کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
2076881 1079468 2007 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Accurate prediction of enzyme subfamily class using an adaptive fuzzy k-nearest neighbor method
موضوعات مرتبط
مهندسی و علوم پایه ریاضیات مدل‌سازی و شبیه سازی
پیش نمایش صفحه اول مقاله
Accurate prediction of enzyme subfamily class using an adaptive fuzzy k-nearest neighbor method
چکیده انگلیسی

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%.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Biosystems - Volume 90, Issue 2, September–October 2007, Pages 405–413
نویسندگان
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