ESLpred -- SVM-based Method for Subcellular Localization of Eukaryotic Proteins Using Dipeptide Composition and PSI-BLAST

What you can do:
Predict the subcellular location of eukaryotic proteins from their different features such as amino acid composition, dipeptide composition and physico-chemical properties.
  • In order to improve the prediction accuracy, a hybrid module was developed using all features of a protein, which consisted of an input vector of 458 dimensions (400 dipeptide compositions, 33 properties, 20 amino acid compositions of the protein and 5 from PSI-BLAST output).
  • Using this hybrid approach, the prediction accuracies of nuclear, cytoplasmic, mitochondrial and extracellular proteins reached 95.3, 85.2, 68.2 and 88.9%, respectively.
  • The overall prediction accuracy of SVM modules based on amino acid composition, physico-chemical properties, dipeptide composition and the hybrid approach was 78.1, 77.8, 82.9 and 88.0%, respectively.
  • protein localizations
  • protein localization prediction tool
  • protein subcellular localization prediction tool
Literature & Tutorials:
PubMed Link: ESLpred
This record last updated: 05-23-2005
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