Splice Site Prediction Using Support Vector Machines with a Bayes Kernel
One of the most important tasks in correctly annotating genes in higher organisms is to accurately locate the DNA splice sites. Although relatively high accuracy has been achieved by existing methods, most of these prediction methods are computationally extensive. Due to the enormous amount of DNA s...
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sg-smu-ink.sis_research-21622011-01-04T01:35:03Z Splice Site Prediction Using Support Vector Machines with a Bayes Kernel ZHANG, Ya CHU, Chao-Hsien CHEN, Yixin ZHA, Hongyuan JI, Xiang One of the most important tasks in correctly annotating genes in higher organisms is to accurately locate the DNA splice sites. Although relatively high accuracy has been achieved by existing methods, most of these prediction methods are computationally extensive. Due to the enormous amount of DNA sequences to be processed, the computational speed is an important issue to consider. In this paper, we present a new machine learning method for predicting DNA splice sites, which first applies a Bayes feature mapping (kernel) to project the data into a new feature space and then uses a linear Support Vector Machine (SVM) as a classifier to recognize the true splice sites. The computation time is linear to the number of sequences tested, while the performance is notably improved compared with the Naive Bayes classifier in terms of classification accuracy, precision, and recall. Our classification results are also comparable to the solution quality obtained by the SVMs with polynomial kernels, while the speed of our proposed method is significantly faster. This is a notable improvement in computational modeling considering the huge amount of DNA sequences to be processed. 2006-01-01T08:00:00Z text https://ink.library.smu.edu.sg/sis_research/1163 info:doi/10.1016/j.eswa.2005.09.052 http://dx.doi.org/10.1016/j.eswa.2005.09.052 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Splice site prediction SVM Support vector machines Bayes classifier Machine learning Splice Site Prediction Using Support Vector Machines with Bayes Kernel Computer Sciences Numerical Analysis and Scientific Computing |
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Splice site prediction SVM Support vector machines Bayes classifier Machine learning Splice Site Prediction Using Support Vector Machines with Bayes Kernel Computer Sciences Numerical Analysis and Scientific Computing ZHANG, Ya CHU, Chao-Hsien CHEN, Yixin ZHA, Hongyuan JI, Xiang Splice Site Prediction Using Support Vector Machines with a Bayes Kernel |
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One of the most important tasks in correctly annotating genes in higher organisms is to accurately locate the DNA splice sites. Although relatively high accuracy has been achieved by existing methods, most of these prediction methods are computationally extensive. Due to the enormous amount of DNA sequences to be processed, the computational speed is an important issue to consider. In this paper, we present a new machine learning method for predicting DNA splice sites, which first applies a Bayes feature mapping (kernel) to project the data into a new feature space and then uses a linear Support Vector Machine (SVM) as a classifier to recognize the true splice sites. The computation time is linear to the number of sequences tested, while the performance is notably improved compared with the Naive Bayes classifier in terms of classification accuracy, precision, and recall. Our classification results are also comparable to the solution quality obtained by the SVMs with polynomial kernels, while the speed of our proposed method is significantly faster. This is a notable improvement in computational modeling considering the huge amount of DNA sequences to be processed. |
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ZHANG, Ya CHU, Chao-Hsien CHEN, Yixin ZHA, Hongyuan JI, Xiang |
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ZHANG, Ya CHU, Chao-Hsien CHEN, Yixin ZHA, Hongyuan JI, Xiang |
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ZHANG, Ya |
title |
Splice Site Prediction Using Support Vector Machines with a Bayes Kernel |
title_short |
Splice Site Prediction Using Support Vector Machines with a Bayes Kernel |
title_full |
Splice Site Prediction Using Support Vector Machines with a Bayes Kernel |
title_fullStr |
Splice Site Prediction Using Support Vector Machines with a Bayes Kernel |
title_full_unstemmed |
Splice Site Prediction Using Support Vector Machines with a Bayes Kernel |
title_sort |
splice site prediction using support vector machines with a bayes kernel |
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Institutional Knowledge at Singapore Management University |
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2006 |
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https://ink.library.smu.edu.sg/sis_research/1163 http://dx.doi.org/10.1016/j.eswa.2005.09.052 |
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