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.
Original language | English (US) |
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Title of host publication | Proceedings - Sixth IEEE Symposium on BioInformatics and BioEngineering, BIBE 2006 |
Pages | 201-208 |
Number of pages | 8 |
DOIs | |
State | Published - Dec 1 2006 |
Event | 6th IEEE Symposium on BioInformatics and BioEngineering, BIBE 2006 - Arlington, VA, United States Duration: Oct 16 2006 → Oct 18 2006 |
Other
Other | 6th IEEE Symposium on BioInformatics and BioEngineering, BIBE 2006 |
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Country/Territory | United States |
City | Arlington, VA |
Period | 10/16/06 → 10/18/06 |
All Science Journal Classification (ASJC) codes
- Biotechnology
- Computer Science Applications
- Information Systems