RASE: Recognition of alternatively spliced exons in C.elegans

Bioinformatics

Volumn 21, Issue SUPPL. 1, 2005, Pages

  Rätsch, Gunnar ^a   Sonnenburg, S ^b   Scholkopf B ^c  

^a FRIEDRICH MIESCHER LABORATORY OF THE MAX PLANCK SOCIETY   (Germany)

^b FRAUNHOFER FIRST   (Germany)

^c MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

MESSENGER RNA;

ALTERNATIVE RNA SPLICING; ARTICLE; CAENORHABDITIS ELEGANS; EXON; EXPRESSED SEQUENCE TAG; GENE SEQUENCE; GENETIC ALGORITHM; GENETIC DATABASE; INTRON; MATHEMATICAL COMPUTING; NONHUMAN; PRIORITY JOURNAL; SIMULATION; VALIDATION PROCESS;

CAENORHABDITIS ELEGANS; EUKARYOTA; NEMATODA;

ALTERNATIVE SPLICING; COMPUTATIONAL STUDIES; FALSE POSITIVE RATES; HUMAN GENES; LOCAL SEQUENCES; MACHINE LEARNING TECHNIQUES; PRE-MRNA; SPLICE SITE; SPLICING MACHINERY; SUPPORT VECTOR MACHINE (SVM); SVM-BASED CLASSIFIERS; TRUE POSITIVE RATES;

GENE EXPRESSION; MACHINERY; MOLECULAR BIOLOGY; MOLECULAR STRUCTURE; NUCLEIC ACIDS; SUPPORT VECTOR MACHINES;

INTELLIGENT SYSTEMS;

EID: 29144512262     PISSN: 13674803     EISSN: 13674811     Source Type: Journal    
DOI: 10.1093/bioinformatics/bti1053     Document Type: Article

References (26)

1. Bach,F.R., Lanckriet,G.R.G. and Jordan,M.I. (2004) Multiple kernel learning, conic duality, and the SMO algorithm. In Proceedings of the 21st International Conference on Machine Learning, (ICML'04), ACM Press.
2. Bennett,K.P., Demiriz,A. and Shawe-Taylor,J. (2000) A column generation algorithm for boosting. In Proceedings of the International Conference on Machine Learning, (ICML'00), San Francisco, Morgan Kaufmann, pp. 65-72.
3. Boguski,M.S., Lowe,T.M. and Tolstoshev,C.M. (1993) dbEST-database for ESTs. Nat. Genet., 4, 332-333.
4. Burge,C. and Karlin,S. (1997) Prediction of complete gene structures in human genomic DNA. J. Mol. Biol., 268, 78-94.
5. CPLEX Optimization Incorporated (1994) Using the CPLEX Callable Library. Incline Village, Nevada.
6. Dror,G., Sorek,R. and Shamir,R. (2004) Accurate identification of alternatively spliced exons using support vector machine. Bioinformatics, 21, 897-901.
7. Gupta,S., Zink,D., Korn,B., Vingron,M. and Haas,S. (2004) Strengths and weaknesses of EST-based prediction of tissue-specific alternative splicing. BMC Genomics, 5, 72.
8. Harris,T.W., Chen,N., Cunningham,F., Tello-Ruiz,M., Antuschechkin,I., Bastiani,C., Bjeri,T., Blasiar,D., Bradnum,K., Chan,J. et al. (2004) Wormbase: A multi-species resource for nematode biology and genomics. Nucleic Acids Res., 32, D411-D417.
9. Haussler,D. (1999) Convolutional kernels on discrete structures. Technical Report CRL-99-10, UC Santa Cruz.
10. Hiller,M., Backofen,R., Heymann,S., Busch,A., Glaesser,T.M. and Freytag,J.C. (2004) Efficient prediction of alternative splice forms using protein domain homology. In Silico Biol., 4, 195-208.
11. Jaakkola,T.S. and Haussler,D. (1999) Probabilistic kernel regression models. In Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, San francisco, CA. AISTAT'99.
12. Kent,W.J. (2002) Blat-the blast-like alignment tool. Genome Res., 12, 656-664.
13. Leslie,C., Eskin,E. and Noble,W.S. (2002) The spectrum kernel: A string kernel for SVM protein classification. In Proceedings of the 7th Pacific Symposium of Biocomputing PSB'02, Kaua'i, Hawaii, 2002.
14. Maniatis,T. and Tasic,B. (2002) Alternative pre-mRNA splicing and proteome expansion in metazoans. Nature, 418, 236-243.
15. Meinicke,P., Tech,M., Morgenstern,B. and Merkl,R. (2004) Oligo kernels for datamining on biological sequences: A case study on prokaryotic translation initiation sites. BMC Bioinformatics, 5, 169.
16. Metz,C.E. (1978) Basic principles of ROC analysis. Seminars Nucl. Med., 8(4), 283-298.
17. Rätsch,G. and Sonnenburg,S. (2003) Accurate splice site prediction for Caenorhabditis elegans. In Schölkopf,B., Tsuda,K. and Vert,J.P. (eds.), Kernel Methods in Computational Biology, MIT Press, Cambridge, MA, pp. 277-298.
18. Rozen,S. and Skaletsky,H.J. (2000) Primer3 on the WWW for general users and for biologist programmers. In Krawet,S. and Misener,S. (eds), Bioinformatics Methods and Protocols: Methods in Molecular Biology. Humana Press, Totowa, NJ, pp. 365-386.
19. Sakai,H. and Maruyama,O. (2004) Extensive search for discriminative features of alternative splicing. In Proceedings of the Pacific Symposium on Biocomputing, Hawaii, USA, pp. 54-65.
20. Schölkopf,B., Tsuda,K. and Vert,J.P. (eds) (2004). Kernel Methods in Computational Biology, MIT Press series on Computational Molecular Biology. MIT Press, Cambridge, MA.
21. Sonnenburg,S., Rätsch,G. and Schäfer,C. (2005) Learning interpretable SVMs for biological sequence classification. In Proceedings of the 9th International Conference on Research in Computational Molecular Biology (RECOMB 2005), LNBI 3500, Springer Berlin, pp. 389-407.
22. Sorek,R. and Ast,G. (2003) Intronic sequences flanking alternatively spliced exons are conserved between human and mouse. Genome Res., 13, 1631-1637.
23. Sorek,R., Shemesh,R., Cohen,Y., Basechess,O., Ast,G. and Shamir,R. (2004) A non-EST-based method for exon-skipping prediction. Genome Res., 14, 1617-1623.
24. Vapnik,V.N. (1995) The Nature of Statistical Learning Theory. Springer Verlag, New York.
25. Watkins,C. (2000) Dynamic alignment kernels. In Smola,A.J., Bartlett,P.L., Schölkopf,B. and Schuurmans,D. (eds), Advances in Large Margin Classifiers. MIT Press, Cambridge, MA, pp. 39-50.
26. Wheeler,D.L., Church,D.M., Federhen,S., Lash,A.E., Madden,T.L., Pontius,J.U., Schuler,G.D., Schriml,L.M., Sequeira,E., Talusova,T.A. and Wagner,L. (2003) Database resources of the national center for biotechnology. Nucleic Acids Res., 31, 28-33.