Accurate prediction of enzyme subfamily class using an adaptive fuzzy k-nearest neighbor method

• Published in: BioSystems 2007-09, Vol.90 (2), p.405-413
• Main Authors: Huang, Wen-Lin, Chen, Hung-Ming, Hwang, Shiow-Fen, Ho, Shinn-Ying
• Format: Article
• Language: English
• Subjects: Algorithms; Amino acid composition; Amino Acids - chemistry; Animals; Computer Simulation; Databases, Protein; Enzyme subfamily class prediction; Enzymes - chemistry; Fuzzy Logic; Fuzzy theory; Genetic Vectors; k-Nearest neighbor; Models, Statistical; Models, Theoretical; Oxidoreductases - genetics; Reproducibility of Results; Sequence Alignment; Sequence Analysis, Protein; Support vector machine; Systems Biology
• ISSN: 0303-2647; 1872-8324
• DOI: 10.1016/j.biosystems.2006.10.004

Amphiphilic pseudo-amino acid composition (Am-Pse-AAC) with extra sequence-order information is a useful feature for representing enzymes. This study first utilizes the k-nearest neighbor ( k-NN) rule to analyze the distribution of enzymes in the Am-Pse-AAC feature space. This analysis indicates the distributions of multiple classes of enzymes are highly overlapped. To cope with the overlap problem, this study proposes an efficient non-parametric classifier for predicting enzyme subfamily class using an adaptive fuzzy r-nearest neighbor (AFK-NN) method, where k and a fuzzy strength parameter m are adaptively specified. The fuzzy membership values of a query sample Q are dynamically determined according to the position of Q and its weighted distances to the k nearest neighbors. Using the same enzymes of the oxidoreductases family for comparisons, the prediction accuracy of AFK-NN is 76.6%, which is better than those of Support Vector Machine (73.6%), the decision tree method C5.0 (75.4%) and the existing covariant-discriminate algorithm (70.6%) using a jackknife test. To evaluate the generalization ability of AFK-NN, the datasets for all six families of entirely sequenced enzymes are established from the newly updated SWISS-PROT and ENZYME database. The accuracy of AFK-NN on the new large-scale dataset of oxidoreductases family is 83.3%, and the mean accuracy of the six families is 92.1%.