A New Kernel Method for RNA Classification
Document Type
Conference Proceeding
Publication Date
12-1-2006
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 Euclidean space. In this paper 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 with support vector machines. 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.
DOI
10.1109/BIBE.2006.253335
Montclair State University Digital Commons Citation
Wu, Xiaoming; Wang, Jason T.L.; and Herbert, Katherine, "A New Kernel Method for RNA Classification" (2006). Department of Computer Science Faculty Scholarship and Creative Works. 50.
https://digitalcommons.montclair.edu/compusci-facpubs/50