Eukaryotic protein subcellular localization based on local pairwise profile alignment SVM

Jian Guo, Man Wai Mak, Sun Yuan Kung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, MLSP 2006
PublisherIEEE Computer Society
Pages391-396
Number of pages6
ISBN (Print)1424406560, 9781424406562
DOIs
StatePublished - Jan 1 2006
Event2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, MLSP 2006 - Maynooth, Ireland
Duration: Sep 6 2006Sep 8 2006

Publication series

NameProceedings of the 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, MLSP 2006

Other

Other2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, MLSP 2006
CountryIreland
CityMaynooth
Period9/6/069/8/06

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Signal Processing

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  • Cite this

    Guo, J., Mak, M. W., & Kung, S. Y. (2006). Eukaryotic protein subcellular localization based on local pairwise profile alignment SVM. In Proceedings of the 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, MLSP 2006 (pp. 391-396). [4053680] (Proceedings of the 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, MLSP 2006). IEEE Computer Society. https://doi.org/10.1109/MLSP.2006.275581