Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/26226
Title: Eukaryotic protein subcellular localization based on local pairwise profile alignment SVM
Authors: Guo, J
Mak, MW 
Kung, SY
Keywords: Biology computing
Cellular biophysics
Feature extraction
Pattern classification
Proteins
Support vector machines
Issue Date: 2006
Publisher: IEEE
Source: Proceedings of the 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, 2006, 6-8 September 2006, Arlington, VA, p. 391-396 How to cite?
Abstract: This paper studies the use of profile alignment and support vector machines for subcellular localization. In the training phase, the profiles of all protein sequences in the training set are constructed by PSI-BLAST and the pairwise profile-alignment scores are used to form feature vectors for training a support vector machine (SVM) classifier. During testing, the profile of a query protein sequence is computed and aligned with all the profiles constructed during training to obtain a feature vector for classification by the SVM classifier. Tests on Reinhardt and Hubbard's eukaryotic protein dataset show that the total accuracy can reach 99.4%, which is significantly higher than those obtained by methods based on sequence alignments and amino acid composition. It was also found that the proposed method can still achieves a prediction accuracy of 96% even if none of the sequence pairs in the dataset contains more than 5% identity. This paper also demonstrates that the performance of the SVM is proportional to the degree of its kernel matrix meeting the Mercer's condition.
URI: http://hdl.handle.net/10397/26226
ISBN: 1-4244-0656-0
1-4244-0657-9 (E-ISBN)
ISSN: 1551-2541
DOI: 10.1109/MLSP.2006.275581
Appears in Collections:Conference Paper

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