Abstract
A class of new kernels has been developed for vectors derived from a coding scheme of the k-peptide composition for protein sequences. Each kernel defines the biological similarity for two mapped k-peptide coding vectors. The mapping transforms a k-peptide coding vector into a new vector based on a matrix formed by high BLOSUM scores associated with pairs of k-peptides. In conjunction with the use of support vector machines, the effectiveness of the new kernels is evaluated against the conventional coding scheme of k-peptide (k ≤ 3) for the prediction of subcellular localizations of proteins in Gram-negative bacteria. It is demonstrated that the new method outperforms all the other methods in a 5-fold cross-validation.
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Lei, Z., Dai, Y. (2005). A Class of New Kernels Based on High-Scored Pairs of k-Peptides for SVMs and Its Application for Prediction of Protein Subcellular Localization. In: Priami, C., Zelikovsky, A. (eds) Transactions on Computational Systems Biology II. Lecture Notes in Computer Science(), vol 3680. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11567752_3
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DOI: https://doi.org/10.1007/11567752_3
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