Eukaryotic protein subcellular localization based on local pairwise profile alignment SVM

Jian Guo, Man Wai Mak, Sun Yuan Kung

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

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
Title of host publicationProceedings of the 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, MLSP 2006
Pages391-396
Number of pages6
DOIs
Publication statusPublished - 1 Dec 2007
Event2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, MLSP 2006 - Maynooth, Ireland
Duration: 6 Sep 20068 Sep 2006

Conference

Conference2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, MLSP 2006
Country/TerritoryIreland
CityMaynooth
Period6/09/068/09/06

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Signal Processing

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