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Kernel Methods in Bioinformatics

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Handbook of Statistical Bioinformatics

Part of the book series: Springer Handbooks of Computational Statistics ((SHCS))

Abstract

Kernel methods have now witnessed more than a decade of increasing popularity in the bioinformatics community. In this article, we will compactly review this development, examining the areas in which kernel methods have contributed to computational biology and describing the reasons for their success.

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Notes

  1. 1.

    The machine learning community often (incorrectly) uses the term positive definite rather than positive semi-definite.

References

  1. Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990). Basic local alignment search tool. Journal of Molecular Biology, 215(3), 403–410.

    Google Scholar 

  2. Ben-Hur, A., & Brutlag, D. (2003). Remote homology detection: A motif based approach. Bioinformatics, 19 (Suppl. 1), i26–i33. URL http://www.ncbi.nlm.nih.gov/pubmed/12855434. PMID: 12855434

  3. Ben-Hur, A., & Noble, W. S. (2005). Kernel methods for predicting protein-protein interactions. Bioinformatics (Oxford, England), 21 (Suppl. 1), i38–i46. DOI10.1093/bioinformatics/bti1016. URL http://www.ncbi.nlm.nih.gov/pubmed/15961482. PMID: 15961482

  4. Ben-Hur, A., Ong, C. S., Sonnenburg, S., Schölkopf, B., & Rätsch, G. (2008). Support vector machines and kernels for computational biology. PLoS Computational Biology, 4(10), e1000,173. DOI10.1371/journal.pcbi.1000173. URL http://www.ncbi.nlm.nih.gov/pubmed/18974822. PMID: 18974822

  5. Bock, J. R., & Gough, D. A. (2001). Predicting protein–protein interactions from primary structure. Bioinformatics (Oxford, England), 17(5), 455–460. URL http://www.ncbi.nlm.nih.gov/pubmed/11331240. PMID: 11331240

    Google Scholar 

  6. Bona, F. D., Ossowski, S., Schneeberger, K., & Rätsch, G. (2008). Optimal spliced alignments of short sequence reads. Bioinformatics (Oxford, England), 24(16), i174–i180. DOI10.1093/bioinformatics/btn300. URL http://www.ncbi.nlm.nih.gov/pubmed/18689821. PMID: 18689821

  7. Borgwardt, K. M., Gretton, A., Rasch, M. J., Kriegel, H. P., Schölkopf, B., & Smola, A. J. (2006). Integrating structured biological data by kernel maximum mean discrepancy. Bioinformatics (ISMB), 22(14), e49–e57.

    Article  Google Scholar 

  8. Borgwardt, K. M., & Kriegel, H. P. (2005). Shortest-path kernels on graphs. In ICDM (pp. 74–81). IEEE Computer Society.

    Google Scholar 

  9. Borgwardt, K. M., Ong, C. S., Schönauer, S., Vishwanathan, S. V. N., Smola, A. J., & Kriegel, H. P. (2005). Protein function prediction via graph kernels. Bioinformatics, 21(Suppl 1), i47–i56.

    Article  Google Scholar 

  10. Borgwardt, K. M., Vishwanathan, S. V. N., & Kriegel, H. P. (2006). Class prediction from time series gene expression profiles using dynamical systems kernels. In R. B. Altman, T. Murray, T. E. Klein, A. K. Dunker, & L. Hunter (Eds.), Pacific symposium on biocomputing (pp. 547–558). World Scientific.

    Google Scholar 

  11. Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. In D. Haussler (Ed.), Proceedings of the annual conference on computational learning theory (pp. 144–152). Pittsburgh, PA: ACM.

    Google Scholar 

  12. Brown, M. P. S., Grundy, W. N., Lin, D., Cristianini, N., Sugnet, C., Furey, T. S., et al. (2000). Knowledge-based analysis of microarray gene expression data using support vector machines. Proceedings of the National Academy of Sciences of the United States of America, 97(1), 262–267.

    Article  Google Scholar 

  13. Cai, Y. D., Liu, X. J., Xu, X. B., & Chou, K. C. (2002). Prediction of protein structural classes by support vector machines. Computational Chemistry, 26(3), 293–296.

    Article  Google Scholar 

  14. Ding, C. H., & Dubchak, I. (2001). Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics, 17(4), 349–358.

    Article  Google Scholar 

  15. Dobson, P. D., & Doig, A. J. (2003). Distinguishing enzyme structures from non-enzymes without alignments. Journal of Molecular Biology, 330(4), 771–783.

    Article  Google Scholar 

  16. Durbin, R., Eddy, S., Krogh, A., & Mitchison, G. (1998). Biological sequence analysis: Probabilistic models of proteins and nucleic acids. Cambridge, UK: Cambridge University Press.

    Book  MATH  Google Scholar 

  17. Gärtner, T., Flach, P. A., & Wrobel, S. (2003). On graph kernels: Hardness results and efficient alternatives. In B. Schölkopf & M. K. Warmuth (Eds.), COLT, Lecture Notes in Computer Science (Vol. 2777, pp. 129–143). Springer.

    Google Scholar 

  18. Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., et al. (1999). Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science, 286(5439), 531–537.

    Article  Google Scholar 

  19. Gretton, A., Borgwardt, K., Rasch, M., Schölkopf, B., & Smola, A. (2007). A kernel method for the two-sample-problem. In Advances in neural information processing systems (Vol. 19, pp. 513–520). Cambridge, MA: MIT.

    Google Scholar 

  20. Gretton, A., Fukumizu, K., Teo, C. H., Song, L., Schölkopf, B., & Smola, A. J. (2007). A kernel statistical test of independence. In J. C. Platt, D. Koller, Y. Singer, & S. T. Roweis (Eds.), NIPS. MIT Press.

    Google Scholar 

  21. Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Machine Learning, 46, 389–422.

    Article  MATH  Google Scholar 

  22. Haussler, D. (1999). Convolutional kernels on discrete structures. Tech. Rep., UCSC-CRL-99-10. UC Santa Cruz: Computer Science Department.

    Google Scholar 

  23. Henikoff, S., Henikoff, J. G. (1991). Automated assembly of protein blocks for database searching. Nucleic Acids Research, 19, 6565–6572.

    Article  Google Scholar 

  24. Horváth, T., Gärtner, T., & Wrobel, S. (2004). Cyclic pattern kernels for predictive graph mining. In W. Kim, R. Kohavi, J. Gehrke, & W. DuMouchel (Eds.), KDD (pp. 158–167). ACM.

    Google Scholar 

  25. Hua, S., & Sun, Z. (2001). A novel method of protein secondary structure prediction with high segment overlap measure: Support vector machine approach. Journal of Molecular Biology, 308(2), 397–407. DOI10.1006/jmbi.2001.4580. URL http://www.ncbi.nlm.nih.gov/pubmed/11327775. PMID: 11327775

  26. Imrich, W., & Klavzar, S. (2000). Product graphs: Structure and recognition. In Wiley Interscience Series in Discrete Mathematics. New York: Wiley VCH.

    Google Scholar 

  27. Jaakkola, T., Diekhans, M., & Haussler, D. (1999). Using the fisher kernel method to detect remote protein homologies. In T. Lengauer, R. Schneider, P. Bork, D. L. Brutlag, J. I. Glasgow, H. W. Mewes, et al. (Eds.), ISMB (pp. 149–158). AAAI.

    Google Scholar 

  28. Kashima, H., Tsuda, K., & Inokuchi, A. (2003). Marginalized kernels between labeled graphs. In Proceedings of the 20th International Conference on Machine Learning (ICML). Washington, DC: United States.

    Google Scholar 

  29. Kato, T., Tsuda, K., & Asai, K. (2005). Selective integration of multiple biological data for supervised network inference. Bioinformatics (Oxford, England), 21(10), 2488–2495. DOI10.1093/bioinformatics/bti339. URL http://www.ncbi.nlm.nih.gov/pubmed/15728114. PMID: 15728114

  30. Kawashima, S., Ogata, H., & Kanehisa, M. (1999). Aaindex: Amino acid index database. Nucleic Acids Research, 27(1), 368–369.

    Article  Google Scholar 

  31. Kim, S., Nam, J., Rhee, J., Lee, W., & Zhang, B. (2006). miTarget: microRNA target gene prediction using a support vector machine. BMC Bioinformatics, 7, 411. DOI10.1186/1471-2105-7-411. URL http://www.ncbi.nlm.nih.gov/pubmed/16978421. PMID: 16978421

  32. Kuksa, P. P., Huang, P. H., & Pavlovic, V. (2008). Scalable algorithms for string kernels with inexact matching. In D. Koller, D. Schuurmans, Y. Bengio, & L. Bottou (Eds.), NIPS (pp. 881–888). MIT.

    Google Scholar 

  33. Lafferty, J. D., McCallum, A., & Pereira, F. C. N. (2001). Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In C. E. Brodley & A. P. Danyluk (Eds.), ICML (pp. 282–289). Morgan Kaufmann.

    Google Scholar 

  34. Lanckriet, G., Cristianini, N., Bartlett, P., Ghaoui, L. E., & Jordan, M. I. (2004). Learning the kernel matrix with semi-definite programming. Journal of Machine Learning Research, 5, 27–72.

    MATH  Google Scholar 

  35. Lanckriet, G. R. G., Bie, T. D., Cristianini, N., Jordan, M. I., & Noble, W. S. (2004). A statistical framework for genomic data fusion. Bioinformatics, 20(16), 2626–2635. DOI10.1093/bioinformatics/bth294. URL http://www.ncbi.nlm.nih.gov/pubmed/15130933. PMID: 15130933

    Google Scholar 

  36. Leslie, C., Eskin, E., & Noble, W. S. (2002). The spectrum kernel: A string kernel for SVM protein classification. In Proceedings of the pacific symposium on biocomputing (pp. 564–575).

    Google Scholar 

  37. Leslie, C., Eskin, E., Weston, J., & Noble, W. S. (2002). Mismatch string kernels for SVM protein classification. In S. Becker, S. Thrun, & K. Obermayer (Eds.), Advances in neural information processing systems (Vol. 15). Cambridge, MA: MIT.

    Google Scholar 

  38. Leslie, C. S., & Kuang, R. (2003). Fast kernels for inexact string matching. In B. Schölkopf & M. K. Warmuth (Eds.), COLT, Lecture Notes in Computer Science (Vol. 2777, pp. 114–128). Springer.

    Google Scholar 

  39. Leslie, C. S., Eskin, E., Cohen, A., Weston, J., & Noble, W. S. (2004). Mismatch string kernels for discriminative protein classification. Bioinformatics (Oxford, England), 20(4), 467–476. DOI10.1093/bioinformatics/btg431. URL http://www.ncbi.nlm.nih.gov/pubmed/14990442. PMID: 14990442

  40. Lewis, D. P., Jebara, T., & Noble, W. S. (2006). Support vector machine learning from heterogeneous data: An empirical analysis using protein sequence and structure. Bioinformatics (Oxford, England), 22(22), 2753–2760. DOI10.1093/bioinformatics/btl475. URL http://www.ncbi.nlm.nih.gov/pubmed/16966363. PMID: 16966363

  41. Liao, L., & Noble, W. S. (2002). Combining pairwise sequence similarity and support vector machines for remote protein homology detection. In RECOMB (pp. 225–232).

    Google Scholar 

  42. Liu, J., Gough, J., & Rost, B. (2006). Distinguishing Protein-Coding from Non-Coding RNAs through support vector machines. PLoS Genetics, 2(4), 529–536.

    Article  Google Scholar 

  43. Logan, B., Moreno, P., Suzek, B., Weng, Z., & Kasif, S. (2001). A study of remote homology detection. Tech. Rep., Cambridge Research Laboratory.

    Google Scholar 

  44. Matsuda, S., Vert, J., Saigo, H., Ueda, N., Toh, H., & Akutsu, T. (2005). A novel representation of protein sequences for prediction of subcellular location using support vector machines. Protein Science: A Publication of the Protein Society, 14(11), 2804–2813. DOI10.1110/ps.051597405. URL http://www.ncbi.nlm.nih.gov/pubmed/16251364. PMID: 16251364

  45. Mewes, H. W., Frishman, D., Gruber, C., Geier, B., Haase, D., Kaps, A., et al. (2000). MIPS: A database for genomes and protein sequences. Nucleic Acids Research, 28(1), 37–40. URL http://www.ncbi.nlm.nih.gov/pubmed/10592176. PMID: 10592176

  46. Mukherjee, S., Tamayo, P., Slonim, D., Verri, A., Golub, T., Mesirov, J.P., et al. (2000). Support vector machine classification of microarray data. Tech. Rep., Artificial Intelligence Laboratory, Massachusetts Institute of Technology.

    Google Scholar 

  47. Murzin, A. G., Brenner, S. E., Hubbard, T., & Chothia, C. (1995). SCOP: A structural classification of proteins database for the investigation of sequences and structures. Journal of Molecular Biology, 247(4), 536–40. DOI10.1006/jmbi.1995.0159. URL http://www.ncbi.nlm.nih.gov/pubmed/7723011. PMID: 7723011

  48. Noble, W. (2004). Support vector machine applications in computational biology. In B. Schölkopf, K. Tsuda, & J. P. Vert (Eds.), Kernel methods in computational biology. Cambridge, MA: MIT.

    Google Scholar 

  49. Noble, W. S. (2006). What is a support vector machine? Nature Biotechnology, 24(12), 1565–1567. DOI10.1038/nbt1206-1565. URL http://dx.doi.org/10.1038/nbt1206-1565

    Google Scholar 

  50. Ong, C. S., & Smola, A. J. (2003). Machine learning with hyperkernels. In T. Fawcett & N. Mishra (Eds.), ICML (pp. 568–575). AAAI.

    Google Scholar 

  51. Ortiz, A. R., Strauss, C. E. M., & Olmea, O. (2002). MAMMOTH (matching molecular models obtained from theory): An automated method for model comparison. Protein Science: A Publication of the Protein Society, 11(11), 2606–2621. DOI10.1110/ps.0215902. URL http://www.ncbi.nlm.nih.gov/pubmed/12381844. PMID: 12381844

  52. Qiu, J., Hue, M., Ben-Hur, A., Vert, J., & Noble, W. S. (2007). A structural alignment kernel for protein structures. Bioinformatics (Oxford, England), 23(9), 1090–1098. DOI10.1093/bioinformatics/btl642. URL http://www.ncbi.nlm.nih.gov/pubmed/17234638. PMID: 17234638

    Google Scholar 

  53. Qiu, J., & Noble, W. S. (2008). Predicting co-complexed protein pairs from heterogeneous data. PLoS Computational Biology, 4(4), e1000,054. DOI10.1371/journal.pcbi.1000054. URL http://www.ncbi.nlm.nih.gov/pubmed/18421371. PMID: 18421371

  54. Rakotomamonjy, A., Bach, F., Canu, S., & Grandvalet, Y. (2007). More efficiency in multiple kernel learning. In Z. Ghahramani (Ed.), ICML, ACM International Conference Proceeding Series (Vol. 227, pp. 775–782). ACM.

    Google Scholar 

  55. Ramon, J., & Gärtner, T. (2003). Expressivity versus efficiency of graph kernels. Tech. Rep., First International Workshop on Mining Graphs, Trees and Sequences (held with ECML/PKDD’03).

    Google Scholar 

  56. Rätsch, G., Sönnenburg, S., & Schölkopf, B. (2005). RASE: Recognition of alternatively spliced exons in c. elegans. Bioinformatics, 21 (Suppl. 1), i369–i377.

    Google Scholar 

  57. Rätsch, G., Sonnenburg, S., Srinivasan, J., Witte, H., Müller, K., Sommer, R., et al. (2007). Improving the Caenorhabditis elegans genome annotation using machine learning. PLoS Computational Biology, 3(2), e20. PMID: 17319737

    Article  Google Scholar 

  58. Sakakibara, Y., Popendorf, K., Ogawa, N., Asai, K., & Sato, K. (2007). Stem kernels for RNA sequence analyses. Journal of Bioinformatics and Computational Biology, 5(5), 1103–1122. URL http://www.ncbi.nlm.nih.gov/pubmed/17933013. PMID: 17933013

    Google Scholar 

  59. Sato, K., Mituyama, T., Asai, K., & Sakakibara, Y. (2008). Directed acyclic graph kernels for structural RNA analysis. BMC Bioinformatics, 9, 318. DOI10.1186/1471-2105-9-318. URL http://www.ncbi.nlm.nih.gov/pubmed/18647390. PMID: 18647390

  60. Schölkopf, B. (1997). Support vector learning. München: R. Oldenbourg Verlag. PhD thesis, TU Berlin. Download: http://www.kernel-machines.org

  61. Schölkopf, B., & Smola, A. J. (2002). Learning with Kernels. Cambridge, MA: MIT.

    Google Scholar 

  62. Schölkopf, B., Smola, A. J., & Müller, K. R. (1997). Kernel principal component analysis. In W. Gerstner, A. Germond, M. Hasler, & J. D. Nicoud (Eds.), Artificial neural networks ICANN’97 (Vol. 1327, pp. 583–588). Berlin: Springer Lecture Notes in Computer Science.

    Chapter  Google Scholar 

  63. Schölkopf, B., Tsuda, K., & Vert, J. P. (2004). Kernel Methods in Computational Biology. Cambridge, MA: MIT.

    Google Scholar 

  64. Schultheiss, S. J., Busch, W., Lohmann, J. U., Kohlbacher, O., & Rätsch, G. (2009). KIRMES: kernel-based identification of regulatory modules in euchromatic sequences. Bioinformatics (Oxford, England), DOI10.1093/bioinformatics/btp278. URL http://www.ncbi.nlm.nih.gov/pubmed/19389732. PMID: 19389732

  65. Schulze, U., Hepp, B., Ong, C. S., & Rätsch, G. (2007). PALMA: mRNA to genome alignments using large margin algorithms. Bioinformatics (Oxford, England), 23(15), 1892–1900.DOI10.1093/bioinformatics/btm275. URL http://www.ncbi.nlm.nih.gov/pubmed/17537755. PMID: 17537755

  66. Schweikert, G., Zien, A., Zeller, G., Behr, J., Dieterich, C., Ong, C. S., et al. (2009). mGene: Accurate SVM-based gene finding with an application to nematode genomes. Genome Research, 19(11), 2133–2143. DOI10.1101/gr.090597.108. URL http://www.ncbi.nlm.nih.gov/pubmed/19564452. PMID: 19564452

  67. Shawe-Taylor, J., & Cristianini, N. (2004). Kernel methods for pattern analysis. Cambridge, UK: Cambridge University Press.

    Book  Google Scholar 

  68. Shervashidze, N., & Borgwardt, K. M. (2009). Fast subtree kernels on graphs. In Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, & A. Culotta (Eds.), NIPS (pp. 1660–1668). Cambridge, MA: MIT.

    Google Scholar 

  69. Shervashidze, N., Vishwanathan, S., Petri, T., Mehlhorn, K., & Borgwardt, K. M. (2009). Efficient graphlet kernels for large graph comparison. In D. van Dyk & M. Welling (Eds.), Proceedings of the twelfth international conference on artificial intelligence and statistics. Clearwater Beach, Florida.

    Google Scholar 

  70. Smith, T. F., & Waterman, M. S. (1981). Identification of common molecular subsequences. Journal of Molecular Biology, 147(1), 195–197. URL http://www.ncbi.nlm.nih.gov/pubmed/7265238. PMID: 7265238

    Google Scholar 

  71. Song, L., Bedo, J., Borgwardt, K., Gretton, A., & Smola, A. (2007). Gene selection via the BAHSIC family of algorithms. Bioinformatics, 23(13), i490–i498.

    Article  Google Scholar 

  72. Song, L., Smola, A., Gretton, A., Borgwardt, K., & Bedo, J. (2007). Supervised feature selection via dependence estimation. In: Ghahramani, Z. (ed.): ACM International Conference Proceeding Series, vol. 227. ACM.

    Google Scholar 

  73. Sonnenburg, S., Rätsch, G., Jagota, A. K., & Müller, K. R. (2002). New methods for splice site recognition. In Proceedings of the International Conference on Artificial Neural Networks (ICANN) (pp. 329–336).

    Google Scholar 

  74. Sonnenburg, S., Rätsch, G., & Rieck, K. (2007). Large-scale learning with string kernels. In L. Bottou, O. Chapelle, D. DeCoste, & J. Weston (Eds.), Large-Scale kernel machines (pp. 73—104). Cambridge, MA: MIT.

    Google Scholar 

  75. Sonnenburg, S., Rätsch, G., & Schäfer, C. (2005). A general and efficient multiple kernel learning algorithm. In NIPS.

    Google Scholar 

  76. Sonnenburg, S., Rätsch, G., & Schäfer, C. (2005). Learning interpretable SVMs for biological sequence classification. In RECOMB 2005, LNBI 3500 (pp. 389–407). Berlin, Heidelberg: Springer-Verlag.

    Google Scholar 

  77. Sonnenburg, S., Zien, A., Philips, P., & Rätsch, G. (2008). POIMs: positional oligomer importance matrices — understanding support vector machine based signal detectors. Bioinformatics, 24(13), i6–i14. URL http://bioinformatics.oxfordjournals.org/cgi/content/full/24/13/i6

  78. Sonnenburg, S., Zien, A., & Rätsch, G. (2006). ARTS: Accurate recognition of tran- scription starts in human. Bioinformatics (Oxford, England)22(14), e472–480. DOI10.1093/ DOIbioinformatics/btl250. URL http://www.ncbi.nlm.nih.gov/pubmed/16873509. PMID: 16873509

  79. Steinwart, I. (2002). Support vector machines are universally consistent. Journal of Complexity, 18, 768–791.

    Article  MathSciNet  MATH  Google Scholar 

  80. Su, Q. J., Lu, L., Saxonov, S., & Brutlag, D. L. (2005). eBLOCKs: Enumerating conserved protein blocks to achieve maximal sensitivity and specificity. Nucleic Acids Research, 33(Database issue), D178–D182. DOI10.1093/nar/gki060. URL http://www.ncbi.nlm.nih.gov/pubmed/15608172. PMID: 15608172

  81. Tsochantaridis, I., Joachims, T., Hofmann, T., & Altun, Y. (2005). Large margin methods for structured and interdependent output variables. Journal of Machine Learning Research, 6, 1453–1484.

    MathSciNet  MATH  Google Scholar 

  82. Tsuda, K., Kin, T., & Asai, K. (2002). Marginalized kernels for biological sequences. Bioinformatics (Oxford, England), 18 (Suppl. 1), S268–S275. URL http://www.ncbi.nlm.nih.gov/pubmed/12169556. PMID: 12169556

  83. Tsuda, K., Noble, W. S. (2004). Learning kernels from biological networks by maximizing entropy. Bioinformatics (Oxford, England), 20 (Suppl. 1), i326–i333. DOI10.1093/bioinformatics/bth906. URL http://www.ncbi.nlm.nih.gov/pubmed/15262816. PMID: 15262816

  84. Tsuda, K., Shin, H., & Schölkopf, B. (2005). Fast protein classification with multiple networks. Bioinformatics, 21 (Suppl. 2), ii59–ii65.

    Google Scholar 

  85. Vapnik, V. (1998). Statistical learning theory. New York: Wiley.

    MATH  Google Scholar 

  86. Vert, J. (2002). A tree kernel to analyse phylogenetic profiles. Bioinformatics, 18, S276–S284.

    Article  Google Scholar 

  87. Vert, J., Qiu, J., & Noble, W. S. (2007). A new pairwise kernel for biological network inference with support vector machines. BMC Bioinformatics, 8 (Suppl. 10), S8. DOI10.1186/1471-2105-8-S10-S8. URL http://www.ncbi.nlm.nih.gov/pubmed/18269702. PMID: 18269702

  88. Vert, J. P., Saigo, H., & Akutsu, T. (2004). Local alignment kernels for biological sequences. In B. Schölkopf, K. Tsuda, & J. P. Vert (Eds.), Kernel methods in computational biology (pp. 261–274). Cambridge, MA: MIT.

    Google Scholar 

  89. Vishwanathan, S., & Smola, A. (2003). Fast kernels for string and tree matching. In K. Tsuda, B. Schölkopf, & J. Vert (Eds.), Kernels and bioinformatics. Cambridge, MA: MIT. Forthcoming

    Google Scholar 

  90. Vishwanathan, S. V., Smola, A. J., & Vidal, R. (2007). Binet-Cauchy kernels on dynamical systems and its application to the analysis of dynamic scenes. International Journal of Computer Vision, 73(1), 95–119. URL http://portal.acm.org/citation.cfm?id=1227529

    Google Scholar 

  91. Vishwanathan, S. V. N., Borgwardt, K., & Schraudolph, N. N. (2007). Fast computation of graph kernels. In B. Schölkopf, J. Platt, & T. Hofmann (Eds.), Advances in neural information processing systems (Vol. 19). Cambridge MA: MIT.

    Google Scholar 

  92. Wang, X., & Naqa, I. M. E. (2008). Prediction of both conserved and nonconserved microRNA targets in animals. Bioinformatics (Oxford, England), 24(3), 325–332. DOI10.1093/bioinformatics/btm595. URL http://www.ncbi.nlm.nih.gov/pubmed/18048393. PMID: 18048393

  93. Weinberger, K. Q., Sha, F., & Saul, L. K. (2004). Learning a kernel matrix for nonlinear dimensionality reduction. In Proceedings of the 21st international conference on machine learning. Banff, Canada.

    Google Scholar 

  94. Weston, J., Mukherjee, S., Chapelle, O., Pontil, M., Poggio, T., & Vapnik, V. (2000). Feature selection for svms. In T. K. Leen, T. G. Dietterich, V. Tresp (Eds.), NIPS (pp. 668–674). MIT.

    Google Scholar 

  95. Yamanishi, Y., Vert, J., & Kanehisa, M. (2004). Protein network inference from multiple genomic data: A supervised approach. Bioinformatics (Oxford, England), 20 (Suppl. 1), i363–i370. DOI10.1093/bioinformatics/bth910. URL http://www.ncbi.nlm.nih.gov/pubmed/15262821. PMID: 15262821

  96. Yamanishi, Y., Vert, J., & Kanehisa, M. (2005). Supervised enzyme network inference from the integration of genomic data and chemical information. Bioinformatics (Oxford, England), 21 (Suppl 1), i468–i477. DOI10.1093/bioinformatics/bti1012. URL http://www.ncbi.nlm.nih.gov/pubmed/15961492. PMID: 15961492

  97. Zeller, G., Clark, R. M., Schneeberger, K., Bohlen, A., Weigel, D., & Rätsch, G. (2008). Detecting polymorphic regions in Arabidopsis thaliana with resequencing microarrays. Genome Research, 18(6), 918–929.

    Article  Google Scholar 

  98. Zeller, G., Henz, S. R., Laubinger, S., Weigel, D., & Rätsch, G. (2008). Transcript normalization and segmentation of tiling array data. In R. B. Altman, A. K. Dunker, L. Hunter, T. Murray, & T.E. Klein (Eds.), Pacific symposium on biocomputing (pp. 527–538). World Scientific.

    Google Scholar 

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Correspondence to Karsten M. Borgwardt .

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© 2011 Springer-Verlag Berlin Heidelberg

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Borgwardt, K.M. (2011). Kernel Methods in Bioinformatics. In: Lu, HS., Schölkopf, B., Zhao, H. (eds) Handbook of Statistical Bioinformatics. Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16345-6_15

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