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 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.
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
Information Systems and Management
Expert Systems with Applications
ZHANG, Ya; CHU, Chao-Hsien; CHEN, Yixin; ZHA, Hongyuan; and JI, Xiang.
Splice Site Prediction Using Support Vector Machines with a Bayes Kernel. (2006). Expert Systems with Applications. 30, (1), 73-81. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/1163