Support vector machines and kernels for computational biology

PLoS Computational Biology

Volumn 4, Issue 10, 2008, Pages

  Ben Hur, Asa ^a   Ong, Cheng Soon ^b,c,e   Sonnenburg, Sören ^d   Schölkopf, Bernhard ^c   Rätsch, Gunnar ^b  

^a Colorado State University   (United States)

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

^c MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

^d FRAUNHOFER FIRST   (Germany)

^e ETH ZURICH   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGY;

COMPUTATIONAL BIOLOGY; SUPPORT VECTORS MACHINE;

SUPPORT VECTOR MACHINES;

AMINO ACID SEQUENCE; ARTICLE; DNA SEQUENCE; EXON; INTRON; KERNEL METHOD; PROTEIN FUNCTION; PROTEIN SYNTHESIS; RECEIVER OPERATING CHARACTERISTIC; RNA TRANSLATION; SYSTEMS BIOLOGY; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOLOGY; COMPUTER PROGRAM; DECISION SUPPORT SYSTEM; INFORMATION RETRIEVAL; METHODOLOGY; REVIEW; STATISTICS;

ARTIFICIAL INTELLIGENCE; COMPUTATIONAL BIOLOGY; DECISION SUPPORT TECHNIQUES; INFORMATION STORAGE AND RETRIEVAL; PATTERN RECOGNITION, AUTOMATED; SOFTWARE; STATISTICS AS TOPIC;

EID: 55449125185     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1000173     Document Type: Article

References (73)

1. Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In: Haussler D, ed. 5th Annual ACM Workshop on COLT. Pittsburgh (Pennsylvania): ACM Press. pp 144-152. Available: http://www.clopinet.com/isabelle/Papers. Accessed 11 August 2008.
2. Schölkopf B, Smola A (2002) Learning with kernels. Cambridge (Massachusetts): MIT Press.
3. Vapnik V (1999) The nature of statistical learning theory. 2nd edition. Springer.
4. Müller KR, Mika S, Rätsch G, Tsuda K, Schölkopf B (2001) An introduction to kernel-based learning algorithms. IEEE Trans Neural Netw 12: 181-201.
5. Schölkopf B, Tsuda K, Vert JP (2004) Kernel methods in computational biology. Cambridge (Massachusetts): MIT Press.
6. Vert JP (2007) Kernel methods in genomics and computational biology. In: Camps-Valls G, Rojo-Alvarez JL, Martinez-Ramon M, eds. Kernel methods in bioengineering, signal and image processing Idea Group. pp 42-63.
7. Shawe-Taylor J, Cristianini N (2004) Kernel methods for pattern analysis. Cambridge (United Kingdom): Cambridge University Press.
8. Black DL (2003) Mechanisms of alternative premessenger RNA splicing. Annu Rev Biochem 72: 291-336. doi:10.1146/annurev.biochem.72.121801.161720. Available: http://arjournals.annualreviews.org/doi/abs/10.1146/annurev.biochem. 72.121801.161720?cookieSet=1&journalCode=biochem. Accessed 11 August 2008.
9. Burge C, Tuschl T, Sharp P (1999) Splicing of precursors to mRNAs by the spliceosomes. In: Gesteland R, Cech T, Atkins JF, eds. The RNA world. 2nd edition. Cold Spring Harbor Laboratory Press. pp 525-560.
10. Nilsen T (2003) The spliceosome: The most complex macromolecular machine in the cell? Bioessays 25.
11. Lewin B (2007) Genes IX. Jones & Bartlett Publishers.
12. Holste D, Ohler U (2008) Strategies for identifying RNA splicing regulatory motifs and predicting alternative splicing events. PLoS Computational Biology 4: e21. doi/10.1371/journal.pcbi.0040021.
13. Sonnenburg S, Schweikert G, Philips P, Behr J, Rätsch G (2007) Accurate splice site prediction using support vector machines. BMC Bioinformatics 8: S7.
14. Metz CE (1978) Basic principles of ROC analysis. Seminars in Nuclear Medicine VIII.
15. Provost FJ, Fawcett T, Kohavi R (1998) The case against accuracy estimation for comparing induction algorithms. In: Shavlik J, ed. ICML '98: Proceedings of the Fifteenth International Conference on Machine Learning. San Francisco: Morgan Kaufmann Publishers Inc. pp 445-453.
16. Davis J, Goadrich M (2006) The relationship between precision-recall and ROC curves. ICML. New York: ACM Press. pp 233-240.
17. Easysvm toolbox. Available: http://www.easysvm.org. Accessed 11 August 2008.
18. Python Software Foundation (2007) Python. Available: http://python.org. Accessed 11 August 2008.
19. Bassi S (2007) A primer on python for life science researchers. PLoS Comput Biol 3: e199. doi:10.1371/journal.pcbi.0030199.
20. GNU general public license. Available: http://www.gnu.org/copyleft/gpl. html. Accessed 11 August 2008.
21. Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge University Press.
22. Cortes C, Vapnik V (1995) Support vector networks. Mach Learn 20: 273-297.
23. Hastie T, Tibshirani R, Friedman J (2001) The elements of statistical learning. Springer.
24. Bishop C (2007) Pattern recognition and machine learning. Springer.
25. Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46: 489-422.
26. Cai C, Han L, Ji Z, Chen X, Chen Y (2003) SVM-Prot: Web-based support vector machine software for functional classification of a protein from its primary sequence. Nucleic Acids Res 31: 3692-3697. doi:10.1093/nar/gkg600. Available: http://nar.oxfordjournals.org/cgi/content/abstract/31/13/3692. Accessed 11 August 2008.
27. Leslie C, Eskin E, Noble WS (2002) The spectrum kernel: A string kernel for SVM protein classification. In: Proceedings of the Pacific Symposium on Biocomputing. pp 564-575.
28. Leslie C, Eskin E, Weston J, Noble W (2003) Mismatch string kernels for discriminative protein classification. Bioinformatics 20.
29. Sonnenburg S, Rätsch G, Rieck K (2007) Large scale learning with string kernels. In: Bottou L, Chapelle O, DeCoste D, Weston J, eds. Large scale kernel machines MIT Press. pp 73-104.
30. Rätsch G, Sonnenburg S (2004) Accurate splice site detection for Caenorhabditis elegans. In: B Schölkopf KT, Vert JP, eds. Kernel methods in computational biology. Cambridge (Massachusetts): MIT Press. pp 277-298. Available: http://www.fml.tuebingen.mpg.de/raetsch/projects/ MITBookSplice/files/RaeSon04.pdf. Accessed 11 August 2008.
31. Rätsch G, Sonnenburg S, Schölkopf B (2005) RASE: Recognition of alternatively spliced exons in C. elegans. Bioinformatics 21: i369-i377.
32. Zien A, Rätsch G, Mika S, Schölkopf B, Lengauer T, et al. (2000) Engineering support vector machine kernels that recognize translation initiation sites. Bioinformatics 16: 799-807.
33. Meinicke P, Tech M, Morgenstern B, Merkl R (2004) Oligo kernels for datamining on biological sequences: A case study on prokaryotic translation initiation sites. BMC Bioinformatics 5: 169.
34. Logan B, Moreno P, Suzek B, Weng Z, Kasif S (2001) A study of remote homology detection. Technical report CRL 2001/05. Compaq Cambridge Research Laboratory.
35. Ben-Hur A, Brutlag D (2003) Remote homology detection: A motif based approach. Bioinformatics 19: i26-i33.
36. Liao L, Noble W (2003) Combining pairwise similarity and support vector machines for detecting remote protein evolutionary and structural relationships. J Comput Biol 10: 2429-2437.
37. Vert JP, Saigo H, Akutsu T (2004) Local alignment kernels for biological sequences. In: B Schölkopf KT, Vert JP, eds. Kernel methods in computational biology. Cambridge (Massachusetts): MIT Press. pp 131-154.
38. Jaakkola T, Diekhans M, Haussler D (2000) A discriminative framework for detecting remote protein homologies. J Comp Biol 7: 95-114.
39. Tsuda K, Kawanabe M, Rätsch G, Sonnenburg S, Müller K (2002) A new discriminative kernel from probabilistic models. Neural Computation 14: 2397-2414. Available: http://neco.mitpress.org/cgi/content/abstract/14/10/2397. Accessed 11 August 2008.
40. Seeger M (2002) Covariance kernels from Bayesian generative models. Adv Neural Information Proc Sys 14: 905-912.
41. Tsuda K, Kin T, Asai K (2002) Marginalized kernels for biological sequences. Bioinformatics 18: 268S-275S.
42. Chang CC, Lin CJ (2001) LIBSVM: A library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm. Accessed 11 August 2008.
43. Provost F (2000) Learning with imbalanced data sets 101. In: AAAI 2000 workshop on imbalanced data sets. Available: http://pages.stern.nyu.edu/ ~fprovost/Papers/skew.PDF. Accessed 11 August 2008.
44. Pavlidis P, Weston J, Cai J, Noble W (2002) Learning gene functional classifications from multiple data types. J Comput Biol 9: 401-411.
45. Lanckriet G, Bie TD, Cristianini N, Jordan M, Noble W (2004) A statistical framework for genomic data fusion. Bioinformatics 20: 2626-2635.
46. Ben-Hur A, Noble WS (2005) Kernel methods for predicting protein-protein interactions. Bioinformatics 21(Supplement 1): i38-i46.
47. Tarca A, Carey V, Chen XW, Romero R, Draghici S (2007) Machine learning and its applications to biology. PLoS Comput Biol 3: e116. doi/10.1371/journal. pcbi.0030116.
48. Duda R, Hart P, Stork D (2001) Pattern classification. New York: Wiley-Interscience.
49. Vert JP (2002) A tree kernel to analyze phylogenetic profiles. Bioinformatics 18: S276-S284.
50. Borgwardt K, Ong C, Schnauer S, Vishwanathan S, Smola A, et al. (2005) Protein function prediction via graph kernels. Bioinformatics 21: i47-i56.
51. Borgwardt KM (2007) Graph dernels [Ph.D. thesis]. Munich: Ludwig-Maximilians-University Munich.
52. Kashima H, Tsuda K, Inokuchi A (2004) Kernels for graphs. In: Schölkopf B, Tsuda K, Vert JP, eds. Kernel methods in computational biology. Cambridge (Massachusetts): MIT Press. pp 155-170.
53. Kondor R, Vert JP (2004) Diffusion kernels. In: Schölkopf B, Tsuda K, Vert JP, eds. Kernel methods in computational biology. Cambridge (Massachusetts): MIT Press. pp 171-192.
54. Haussler D (1999) Convolutional kernels on discrete structures. Technical Report UCSC-CRL-99-10. Santa Cruz (California): UC Santa Cruz Computer Science Department.
55. Cortes C, Haffner P, Mohri M (2004) Rational kernels: Theory and algorithms. J Mach Learn Res 5: 1035-1062.
56. Gä rtner T (2008) Kernels for structured data. World Scientific Publishing.
57. Bottou L, Chapelle O, DeCoste D, Weston J, eds. (2007) Large scale kernel machines. Cambridge (Massachusetts): MIT Press. Available: http://leon.bottou. org/papers/lskm-2007. Accessed 11 August 2008.
58. Joachims T (1998) Making large-scale support vector machine learning practical. In: Schölkopf B, Burges C, Smola A, eds. Advances in kernel methods: Support vector machines. Cambridge (Massachusetts): MIT Press, Chapter 11.
59. Joachims T (2006) Training linear SVMs in linear time. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). pp 217-226.
60. Sindhwani V, Keerthi SS (2006) Large scale semisupervised linear SVMs. In: Proceedings of the 29th Annual International ACM SI-GIR Conference on Research and Development in Information Retrieval. New York: ACM Press, pp 477-484. doi:http://doi.acm.org/10.1145/1148170.1148253. Available: http://portal.acm.org/citation.cfm?id=1148170.1148253. Accessed 16 August 2008.
61. Bordes A, Ertekin S, Weston J, Bottou L (2005) Fast kernel classifiers with online and active learning. J Mach Learn Res 6: 1579-1619.
62. Demsar J, Zupan B, Leban G (2004) Orange: From experimental machine learning to interactive data mining. Faculty of Computer and Information Science, University of Ljubljana. http://www.ailab.si/orange.
63. The Spider toolbox. Available: http://www.kyb.tuebingen.mpg.de/bs/ people/spider. Accessed 11 August 2008.
64. Gawande K,Webers C, Smola A, Vishwanathan S, Gunter S, et al. (2007) ELEFANT user manual (revision 0.1). Technical report. NICTA. Available: http://elefant.developer.nicta.com.au. Accessed 11 August 2008.
65. Plearn toolbox. Available: http://plearn.berlios.de/. Accessed 11 August 2008.
66. Witten IH, Frank E (2005) Data mining: Practical machine learning tools and techniques. 2nd edition. Morgan Kaufmann.
67. Bottou L, Cun YL (2002) Lush reference manual. Available: http://lush.sourceforge.net. Accessed 11 August 2008.
68. Sonnenburg S, Rätsch G, Schäfer C, Schölkopf B (2006) Large scale multiple kernel learning. J Mach Learn Res 7: 1531-1565. Available: http://jmlr.csail.mit.edu/papers/v7/sonnenburg06a.html. Accessed 11 August 2008.
69. Mierswa I, Wurst M, Klinkenberg R, Scholz M, Euler T (2006) YALE: Rapid prototyping for complex data mining tasks. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Available: http://portal.acm.org/citation.cfm?id=1276958.1277323. Accessed 11 August 2008.
70. PyML toolbox. Available: http://pyml.sourceforge.net. Accessed 11 August 2008.
71. Sonnenburg S, Braun M, Ong C, Bengio S Bottou L, et al. (2007) The need for open source software in machine learning. J Mach Learn Res 8: 2443-2466. Available: http://jmlr.csail.mit.edu/papers/v8/sonnenburg07a.html. Accessed 11 August 2008.
72. Schneider T, Stephens R (1990) Sequence logos: A new way to display consensus sequences. Nucleic Acids Res 18.
73. Crooks G, Hon G, Chandonia J, Brenner S (2004) Weblogo: A sequence logo generator. Genome Res 14: 1188-1190.