Efficiency analysis of KNN and minimum distance-based classifiers in enzyme family prediction
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.
enzyme class k-nearest neighbor amino acid composition minimum-distance classifier