Amino acid composition, Enzyme class, K-nearest neighbor, Minimum-distance classifier, AMINO-ACID-COMPOSITION, SUBCELLULAR LOCATION PREDICTION, PROTEIN-STRUCTURE, LOCALIZATION
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

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.