標題: Accurate prediction of enzyme subfamily class using an adaptive fuzzy k-nearest neighbor method
作者: Huang, Wen-Lin
Chen, Hung-Ming
Hwang, Shiow-Fen
Ho, Shinn-Ying
生物科技學系
生物資訊及系統生物研究所
Department of Biological Science and Technology
Institude of Bioinformatics and Systems Biology
關鍵字: amino acid composition;enzyme subfamily class prediction;fuzzy theory;k-nearest neighbor;support vector machine
公開日期: 1-九月-2007
摘要: 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 %. (c) 2006 Elsevier Ireland Ltd. All rights reserved.
URI: http://dx.doi.org/10.1016/j.biosystems.2006.10.004
http://hdl.handle.net/11536/10347
ISSN: 0303-2647
DOI: 10.1016/j.biosystems.2006.10.004
期刊: BIOSYSTEMS
Volume: 90
Issue: 2
起始頁: 405
結束頁: 413
顯示於類別:期刊論文


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  1. 000250184500011.pdf