Predicting Enzyme Subclasses by Using Support Vector Machine with Composite Vectors

Title: Predicting Enzyme Subclasses by Using Support Vector Machine with Composite Vectors

Volume: 17 Issue: 5

Author(s): Ruijia Shi and Xiuzhen Hu

Affiliation:

Keywords: Enzymes, enzyme subclasses, low-frequency power spectral density, pseudo amino acid composition, increment of diversity, support vector machine, jackknife test

Abstract: Based on enzyme sequence, using composite vectors with amino acid composition, low-frequency power spectral density, increment of diversity by combining a different form of pseudo amino acid composition to express the information of sequence, a support vector machine (SVM) for predicting enzyme subclasses is proposed. By the jackknife test, success rates of our algorithm are higher than other methods.

Cite this article as:

Shi Ruijia and Hu Xiuzhen, Predicting Enzyme Subclasses by Using Support Vector Machine with Composite Vectors, Protein & Peptide Letters 2010; 17 (5) . https://dx.doi.org/10.2174/092986610791112710

DOI https://dx.doi.org/10.2174/092986610791112710	Print ISSN 0929-8665
Publisher Name Bentham Science Publisher	Online ISSN 1875-5305