An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
IEEE Intelligent Systems
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Sequence-driven features for prediction of subcellular localization of proteins
Pattern Recognition
Using pseudo amino acid composition to predict protein subnuclear localization: Approached with PSSM
Pattern Recognition Letters
Generalized Needleman-Wunsch algorithm for the recognition of T-cell epitopes
Expert Systems with Applications: An International Journal
Fusion of feature selection methods for pairwise scoring SVM
Neurocomputing
PairProSVM: Protein Subcellular Localization Based on Local Pairwise Profile Alignment and SVM
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A solution to the curse of dimensionality problem in pairwise scoring techniques
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
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Predicting the destination of a protein in a cell is important for annotating the function of the protein. Recent advances have allowed us to develop more accurate methods for predicting the subcellular localization of proteins. One of the most important factors for improving the accuracy of these methods is related to the introduction of new useful features for protein sequences. In this paper we present a new method for extracting appropriate features from the sequence data by computing pairwise sequence alignment scores. As a classifier, support vector machine (SVM) is used. The overall prediction accuracy evaluated by the jackknife validation technique reached 94.70% for the eukaryotic non-plant data set and 92.10% for the eukaryotic plant data set, which is the highest prediction accuracy among the methods reported so far with such data sets. Our experimental results confirm that our feature extraction method based on pairwise sequence alignment is useful for this classification problem.