Predicting Enzyme Subclasses by Using Random Forest with Multicharacteristic Parameters

Title:Predicting Enzyme Subclasses by Using Random Forest with Multicharacteristic Parameters

Volume: 21 Issue: 3

Author(s): Ying Wang, Xiuzhen Hu, Lixia Sun, Zhenxing Feng and Hangyu Song

Affiliation:

Keywords: Enzyme subclasses, Increment of diversity value, matrix scoring function value, motif, prediction, random forest algorithm.

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.

Cite this article as:

Wang Ying, Hu Xiuzhen, Sun Lixia, Feng Zhenxing and Song Hangyu, Predicting Enzyme Subclasses by Using Random Forest with Multicharacteristic Parameters, Protein & Peptide Letters 2014; 21 (3) . https://dx.doi.org/10.2174/09298665113206660114

DOI https://dx.doi.org/10.2174/09298665113206660114	Print ISSN 0929-8665
Publisher Name Bentham Science Publisher	Online ISSN 1875-5305