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Title: |
Kernel design for RNA classification using Support Vector Machines |
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Author: |
Jason T.L. Wang, Xiaoming Wu 
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Address: |
Bioinformatics Center and Department of Computer Science, New Jersey Institute of Technology, University Heights, Newark, NJ 07102, USA. ' Department of Computer Science, Math and Engineering, Shepherd University, Shepherdstown, WV 25443, USA |
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Journal: |
International Journal of Data Mining and Bioinformatics 2006 - Vol. 1, No.1 pp. 57 - 76 |
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Abstract: |
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. |
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Keywords: |
data mining; bioinformatics; support vector machines; SVMs; RNA sequences; RNA structure; kernel methods; RNA classification; machine learning; ribonucleic acid. |
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DOI: |
10.1504/IJDMB.2006.009921 |
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