The nature of statistical learning theory
The nature of statistical learning theory
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Optimal control by least squares support vector machines
Neural Networks
Benchmarking Least Squares Support Vector Machine Classifiers
Machine Learning
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A review of feature selection techniques in bioinformatics
Bioinformatics
ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
Comparison of feature selection techniques for viral DNA replication origin prediction
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
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Replication of their DNA genomes is a central step in the reproduction of many viruses. Procedures to find replication origins, which are initiation sites of the DNA replication process, are therefore of great importance for controlling the growth and spread of such viruses. Existing computational methods for viral replication origin prediction have mostly been tested within the family of herpesviruses. This paper proposes a new approach by least-squares support vector machines (LS-SVMs) and tests its performance not only on the herpes family but also on a collection of caudoviruses coming from three viral families under the order of caudovirales. The LS-SVM approach provides sensitivities and positive predictive values superior or comparable to those given by the previous methods. When suitably combined with previous methods, the LS-SVM approach further improves the prediction accuracy for the herpesvirus replication origins. Furthermore, by recursive feature elimination, the LS-SVM has also helped find the most significant features of the data sets. The results suggest that the LS-SVMs will be a highly useful addition to the set of computational tools for viral replication origin prediction and illustrate the value of optimization-based computing techniques in biomedical applications.