A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Systematic and automated discovery of patterns in PROSITE families
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Effective hidden Markov models for detecting splicing junction sites in DNA sequences
Information Sciences: an International Journal
Pattern Discovery in Biomolecular Data: Tools, Techniques, and Applications
Pattern Discovery in Biomolecular Data: Tools, Techniques, and Applications
Computing similarity between RNA structures
Theoretical Computer Science
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Discovering Patterns and Subfamilies in Biosequences
Proceedings of the Fourth International Conference on Intelligent Systems for Molecular Biology
Color Set Size Problem with Application to String Matching
CPM '92 Proceedings of the Third Annual Symposium on Combinatorial Pattern Matching
A survey of kernels for structured data
ACM SIGKDD Explorations Newsletter
GeneScout: a data mining system for predicting vertebrate genes in genomic DNA sequences
Information Sciences: an International Journal - Special issue: Soft computing data mining
New techniques for extracting features from protein sequences
IBM Systems Journal - Deep computing for the life sciences
Data Mining in Bioinformatics
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Design of an RNA structural motif database
International Journal of Computational Intelligence in Bioinformatics and Systems Biology
Detecting conserved RNA secondary structures in viral genomes: the RADAR approach
BioSurveillance'07 Proceedings of the 2nd NSF conference on Intelligence and security informatics: BioSurveillance
Toward an integrated RNA motif database
DILS'07 Proceedings of the 4th international conference on Data integration in the life sciences
Protein interaction detection in sentences via Gaussian Processes: a preliminary evaluation
International Journal of Data Mining and Bioinformatics
SVM-RFE based feature selection for tandem mass spectrum quality assessment
International Journal of Data Mining and Bioinformatics
Identification of true EST alignments for recognising transcribed regions
International Journal of Data Mining and Bioinformatics
Robust classification ensemble method for microarray data
International Journal of Data Mining and Bioinformatics
International Journal of Data Mining and Bioinformatics
In silico prediction of noncoding RNAs using supervised learning and feature ranking methods
International Journal of Bioinformatics Research and Applications
In silico prediction of noncoding RNAs using supervised learning and feature ranking methods
International Journal of Bioinformatics Research and Applications
Selection of vocal features for Parkinson's Disease diagnosis
International Journal of Data Mining and Bioinformatics
Modelling splice sites with locality-sensitive sequence features
International Journal of Data Mining and Bioinformatics
Research Article: Novel features for identifying A-minors in three-dimensional RNA molecules
Computational Biology and Chemistry
Multi-level clustering support vector machine trees for improved protein local structure prediction
International Journal of Data Mining and Bioinformatics
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Support Vector Machines (SVMs) are a state-of-the-art machine learning tool widely used in speech recognition, image processing and biological sequence analysis. An essential step in SVMs is to devise a kernel function to compute the similarity between two data points. In this paper we review recent advances of using SVMs for RNA classification. In particular we present a new kernel that takes advantage of both global and local structural information in RNAs and uses the information together to classify RNAs. Experimental results demonstrate the good performance of the new kernel and show that it outperforms existing kernels when applied to classifying non-coding RNA sequences.