Predicting Enzyme Subclasses by Using Support Vector Machine with Composite Vectors

Ruijia      Shi; Xiuzhen      Hu

doi:10.2174/092986610791112710

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.

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

« Previous Next »

Rights & Permissions Print Cite

Protein & Peptide Letters

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.

Export Options

Export File:

RIS (for EndNote, Reference Manager, ProCite)

BibTeX

Text

Content:

Citation Only

Citation and Abstract

About this article

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