pSLIP: SVM based protein subcellular localization prediction using multiple physicochemical properties. Results: In this paper, we propose a new algorithm called pSLIP which uses Support Vector Machines (SVMs) in conjunction with multiple physicochemical properties of amino acids to predict protein subcellular localization in eukaryotes across six different locations, namely, chloroplast, cytoplasmic, extracellular, mitochondrial, nuclear and plasma membrane. The algorithm was applied to the dataset provided by Park and Kanehisa and we obtained prediction accuracies for the different classes ranging from 87.7% – 97.0% with an overall accuracy of 93.1%. Conclusion: This study presents a physicochemical property based protein localization prediction algorithm. Unlike other algorithms, contextual information is preserved by dividing the protein sequences into clusters. The prediction accuracy shows an improvement over other algorithms based on various types of amino acid composition (single, pair and gapped pair). We have also implemented a web server to predict protein localization across the six classes (available at http://pslip.bii.a-star.edu.sg/).
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References in zbMATH (referenced in 6 articles )
Showing results 1 to 6 of 6.
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- Nanni, Loris; Shi, Jian-Yu; Brahnam, Sheryl; Lumini, Alessandra: Protein classification using texture descriptors extracted from the protein backbone image (2010)
- Nanni, Loris; Lumini, Alessandra: A genetic approach for building different alphabets for peptide and protein classification (2008) ioport
- Shah, Anuj R.; Oehmen, Christopher S.; Harper, Jill; Webb-Robertson, Bobbie-Jo M.: Integrating subcellular location for improving machine learning models of remote homology detection in eukaryotic organisms (2007)