ProLoc: Prediction of protein subnuclear localization using SVM with automatic selection from physicochemical composition features

Wen Lin Huang, Chun Wei Tung, Hui Ling Huang, Shiow Fen Hwang, Shinn-Ying Ho*

*Corresponding author for this work

Research output: Contribution to journalArticle

62 Scopus citations

Abstract

Accurate prediction methods of protein subnuclear localizations rely on the cooperation between informative features and classifier design. Support vector machine (SVM) based learning methods are shown effective for predictions of protein subcellular and subnuclear localizations. This study proposes an evolutionary support vector machine (ESVM) based classifier with automatic selection from a large set of physicochemical composition (PCC) features to design an accurate system for predicting protein subnuclear localization, named ProLoc. ESVM using an inheritable genetic algorithm combined with SVM can automatically determine the best number m of PCC features and identify m out of 526 PCC features simultaneously. To evaluate ESVM, this study uses two datasets SNL6 and SNL9, which have 504 proteins localized in 6 subnuclear compartments and 370 proteins localized in 9 subnuclear compartments. Using a leave-one-out cross-validation, ProLoc utilizing the selected m = 33 and 28 PCC features has accuracies of 56.37% for SNL6 and 72.82% for SNL9, which are better than 51.4% for the SVM-based system using k-peptide composition features applied on SNL6, and 64.32% for an optimized evidence-theoretic k-nearest neighbor classifier utilizing pseudo amino acid composition applied on SNL9, respectively.

Original languageEnglish
Pages (from-to)573-581
Number of pages9
JournalBioSystems
Volume90
Issue number2
DOIs
StatePublished - 1 Sep 2007

Keywords

  • Amino acid composition
  • Genetic algorithm
  • k-Nearest neighbor
  • Physicochemical property
  • Prediction
  • Subnuclear localization
  • Support vector machine

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