Full metadata record
DC FieldValueLanguage
dc.contributor.authorHuang, Wen-Linen_US
dc.contributor.authorChen, Hung-Mingen_US
dc.contributor.authorHwang, Shiow-Fenen_US
dc.contributor.authorHo, Shinn-Yingen_US
dc.date.accessioned2014-12-08T15:13:22Z-
dc.date.available2014-12-08T15:13:22Z-
dc.date.issued2007-09-01en_US
dc.identifier.issn0303-2647en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.biosystems.2006.10.004en_US
dc.identifier.urihttp://hdl.handle.net/11536/10347-
dc.description.abstractAmphiphilic 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.en_US
dc.language.isoen_USen_US
dc.subjectamino acid compositionen_US
dc.subjectenzyme subfamily class predictionen_US
dc.subjectfuzzy theoryen_US
dc.subjectk-nearest neighboren_US
dc.subjectsupport vector machineen_US
dc.titleAccurate prediction of enzyme subfamily class using an adaptive fuzzy k-nearest neighbor methoden_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.biosystems.2006.10.004en_US
dc.identifier.journalBIOSYSTEMSen_US
dc.citation.volume90en_US
dc.citation.issue2en_US
dc.citation.spage405en_US
dc.citation.epage413en_US
dc.contributor.department生物科技學系zh_TW
dc.contributor.department生物資訊及系統生物研究所zh_TW
dc.contributor.departmentDepartment of Biological Science and Technologyen_US
dc.contributor.departmentInstitude of Bioinformatics and Systems Biologyen_US
dc.identifier.wosnumberWOS:000250184500011-
dc.citation.woscount15-
Appears in Collections:Articles


Files in This Item:

  1. 000250184500011.pdf