A new kernel method for RNA classification

Xiaoming Wu; Jason Wang; Katherine G. Herbert

doi:10.1109/BIBE.2006.253335

A new kernel method for RNA classification

Xiaoming Wu, Jason Wang, Katherine G. Herbert

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

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)
Title of host publication	Proceedings - Sixth IEEE Symposium on BioInformatics and BioEngineering, BIBE 2006
Pages	201-208
Number of pages	8
DOIs	https://doi.org/10.1109/BIBE.2006.253335
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
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

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10.1109/BIBE.2006.253335

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title = "A new kernel method for RNA classification",

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.",

author = "Xiaoming Wu and Jason Wang and Herbert, {Katherine G.}",

year = "2006",

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day = "1",

doi = "10.1109/BIBE.2006.253335",

language = "English (US)",

isbn = "0769527272",

pages = "201--208",

booktitle = "Proceedings - Sixth IEEE Symposium on BioInformatics and BioEngineering, BIBE 2006",

note = "6th IEEE Symposium on BioInformatics and BioEngineering, BIBE 2006 ; Conference date: 16-10-2006 Through 18-10-2006",

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TY - GEN

T1 - A new kernel method for RNA classification

AU - Wu, Xiaoming

AU - Wang, Jason

AU - Herbert, Katherine G.

PY - 2006/12/1

Y1 - 2006/12/1

N2 - 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.

AB - 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.

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U2 - 10.1109/BIBE.2006.253335

DO - 10.1109/BIBE.2006.253335

M3 - Conference contribution

AN - SCOPUS:34547482842

SN - 0769527272

SN - 9780769527277

SP - 201

EP - 208

BT - Proceedings - Sixth IEEE Symposium on BioInformatics and BioEngineering, BIBE 2006

T2 - 6th IEEE Symposium on BioInformatics and BioEngineering, BIBE 2006

Y2 - 16 October 2006 through 18 October 2006

ER -

A new kernel method for RNA classification

Abstract

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