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A novel representation of protein sequences for prediction of subcellular location using support vector machines

2005

Article

ei


As the number of complete genomes rapidly increases, accurate methods to automatically predict the subcellular location of proteins are increasingly useful to help their functional annotation. In order to improve the predictive accuracy of the many prediction methods developed to date, a novel representation of protein sequences is proposed. This representation involves local compositions of amino acids and twin amino acids, and local frequencies of distance between successive (basic, hydrophobic, and other) amino acids. For calculating the local features, each sequence is split into three parts: N-terminal, middle, and C-terminal. The N-terminal part is further divided into four regions to consider ambiguity in the length and position of signal sequences. We tested this representation with support vector machines on two data sets extracted from the SWISS-PROT database. Through fivefold cross-validation tests, overall accuracies of more than 87% and 91% were obtained for eukaryotic and prokaryotic proteins, respectively. It is concluded that considering the respective features in the N-terminal, middle, and C-terminal parts is helpful to predict the subcellular location. Keywords: subcellular location; signal sequence; amino acid composition; distance frequency; support vector machine; predictive accuracy

Author(s): Matsuda, S. and Vert, J-P. and Saigo, H. and Ueda, N. and Toh, H. and Akutsu, T.
Journal: Protein Science
Volume: 14
Pages: 2804-2813
Year: 2005
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1110/ps.051597405
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: Web

BibTex

@article{4604,
  title = {A novel representation of protein sequences for prediction of subcellular location using support vector machines},
  author = {Matsuda, S. and Vert, J-P. and Saigo, H. and Ueda, N. and Toh, H. and Akutsu, T.},
  journal = {Protein Science},
  volume = {14},
  pages = {2804-2813},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  year = {2005}
}