A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Making large-scale support vector machine learning practical
Advances in kernel methods
Pairwise classification and support vector machines
Advances in kernel methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Training v-support vector regression: theory and algorithms
Neural Computation
Predicting allergenic proteins using wavelet transform
Bioinformatics
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
A hybrid WA-CPSO-LSSVR model for dissolved oxygen content prediction in crab culture
Engineering Applications of Artificial Intelligence
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Knowing the subcellular localization of proteins within the cell is an important step in elucidating its role in biological processes, its function and its potential as a drug target for disease diagnosis. As the number of complete genomes rapidly increases, accurate and efficient methods that automatically predict the subcellular localizations become more urgent. In the current paper, we developed a novel method that coupled the discrete wavelet transform with support vector machine based on the amino acid polarity to predict the subcellular localizations of prokaryotic and eukaryotic proteins. The results obtained by the jackknife test were quite promising, and indicated that the proposed method remarkably improved the prediction accuracy of subcellular locations, and could be as an effective and promising high-throughput method in the subcellular localization research.