Support vector machines for separation of mixed plant-pathogen EST collections based on codon usage

Bioinformatics

Volumn 21, Issue 8, 2005, Pages 1383-1388

  Friedel, Caroline C ^a,b   Jahn, Katharina H V ^a,b   Sommer, Selina ^a,b   Rudd, Stephen ^c   Mewes, Hans W ^b,d   Tetko, Igor V ^d  

^a UNIVERSITY OF MUNICH   (Germany)

^b TECHNICAL UNIVERSITY OF MUNICH   (Germany)

^c TURKU CENTRE FOR BIOTECHNOLOGY   (Finland)

^d INSTITUTE OF EPIDEMIOLOGY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ANALYTIC METHOD; ARTICLE; BARLEY; CLASSIFICATION; CODON USAGE; COMPARATIVE STUDY; COMPUTER PROGRAM; CONTROLLED STUDY; EXPRESSED SEQUENCE TAG; FUNGAL GENETICS; GENETIC ALGORITHM; GENETIC CODE; LEARNING; MACHINE; METHODOLOGY; NONHUMAN; PLANT GENETICS; PLANT PATHOGEN INTERACTION; PREDICTION; PRIORITY JOURNAL; PROBABILITY; SEQUENCE ANALYSIS; SEQUENCE HOMOLOGY; STATISTICAL SIGNIFICANCE;

BLUMERIA GRAMINIS; HORDEUM VULGARE;

EID: 17444396383     PISSN: 13674803     EISSN: 13674811     Source Type: Journal    
DOI: 10.1093/bioinformatics/bti200     Document Type: Article

References (21)

1. Boser,B.E., Guyon,I.M. and Vapnik,V. (1992) A training algorithm for optimal margin classifiers. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory, Pittsburg, PA, USA, pp. 144-152. ACM press, New York, NY.
2. Burges,C.J.C. (1998) A tutorial on support vector machines for pattern recognition. Data Mining Knowl. Discov., 2, 121-167.
3. Chang,C.-C. and Lin,C.-J. (2001) LIBSVM: a library for support vector machines.
4. Duret,L. and Mouchiroud,D. (1999) Expression pattern and, surprisingly, gene length shape codon usage in Caenorhabditis, Drosophila, and Arabidopsis. Proc. Natl Acad. Sci., USA, 96 4482-4487.
5. Fennoy,S.L. and Bailey-Serres,J. (1993) Synonymous codon usage in Zea mays L. nuclear genes is varied by levels of C and G-ending codons. Nucleic Acids Res., 21, 5294-5300.
6. Heumann,K. and Mewes,H.W. (1996) The Hashed Position Tree (HPT): a suffix tree variant for large data sets stored on slow mass storage devices. In Ziviani,N., Baeza-Yates,A. and Guimaraes,G. (eds), Proceedings of the Third South American Workshop on String Processing, Recife, Brazil, pp. 101-115.
7. Hsiang,T. and Goodwin,P.H. (2003) Distinguishing plant and fungal sequences in ESTs from infected plant tissues. J. Microbiol. Methods, 54, 339-351.
8. Huang,X. and Madan,A. (1999) CAP3: a DNA sequence assembly program. Genome Res., 9, 868-877.
9. Joachims,T. (1999) Making large-scale SVM learning practical. In Schölkopf,B., Burges,C. and Smola,A. (eds), Advances in Kernel Methods - Support Vector Learning. MIT Press, Cambridge, MA.
10. Kawabe,A. and Miyashita,N.T. (2003) Patterns of codon usage bias in three dicot and four monocot plant species. Genes Genet. Syst. 78, 343-352.
11. Keerthi,S.S. and Lin,C.-J. (2003) Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Comput., 15 1667-1689.
12. Koski,L.B. and Golding,G.B. (2001) The closest BLAST hit is often not the nearest neighbor. J. Mol. Evol., 52, 540-542.
13. Lin,H.-T. and Lin,C.-J. (2003) A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods. Technical report, Department of Computer Science and Information Engineering, National Taiwan University.
14. Maor,R., Kosman,E., Golobinski,R., and Goodwin,P. and Sharon,A. (2003) PF-IND: probability algorithm and software for separation of plant and fungal sequences. Curr. Genet., 43, 296-302.
15. Platt,J. (1998) Fast training of support vector machines using sequential minimal optimization. In Schölkopf,B., Burges,C. and Smola,A. (eds), Advances in Kernel Methods - Support Vector Learning MIT Press, Cambridge, MA, pp.185-208.
16. Rudd,S., Mewes,H.W. and Mayer,K.F. (2003) Sputnik: a database platform for comparative plant genomics. Nucleic Acids Res., 31 128-132.
17. Sharp,P.M., Cowe,E., Higgins,D.G., Shields,D.C., Wolfe,K.H. and Wright,F. (1988) Codon usage patterns in Escherichia coli, Bacillus subtilis, Saccharomyces cerevisiae, Schizosaccharomyces pombe, Drosophila melanogaster and Homo sapiens: a review of the considerable within-species diversity. Nucleic Acids Res., 16 8207-8211.
18. Slater,G. (2000) Algorithms for analysis of ESTs. Ph.D. thesis, University of Cambridge, UK.
19. Tetko,I.V. (2002) Neural network studies. 4. Introduction to associative neural networks. J. Chem. Inf. Comput. Sci., 42, 717-728.
20. Vapnik,V. (1995) The Nature of Statistical Learning Theory Springer-Verlag, New York, NY.
21. Witten,I.H. and Frank,E. (2000) Data Mining: Practical Machine Learning Tools with Java Implementations. Morgan Kaufmann, San Francisco, CA.