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Least-Squares Support Vector Machine Approach to Viral Replication Origin Prediction

Department of Computer Science, Texas A&M University–Texarkana, Texarkana, Texas 75505
Department of Statistics and Applied Probability, National University of Singapore, Singapore 117546, Singapore, and Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, California 90089
Department of Statistics and Applied Probability, National University of Singapore, Singapore 117546, Singapore
Bioinformatics Program and Department of Mathematical Sciences, University of Texas at El Paso, El Paso, Texas 79968
raul.cruz-cano{at}tamut.edu
david.chew{at}nus.edu.sg
stackp{at}nus.edu.sg
mleung{at}utep.edu

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.

Key words: replication origins; herpesviruses; caudoviruses; feature selection; least-squares support vector machines
History: received August 2008; revised June 2009; accepted July 2009.