Learning interpretable SVMs for biological sequence classification

Lecture Notes in Bioinformatics (Subseries of Lecture Notes in Computer Science)

Volumn 3500, Issue , 2005, Pages 389-407

  Sonnenburg, S ^a   Ratsch G ^b   Schafer C ^a  

^a FRAUNHOFER FIRST   (Germany)

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

Author keywords

Interpretation of SVM results; Multiple Kernel Learning; Splice Site Prediction; String Kernel; Support Vector Machine; Weighted Degree Kernel

Indexed keywords

ALGORITHMS; DECISION MAKING; FUNCTIONS; OPTIMIZATION; PROBLEM SOLVING; STATISTICAL METHODS; KNOWLEDGE ACQUISITION; LINEAR PROGRAMMING; OLIGOMERS; OPERATIONS RESEARCH;

INTERPRETATION OF SVM RESULTS; MULTIPLE KERNEL LEARNING; SPLICE SITE PREDICTION; STRING KERNEL; SUPPORT VECTOR MACHINE; WEIGHTED DEGREE KERNEL;

MOLECULAR BIOLOGY; LEARNING ALGORITHMS;

INTERPRETATION OF SVM RESULTS; MULTIPLE KERNEL LEARNING; SPLICE SITE PREDICTION; STRING KERNEL; SUPPORT VECTOR MACHINE; WEIGHTED DEGREE KERNEL;

EID: 26444467746     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/11415770_30     Document Type: Conference Paper

References (28)

1. Francis R. Bach, Gert R. G. Lanckriet, and Michael I. Jordan. Multiple kernel learning, conic duality, and the SMO algorithm. In Twenty-first international conference on Machine learning. ACM Press, 2004.
2. K.P. Bennett, A. Demiriz, and J. Shawe-Taylor. A column generation algorithm for boosting. In P. Langley, editor, Proceedings, 17th ICML, pages 65-72, San Francisco, 2000. Morgan Kaufmann.
3. M.S. Boguski and T.M. Lowe C.M. Tolstoshev. dbEST-database for "expressed sequence tags". Nat Genet., 4(4):332-3, 1993.
4. L. Breiman. Prediction games and arcing algorithms. Technical Report 504, Statistics Department, University of California, December 1997.
5. C. Cortes and V.N. Vapnik. Support vector networks. Machine Learning, 20:273-297, 1995.
6. A.L. Delcher, D. Harmon, S. Kasif, O. White, and S.L. Salzberg. Improved microbial gene identification with GLIMMER. Nucleic Acids Research, 27(23):4636-4641, 1999.
7. Y. Engel, S. Mannor, and R. Meir. Sparse online greedy support vector regression. In ECML, pages 84-96, 2002.
8. Y. Freund and R.E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. In EuroCOLT: European Conference on Computational Learning Theory. LNCS, 1994.
9. Harris, T.W. et al. Wormbase: a multi-species resource for nematode biology and genomics. Nucl. Acids Res., 32, 2004. Database issue:D411-7.
10. R. Hettich and K.O. Kortanek. Semi-infinite programming: Theory, methods and applications. SIAM Review, 3:380-429, September 1993.
11. T. Jaakkola, M. Diekhans, and D. Haussler. A discriminative framework for detecting remote protein homologies. J Comput Biol., 7(1-2):95-114, February 2000.
12. T. Joachims. Making large-scale SVM learning practical. In B. Schölkopf, C.J.C. Burges, and A.J. Smola, editors, Advances in Kernel Methods - Support Vector Learning, pages 169-184, Cambridge, MA, 1999. MIT Press.
13. W.J. Kent. Blat-the blast-like alignment tool. Genome Res., 12(4):656-64, 2002.
14. R. Kuang, E. Ie, K. Wang, K. Wang, M. Siddiqi, Y. Freund, and C. Leslie. Profile-based string kernels for remote homology detection and motif extraction. In Computational Systems Bioinformatics Conference 2004, pages 146-154, 2004.
15. G.R.G. Lanckriet, T. De Bie, N. Cristianini, M.I. Jordan, and W.S. Noble. A statistical framework for genomic data fusion. Bioinformatics, 2004.
16. E.L. Lehmann. Testing Statistical Hypotheses. Springer, New York, second edition edition, 1997.
17. C. Leslie, E. Eskin, and W.S. Noble. The spectrum kernel: A string kernel for SVM protein classification. In Proceedings of the Pacific Symposium on Biocomputing, Kaua'i, Hawaii, 2002.
18. A.M. Mood, F.A. Graybill, and D.C. Boes. Introduction to the Theory of Statistics. McGraw-Hill, third edition edition, 1974.
19. K.-R. Müller, S. Mika, G. Rätsch, K. Tsuda, and B. Schölkopf. An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks, 12(2):181-201, 2001.
20. J. Platt. Fast training of support vector machines using sequential minimal optimization. In B. Schölkopf, C.J.C. Burges, and A.J. Smola, editors, Advances in Kernel Methods - Support Vector Learning, pages 185-208, Cambridge, MA, 1999. MIT Press.
21. G. Rätsch. Robust Boosting via Convex Optimization. PhD thesis, University of Potsdam, Computer Science Dept., August-Bebel-Str. 89, 14482 Potsdam, Germany, 2001.
22. G. Rätsch, A. Demiriz, and K. Bennett. Sparse regression ensembles in infinite and finite hypothesis spaces. Machine Learning, 48(1-3):193-221, 2002. Special Issue on New Methods for Model Selection and Model Combination.
23. Also NeuroCOLT2 Technical Report NC-TR-2000-085.
24. G. Rätsch and S. Sonnenburg. Accurate Splice Site Prediction for Caenorhabditis Elegans, pages 277-298. MIT Press series on Computational Molecular Biology. MIT Press, 2003.
25. G. Rätsch and M.K. Warmuth. Marginal boosting. NeuroCOLT2 Technical Report 97, Royal Holloway College, London, July 2001.
26. Wheeler, D.L. et al. Database resources of the national center for biotechnology. Nucl. Acids Res, 31:38-33, 2003.
27. X.H. Zhang, K.A. Heller, I. Hefter, C.S. Leslie, and L.A. Chasin. Sequence information for the splicing of human pre-mrna identified by support vector machine classification. Genome Res, 13(12):637-50, 2003.
28. A. Zien, G. Rätsch, S. Mika, B. Schölkopf, T. Lengauer, and K.-R. Müller. Engineering Support Vector Machine Kernels That Recognize Translation Initiation Sites. BioInformatics, 16(9):799-807, September 2000.