Please use this identifier to cite or link to this item: https://doi.org/10.1287/ijoc.1090.0360
Title: Least-squares support vector machine approach to viral replication origin prediction
Authors: Cruz-Cano, R.
Chew, D.S.H. 
Choi, K.-P. 
Leung, M.-Y.
Keywords: Caudoviruses
Feature selection
Herpesviruses
Least-squares support vector machines
Replication origins
Issue Date: Jun-2010
Citation: Cruz-Cano, R., Chew, D.S.H., Choi, K.-P., Leung, M.-Y. (2010-06). Least-squares support vector machine approach to viral replication origin prediction. INFORMS Journal on Computing 22 (3) : 457-470. ScholarBank@NUS Repository. https://doi.org/10.1287/ijoc.1090.0360
Abstract: 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. © 2010 INFORMS.
Source Title: INFORMS Journal on Computing
URI: http://scholarbank.nus.edu.sg/handle/10635/105193
ISSN: 10919856
DOI: 10.1287/ijoc.1090.0360
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