title: Predicting enzyme subclasses by using random forest with multicharacteristic parameters.
abstract: In order to predict enzyme subclasses, this paper builds a new enzyme database in term of previous ideas and methods. Based on protein sequence, by selecting increment of diversity value, low-frequency of power spectral density, matrix scoring values and motif frequency as characteristic parameters to describe the sequence information, a Random Forest algorithm for predicting enzyme subclass is proposed. Using the Jack-knife test, the overall success rate identifying the 18 subclasses of oxidoreductases, the 8 subclasses of transferases, the 5 subclasses of hydrolases, the 6 subclasses of lyases, the 6 subclasses of isomerases, and the 6 subclasses of ligases are 90.86%, 95.24%, 96.42%, 98.60%, 97.53% and 98.03%. Furthermore, the same way is used to the previous database, the better results are obtained.
authors.forename: Ying
authors.initials: Y
authors.lastname: Wang
authors.forename: Xiuzhen
authors.initials: X
authors.lastname: Hu
authors.forename: Lixia
authors.initials: L
authors.lastname: Sun
authors.forename: Zhenxing
authors.initials: Z
authors.lastname: Feng
authors.affiliation: College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051. P.R. China. hxz@imut.edu.cn.
authors.forename: Hangyu
authors.initials: H
authors.lastname: Song
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D000818:Animals; D019295:Computational Biology / Q000379:methods; D030562:Databases, Protein; D004798:Enzymes / Q000145:classification*; D006801:Humans; D008969:Molecular Sequence Data
chemical_list: D004798:Enzymes