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

Authors:
Efendi Nasibov;Cagin Kandemir-Cavas
Affiliations:
Dokuz Eylül University, Faculty of Arts and Sciences, Department of Statistics, 35160 Kaynaklar Campus, Izmir, Turkey;Dokuz Eylül University, Faculty of Arts and Sciences, Department of Statistics, 35160 Kaynaklar Campus, Izmir, Turkey
Venue:
Computational Biology and Chemistry
Year:
2009

Citing 2
Cited 4

Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes

Bioinformatics
Short communication: Protein subcellular location prediction using optimally weighted fuzzy k-NN algorithm

Computational Biology and Chemistry

Classification of Error-Related Negativity (ERN) and Positivity (Pe) potentials using kNN and Support Vector Machines

Computers in Biology and Medicine
OWA-based linkage method in hierarchical clustering: Application on phylogenetic trees

Expert Systems with Applications: An International Journal
Research on classification methods of glycoside hydrolases mechanism

ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
Research article: Relationship between global structural parameters and Enzyme Commission hierarchy: Implications for function prediction

Computational Biology and Chemistry

Quantified Score

Hi-index	0.00

Visualization

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. In this paper, two kinds of simple minimum distance-based classifier methods have been proposed. These methods and known K-nearest neighbor (KNN) classification algorithm have been performed in order to classify enzymes according to their amino acid composition. Performance measurements and elapsed time to execute algorithms have been compared. In addition, equality of two proposed approaches under special condition has been proved in order to be a guide for researchers.