Least-squares support vector machine approach to viral replication origin prediction

INFORMS Journal on Computing

Volumn 22, Issue 3, 2010, Pages 457-470

  Cruz Cano, Raul ^a   Chew, David S H ^b,c   Choi, Kwok Pui ^b   Leung, Ming Ying ^d  

^a TEXAS A AND M UNIVERSITY TEXARKANA   (United States)

^b NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^c UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

^d UNIVERSITY OF TEXAS AT EL PASO   (United States)

Author keywords

Caudoviruses; Feature selection; Herpesviruses; Least squares support vector machines; Replication origins

Indexed keywords

EID: 77955484544     PISSN: 10919856     EISSN: 15265528     Source Type: Journal    
DOI: 10.1287/ijoc.1090.0360     Document Type: Article

References (47)

1. Abe, S. 2005. Support Vector Machines for Pattern Classification Advances in Pattern Recognition. Springer-Verlag, New York.
2. Abe, S. 2008. Sparse least squares support vector machines by forward selection based on linear discriminant analysis. ANNPR '08: Proc. 3rd IAPR Workshop on Artificial Neural Networks in Pattern Recognition. Springer-Verlag, Berlin, 54-65.
3. Aizenberg, I., C. Moraga. 2006. Multilayer feedforward neural network based on multi-valued neurons (MLMVN) and a backpropagation learning algorithm. Soft Comput. 11 (2) 169-183.
4. Babuska, R. 2000. Fuzzy clustering algorithms with applications to rule extraction. P. S. Szczepaniak, J. Kacprzyk, P. J. G. Lisboa, eds. Fuzzy Systems in Medicine, Studies in Fuzziness and Soft. Computing, Vol. 41. Physica-Verlag, Heidelberg, Germany 139-173.
5. Borin, A., M. F. Ferrão, C. Mello, L. Cordi, L. C. M. Pataca, N. Durán, R. J. Poppi. 2007. Quantification of lactobacillus in fermented milk by multivariate image analysis with least-squares support-vector machines. Anal. Bioanal. Chem. 387 (3) 1105-1112.
6. Brüssow, H. 2001. Phages of dairy bacteria. Annual Rev. Microbiol. 55 283-303.
7. Caballero, J., L. Fernández, M. Garriga, J. I. Abreu, S. Collina, M. Fernández. 2007. Proteometric study of ghrelin receptor function variations upon mutations using amino acid sequence autocorrelation vectors and genetic algorithm-based least square support vector machines. J. Molecular, Graphics Model. 26 (1) 166-178.
8. Canu, S., Y. Grandvalet, V. Guigue, A. Rakotomamonjy. 2005. SVM and kernel methods Matlab toolbox. Perception Systèmes et Information. INSA de Rouen, Rouen, France. http://asi.insarouen.fr/enseignants/~arakotom/toolbox/ index.html.
9. Capriotti, E., R. Calabrese, R. Casadio. 2006. Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information. Bioinformatics 22 (22) 2729-2734.
10. Chang, C. C., C. J. Lin. 2001. LIBSVM: A library for support vector machines. http://www.csie.ntu.edu.tw/~cjlin/libsvm.
11. Cheng, J., A. Randall, P. Baldi. 2006. Prediction of protein stability changes for single-site mutations using support vector machines. Proteins 62 (4) 1125-1132.
12. Chew, D. S. H., K. P. Choi, M.-Y. Leung. 2005. Scoring schemes of palindrome clusters for more sensitive prediction of replication origins in herpesviruses. Nucleic Acids Res. 33 (15) e134.
13. Chew, D. S. H., M.-Y. Leung, K. P. Choi. 2007. AT excursion: A new approach to predict replication origins in viral genomes by locating AT-rich regions. BMC Bioinformatics 8 163.
14. Cruz-Cano, R., D. Chandran, M.-Y. Leung. 2007. Computational prediction of replication origins in herpesviruses. Comput. Intelligence Bioinform. Comput. Biol. 2007 (CIBCB '07) IEEE Sympos., Honolulu, HI. IEEE, Piscataway, NJ, 283-290.
15. Delecluse, H.-J., W. Hammerschmidt. 2000. The genetic approach to the Epstein-Barr virus: From basic virology to gene therapy. Molecular Pathology 53 270-279.
16. Deng, H., J. T. Chu, N.-H. Park, R. Sun. 2004. Identification of cis sequences required for lytic DNA replication and packaging of murine gammaherpesvirus 68. J. Virology 78 (17) 9123-9131.
17. Doran, M., D. S. Raicu, J. D. Furst, R. Settimi, M. Schipma, D. P. Chandler. 2007. Oligonucleotide microarray identification of Bacillus anthracis strains using support vector machines. Bioinformatics 23 (4) 487-492.
18. Grandvalet, Y., S. Canu. 2002. Adaptive scaling for feature selection in SVMs. S. Becker, S. Thrun, K. Obermayer, S. Becker, S. Thrun, K. Obermayer, eds. Neural Information Processing Systems 15. MIT Press, Cambridge, MA, 553-560.
19. Guyon, I., J. Weston, S. Barnhill, V. Vapnik. 2002. Gene selection for cancer classification using support vector machines. Machine Learn. 46 (1-3) 389-422.
20. Hartline, C. B., E. A. Harden, S. L. Williams-Aziz, N. L. Kushner, R. J. Brideau, E. R. Kern. 2005. Inhibition of herpesvirus replication by a series of 4-oxo-dihydroquinolines with viral polymerase activity. Antiviral Res. 65 (2) 97-105.
21. Jernigan, R. W., R. H. Baran. 2002. Pervasive properties of the genomic signature. BMC Genomics 3 23.
22. Karlin, S., C. Burge. 1995. Dinucleotide relative abundance extremes: A genomic signature. Trends Genetics 11 (7) 283-290.
23. Kong, W., K. W. Choo. 2007. Predicting single nucleotide polymorphisms (SNP) from DNA sequence by support vector machine. Frontiers Biosci. 12 1610-1614.
24. Lee, K., S. Gunn, C. Harris, P. Reed. 2001. Classification of unbalanced data with transparent kernels. Neural Networks, 2001. Proc. IJCNN '01. Internat. Joint Conf., Washington, DC, Vol. 4. INNS, Madison, WI, 2410-2415.
25. Leung, M.-Y., K. P. Choi, A. Xia, L. H. Y. Chen. 2005. Nonrandom clusters of palindromes in herpesvirus genomes. J. Comput. Biol. 12 (3) 331-354.
26. Luo, F., Y.-G. Xu, J.-Z. Cao. 2005. Elevator traffic flow prediction with least squares support vector machines. Proc. Internat. Conf. Machine Learn. Cybernetics, Guangzhou, China, Vol. 7. IEEE, Piscataway, NJ, 4266-4270.
27. Masse, M. J., S. Karlin, G. A. Schachtel, E. S. Mocarski. 1992. Human cytomegalovirus origin of DNA replication (oriLyt) resides within a highly complex repetitive region. Proc. Natl. Acad. Sci. USA 89 (12) 5246-5250.
28. Newlon, C. S., J. F. Theis. 2002. DNA replication joins the revolution: Whole-genome views of DNA replication in budding yeast. Bioessays 24 (4) 300-304.
29. Passerini, A., M. Punta, A. Ceroni, B. Rost, P. Frasconi. 2006. Identifying cysteines and histidines in transition-metal-binding sites using support vector machines and neural networks. Proteins 65 (2) 305-316.
30. Reisman, D., J. Yates, B. Sugden. 1985. A putative origin of replication of plasmids derived from Epstein-Barr virus is composed of two cis-acting components. Molecular Cellular Biol. 5 (8) 1822-1832.
31. Rutkowska, D., A. Starczewski. 2000. Fuzzy inference neural networks and their applications to medical diagnosis. P. S. Szczepaniak, J. Kacprzyk, P. J. G. Lisboa, eds. Fuzzy Systems in Medicine, Studies in Fuzziness and Soft Computing, Vol. 41. Physica-Verlag, Heidelberg, Germany, 503-518.
32. Saeys, Y., I. Inza, P. Larrañaga. 2007. A review of feature selection techniques in bioinformatics. Bioinformatics 23 (19) 2507-2517.
33. Segurado, M., A. de Luis, F. Antequera. 2003. Genome-wide distribution of DNA replication origins at A+T-rich islands in Schizosaccharomyces pombe. EMBO Rep. 4 (11) 1048-1053.
34. Suykens, J. A. K. 2000. Least squares support vector machines for classification and nonlinear modeling. Neural Network World 10 29-47.
35. Suykens, J. A. K., J. Vandewalle. 1999. Least squares support vector machine classifiers. Neural Processing Lett. 9 (3) 293-300.
36. Suykens, J. A. K., J. Vandewalle, B. De Moor. 2001. Optimal control by least squares support vector machines. Neural Networks 14 (1) 23-35.
37. Suykens, J. A. K., T. Van Gestel, J. De Brabanter, B. De Moor, J. Vandewalle. 2002. Least Squares Support Vector Machines. World Scientific Publishing, Singapore.
38. Van Gestel, T., J. A. K. Suykens, B. Baesens, S. Viaene, J. Vanthienen, G. Dedene, B. D. Moor, J. Vandewalle. 2001. Benchmarking least squares support vector classifiers. Machine Learn. 54 (1) 5-32.
39. van Passel, M. W. J., E. E. Kuramae, A. C. M. Luyf, A. Bart, T. Boekhout. 2006. The reach of the genome signature in prokaryotes. BMC Evolutionary Biol. 6 84.
40. Vapnik, V. N. 1995. The Nature of Statistical Learning Theory. Springer-Verlag, New York.
41. Villarreal, E. C. 2003. Current and potential therapies for the treatment of herpes-virus infections. Progress Drug Res. 60 263-307.
42. Wei, L., Z. Chen, J. Li, W. Xu. 2007. Sparse and robust least squares support vector machine: A linear programming formulation. Proc. IEEE Internat. Conf. Grey Systems Intelligent Services GSIS 2007, Nanjing, China. IEEE, Piscataway, NJ, 1134-1138.
43. Weller, S. K., A. Spadaro, J. E. Schaffer, A. W. Murray, A. M. Maxam, P. A. Schaffer. 1985. Cloning, sequencing, and functional analysis of oriL, a herpes simplex virus type 1 origin of DNA synthesis. Molecular Cellular Biol. 5 (5) 930-942.
44. Weston, J., S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio, V. Vapnik. 2000. Feature selection for SVMS. Advances in Neural Information Processing Systems 13. MIT Press, Cambridge, MA, 668-674.
45. Wishart, D. S., C. Knox, A. C. Guo, S. Shrivastava, M. Hassanali, P. Stothard, Z. Chang, J. Woolsey. 2006. DrugBank: A comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 34 (Database issue) D668-D672.
46. Yao, X., H. Liu, R. Zhang, M. Liu, Z. Hu, A. Panaye, J. P. Doucet, B. Fan. 2005. QSAR and classification study of 1,4-dihydropyridine calcium channel antagonists based on least squares support vector machines. Molecular Pharmaceutics 2 (5) 348-356.
47. Zhu, Y., L. Huang, D. G. Anders. 1998. Human cytomegalovirus oriLyt sequence requirements. J. Virol. 72 (6) 4989-4996.