Making large-scale support vector machine learning practical
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Using the Fisher Kernel Method to Detect Remote Protein Homologies
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
A class of edit kernels for SVMs to predict translation initiation sites in eukaryotic mRNAs
RECOMB '04 Proceedings of the eighth annual international conference on Resaerch in computational molecular biology
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part II
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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.