Abstract
A multi-scale parameterization approach, factor analysis scales of generalized amino acid information combined with auto cross covariance, was used to develop quantitative sequence-activity models of peptides using support vector machines. The results demonstrated that this approach could well characterize sequence features of the peptides studied.
Keywords: Factor analysis scales of generalized amino acid information (FASGAI), auto cross covariance (ACC), FASGAI-ACC, quantitative sequence-activity model (QSAM), support vector machines (SVM)
Protein & Peptide Letters
Title: A Multi-Scale Parameterization Approach of Peptides for Quantitative Sequence-Activity Models
Volume: 17 Issue: 5
Author(s): Weihuan Niu, Qingyou Xia and Guizhao Liang
Affiliation:
Keywords: Factor analysis scales of generalized amino acid information (FASGAI), auto cross covariance (ACC), FASGAI-ACC, quantitative sequence-activity model (QSAM), support vector machines (SVM)
Abstract: A multi-scale parameterization approach, factor analysis scales of generalized amino acid information combined with auto cross covariance, was used to develop quantitative sequence-activity models of peptides using support vector machines. The results demonstrated that this approach could well characterize sequence features of the peptides studied.
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Cite this article as:
Niu Weihuan, Xia Qingyou and Liang Guizhao, A Multi-Scale Parameterization Approach of Peptides for Quantitative Sequence-Activity Models, Protein & Peptide Letters 2010; 17 (5) . https://dx.doi.org/10.2174/092986610791112738
DOI https://dx.doi.org/10.2174/092986610791112738 |
Print ISSN 0929-8665 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5305 |
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